Monthly Archives: April, 2016

INTRODUCTION TO SYSTEM OF SYSTEMS


INTRODUCTION TO SYSTEM OF SYSTEMS

1.1 INTRODUCTION
Recently, there has been a growing interest in a class of complex systems whose constituents are
themselves complex. Performance optimization, robustness and reliability among an emerging group
of heterogeneous systems in order to realize a common goal has become the focus of various
applications including military, security, aerospace, space, manufacturing, service industry,
environmental systems, disaster management, to name a few [Crossley 2006; Lopez 2006; Wojcik and
Hoffman 2006]. There is an increasing interest in achieving synergy between these independent
systems to achieve the desired overall system performance [Azarnoosh, et al. 2006]. In the literature,
researchers have addressed the issue of coordination and interoperability in a SoS [Abel and Sukkarieh
2006; DiMario 2006]. SoS technology is believed to more effectively implement and analyze large,
complex, independent, and heterogeneous systems working (or made to work) cooperatively [Abel
and Sukkarieh 2006]. The main thrust behind the desire to view the systems as an SoS is to obtain
higher capabilities and performance than would be possible with a traditional system view. The SoS
concept presents a high-level viewpoint and explains the interactions between each of the independent
systems. However, the SoS concept is still at its developing stages [Meilich 23006; Abbott 2006].
Next section will present some definitions out of many possible definitions of SoS. However, a
practical definition may be that a System of Systems is a “super system” comprised of other elements
which themselves are independent complex operational systems and interact among themselves to
achieve a common goal. Each element of a SoS achieves well-substantiated goals even if they are
detached from the rest of the SoS. For example a Boeing 747 airplane, as an element of a SoS, is not
SoS, but an airport is a SoS, or a rover on Mars is not a SoS, but a robotic colony (or a robotic swarm)
exploring the red planet, or any other place, is a SoS. As will be illustrated shortly, associated with
SoS, there are numerous problems and open-ended issues which need a great deal of fundamental
advances in theory and verifications. It is hoped that this volume will be a first effort towards bridging
the gaps between an idea and a practice.
1.2 SYSTEM OF SYSTEMS DEFINITIONS
Based on the literature survey on system of systems, there are numerous definitions whose detailed
discussion is beyond the space allotted to this chapter [Jamshidi 2005; Sage and Cuppen 2001; Kotov
1997; Carlock and Fenton 2001; Pei 2000; Luskasik 1998]. Here we enumerate only 6 of many
potential definitions:
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Definition 1: Enterprise Systems of Systems Engineering is focused on coupling traditional systems
engineering activities with enterprise activities of strategic planning and investment analysis [Carlock
and Fenton 2001].
Definition 2: System of Systems Integration is a method to pursue development, integration,
interoperability, and optimization of systems to enhance performance in future battlefield scenarios
[Pei 2000].
Definition 3: Systems of systems exist when there is a presence of a majority of the following five
characteristics: operational and managerial independence, geographic distribution, emergent behavior,
and evolutionary development [Jamshidi 2005].
Definition 4: Systems of systems are large-scale concurrent and distributed systems that are
comprised of complex systems [Jamshidi 2005; Carlock and Fenton 2001].
Definition 5: In relation to joint war-fighting, system of systems is concerned with interoperability and
synergism of Command, Control, Computers, Communications, and Information (C4I) and
Intelligence, Surveillance, and Reconnaissance (ISR) Systems [Manthorpe 1996].
Definition 6: SoSE involves the integration of systems into systems of systems that ultimately
contribute to evolution of the social infrastructure [Luskasik 1998].
Detailed literature survey and discussions on these definitions are given in [Jamshidi 2005, 2008].
Various definitions of SoS have their own merits, depending on their application.
Favorite definition of this author and the volume’s editor is Systems of systems are large-scale
integrated systems which are heterogeneous and independently operable on their own, but are
networked together for a common goal. The goal, as mentioned before, may be cost, performance,
robustness, etc.
1.3 PROBLEMS IN SYSTEM OF SYSTEMS
In the realm of open problems in SoS, just about anywhere one touches, there is an unsolved problem
and immense attention is needed by many engineers and scientists. No engineering field is more
urgently needed in tackling SoS problems than SE – system engineering. On top of the list of
engineering issues in SoS is the “engineering of SoS,” leading to a new filed of SoSE (Wells and Sage,
2008). How does one extend SE concepts like analysis, control, estimation, design, modeling,
controllability, observability, stability, filtering, simulation, etc. can be applied to SoS? Among
numerous open questions are how can one model and simulate such systems (see Chapter 4 by Sahin,
et al.). In almost all cases a chapter in this volume will accommodate the topic raised. Additional
references are given to enhance a search by the interested reader.
1.3.1 Theoretical Problems
In this section a number of urgent problems facing SoS and SoSE are discussed. The major issue
here is that a merger between SoS and engineering needs to be made. In other words, systems
engineering (SE) needs to undergo a number of innovative changes to accommodate and encompass
SoS.
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Architecting System-of-Systems Solutions – Cole in Chapter 2 presents solution ideas for SoS
architectures. Author indicates that along with defining the problem, one of the most important jobs of
the systems engineer is to partition the problem into smaller, more manageable problems and make
critical decisions about the solution. One of the most critical decisions is the architecture – the
fundamental organization of a system embodied in its components, their relationships to each other,
and to the environment, and the principles guiding its design and evolution [ANSI/IEEE-1472, 2000].
While it is impossible to understand all the characteristics and consequences of the architecture at the
time the system is designed, it is possible to produce a system architecture that maximizes the ability of
the system to meet user needs, while minimizing the unintentional consequences.
In one sense, architecting a complex system that is comprised of a number of collaborating
independent systems is no different than designing a simple system. Both start with definition of a problem
and conception of solution. Both warrant consideration of environmental context. Both involve analysis of
factors related to effectiveness. And both require design compromises and balancing of competing
priorities. The basic process is the same. In fact, it has been well documented for nearly 50 years [Asimow,
1962]. But compared to the design of simple systems, the complexity of designing a system-of-systems
(SoS) solution is daunting and the design process must be approached with that complexity in mind.
Details are found here in Chapter 2.
Dagli and Ergin [2008] also provide a framework for SoS Architectures. As the world is moving towards a
networked society, the authors assert, the business and government applications require integrated systems
that exhibit intelligent behavior. The dynamically changing environmental and operational conditions
necessitate a need for system architectures that will be effective for the duration of the mission but evolve
to new system architectures as the mission changes. This new challenging demand has led to a new
operational style: Instead of designing or subcontracting systems from scratch, business or government
gets the best systems the industry develops and focuses on becoming the lead system integrator to provide
a System of Systems (SoS). SoS is a set of interdependent systems that are related or connected to provide
a common mission. In the SoS environment, architectural constraints imposed by existing systems have a
major effect on the system capabilities, requirements and behavior. This fact is important, as it complicates
the systems architecting activities. Hence, architecture becomes a dominating but confusing concept in
capability development. There is a need to push system architecting research to meet the challenges
imposed by new demands of the SoS environment. This chapter focuses on System of Systems architecting
in terms of creating meta-architectures from collections of different systems. Several examples are
provided to clarify System of Systems architecting concept. Since the technology base, organizational
needs, and human needs are changing, the System of System architecting becomes an evolutionary
process. Components and functions are added, removed, and modified as owners of the SoS experience
and use the system. Thus in evolutionary system architecting is described and the challenges are identified
for this process. Finally, the authors discuss the possible use of artificial life tools for the design and
architecting of SoS. Artificial life tools such as swarm intelligence, evolutionary computation and
multi-agent systems have been successfully used for the analysis of complex adaptive systems. The
potential use of these tools for SoS analysis and architecting are discussed, by the authors, using several
domain application specific examples.
Emergence of SoS, Socio-Cognitive Aspects – McCarter and White in Chapter 3, address emergence in
SoS. The authored have offered a human-centric treatment of the concepts of multi-scale analysis and
emergence in system of systems (SoS) engineering, or more generally, complex systems engineering
(CSE) . This includes a characterization of what an individual might do in conceptualizing a given systems
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engineering situation s/he is facing. The authors suggest fresh interpretations of the terms scale and
emergence that will contribute to a more collaborative approach to improving the CSE practice. Because
other authors use “scale” in several different ways, potentially causing confusion, the present authors
propose “view” instead. Here a given view is defined as a combination of “scope”, “granularity”,
“mindset”, and “timeframe”. Although “emergence” has a rich spectrum of definitions in the literature, the
authors prefer to emphasize the unexpected, especially “surprising”, flavor of emergence. In this endeavor
socio-cognitive aspects are paramount, and in an age of increasing organizational complexity, small group
dynamics are becoming more significant. Human psychological and social behaviors that impact
information sharing, productivity, and system interoperability need closer examination as organizations
increasingly rely on decentralization to achieve their goals. Therefore, this chapter also highlights
important dynamic social issues, although the authors do not focus on trying to solve them. Rather, areas
of further research are suggested which can lead to better CSE, when people are considered part of any
(complex) system in the SoS.
Emergence of SoS, Socio-Cognitive Aspects – McCarter and White, in Chapter 3, have offered a
human-centric treatment of the concepts of multi-scale analysis and emergence in system of systems (SoS)
engineering, or more generally, complex systems engineering (CSE) [Gladwell, 2005]. This includes a
characterization of what an individual might do in conceptualizing a given systems engineering situation
s/he is facing. The authors suggest fresh interpretations of the terms scale and emergence that will
contribute to a more collaborative approach to improving the CSE practice. Because other authors use
“scale” in several different ways, potentially causing confusion, the present authors propose “view”
instead. Here a given view is defined as a combination of “scope”, “granularity”, “mindset”, and
“timeframe”. Although “emergence” has a rich spectrum of definitions in the literature, the authors prefer
to emphasize the unexpected, especially “surprising”, flavor of emergence. In this endeavor
socio-cognitive aspects are paramount, and in an age of increasing organizational complexity, small group
dynamics are becoming more significant. Human psychological and social behaviors that impact
information sharing, productivity, and system interoperability need closer examination as organizations
increasingly rely on decentralization to achieve their goals. Therefore, this chapter also highlights
important dynamic social issues, although the authors do not focus on trying to solve them. Rather, areas
of further research are suggested which can lead to better CSE, when people are considered part of any
(complex) system in the SoS.
SoS Simulation – Sahin, et al. in Chapter 4 provide a framework for simulation of SoS. They have
presented a SoS architecture based on Extensible Markup Language (XML) in order to wrap data coming
from different systems in a common way. The XML can be used to describe each component of the SoS
and their data in a unifying way. If XML based data architecture is used in a SoS, only requirement for the
SoS components to understand/parse XML file received from the components of the SoS. In XML, data
can be represented in addition to the properties of the data such as source name, data type, importance of
the data, and so on. Thus, it does not only represent data but also gives useful information which can be
used in the SoS to take better actions and to understand the situation better. The XML language has a
hierarchical structure where an environment can be described with a standard and without a huge
overhead. Each entity can be defined by the user in the XML in terms of its visualization and
functionality. As a case study in this effort (see also [Mittal, et al. 2008]) a master-scout rover
combination representing a SoS where first a sensor detects a fire in a field. The fire is detected by the
master rover and commands the scout rover to verify the existence of the fire. It is important to note that
such an architecture and simulation does not need any mathematical model for members of the systems.
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Biltgen in Chapter 5 provides a fundamental view of technology and its evaluation for SoS. The author
notes that technology evaluation is the assessment of the relative benefit of a proposed technology for a
particular purpose with respect to one or more metrics. While a number of quantitative assessment
techniques have been developed to support engineering and design of systems, a flexible, traceable, rapid
method for technology evaluation for systems-of-systems is needed. The primary difficulty in the
assessment process is the complexity associated with large-scale systems-of-systems and the integrated
nature of models required to study complex phenomena. The author goes on to note that many qualitative
techniques exist for resource allocation, an approach based on modeling and simulation is usually required
to quantify technology potential with respect to systems-of-systems level Measures of Effectiveness
(MoEs). The modeling and simulation environment relies on a linked suite of variable fidelity models that
calculate the impact of technologies across a system-of-systems hierarchy. Unfortunately, the run time of
this suite of tools is often prohibitive for exploratory analysis and domain spanning optimization
applications. In this chapter, the concept of surrogate models is introduced to speed up the analysis
process, provide a measure of transparency to the underlying physical phenomena, and to enable the
execution of design-of-experiments across a range of technology parameters at all levels of the
system-of-systems hierarchy. While polynomial surrogate models have exhibited much promise in
systems design applications, neural network surrogates are introduced in this chapter due to their ability to
capture the nonlinearities and discontinuities often present in systems-of-systems problems. Techniques
for data farming and visualization of results are also introduced as a means to understand the intricacies of
complex design spaces and an approach to “inverse design” where any variable can be treated as an
independent variable is demonstrated. The inverse design technique allows the user to set thresholds for
capability-based MoEs and decompose the problem to identify the critical technology factors that are most
significant to the responses in question. Finally, insight is provided into the value of the surrogate-based
approach for understanding sensitivities and quantifying the relative benefit of proposed technologies
with respect to the appropriate MoEs at the system-of-systems level.
Enterprise Systems of Systems – Rebovitch in Chapter 6 takes on the enterprise systems engineering. The
author notes that the 21st century is an exciting time for the field of systems engineering. Advances in our
understanding of the traditional discipline are being made. At the same time new modes of systems
engineering are emerging to address the engineering challenges of systems-of-systems and enterprise
systems. Even at this early point in their evolution, these new modes of systems engineering are evincing
their own principles, processes and practices. Some are different in degree than engineering at the system
level while others are different in kind.
While it is impossible to predict how the traditional and new forms of systems engineering will evolve in
the future, it is clear even now that there is a long and robust future for all three. Increases in technology
complexity have led to new challenges in architecture, networks, hardware and software engineering, and
human systems integration. At the same time, the scale at which systems are engineered is exceeding
levels that could have been imagined only a short time ago. As a consequence, all three forms of systems
engineering will be needed to solve the engineering problems of the future, sometimes separately but
increasingly in combination with each other.
This chapter defines three modes of systems engineering, discusses the challenge space each addresses,
describes how they differ from and complement each other, and suggests what their interrelationships
should be in solving engineering problems of the future.
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Definition, Classification and Methodological Issues Of System of Systems – Bjelkemyr, et. al. in
Chapter 7 present a number of basic issues in Sos, similar to those discussed in this chapter. They present
a generic definition of the term System-of-Systems is established. This definition, the authors go on, is
based both on common definitions of SoS and on the characteristics that systems that are usually labeled
SoS exhibit. Due to the inclusive nature of this generic definition, a wide range of socio-technical systems
of very different size and complexity are included in the concept of SoS. In an attempt to delimit the
concept, a classification is suggested based on the redundancy of higher-level subsystems. Most systems
that traditionally are considered SoS are labeled SoS type I, e.g. International Space Station, an integrated
defense systems, national power supply networks, transportation systems, larger infrastructure
constructions; and systems with non-redundant subsystems are simply labeled SoS type II., in this chapter
exemplified by a production system. The purpose of this classification is partly to advance knowledge of
both SoS characteristics and how to address them, and partly to improve transferring of this knowledge
from the area of traditional SoS to other areas where SoS characteristics are present.
1.3.2 Implementation Problems
Asides from many theoretical and essential difficulties with SoS, there are many implementation
challenges facing SoS. Here some of these implementation problems are briefly discussed and references
are made to some with their full coverage.
Policymaking to Reduce Carbon Emissions – Agusdinata, et al. in Chapter 8, presents a
system-of-systems perspective combined with exploratory modeling and analysis method as an approach
to deal with complexity and uncertainty in policymaking. The test case is reduction of CO2 (carbon)
emissions, which stems from the interactions of many independent players involved and the multiple
aspects. The uncertainty about the physical and socio-economic systems is compounded by the long time
horizon the policymaking covers. Using a case of the long-term carbon emissions in Dutch residential
sector, the SoS perspective is used as a way to decompose the policy issue into interdependent relevant
policy systems. This representation of the policy system provides a framework to test a large number of
hypotheses about the evolution of the system’s performance by way of computational experiments, which
forms the core of the EMA method. In particular, in a situation where the realized emission level misses
the intermediate target in the year 2025, policies can be adapted, amongst others, by increasing the subsidy
on energy efficiency measures and tightening the standard for dwelling energy performance. As some of
the system uncertainties are resolved, we test whether adapting policies can lead to meeting the policy
target in the year 2050. Our approach shows the extent to which the constraints imposed on the
System-of-Systems should be relaxed in order to bring the emission level back to the intended target. The
relaxation of the constraints, which include among others energy prices, investment behavior,
demographic, and technological developments also point out to the different policy designs that
decisionmakers can envisioned to influence the performance of a System-of-Systems.
Medical and Health Management System of Systems – In Chapter 9, Hata, et al. have bridged a gap
between medical systems and SoS. Such system is widely used in diagnosis, treatment, and management
for patients. Human health management for healthy persons has much considerable attentions as a new
application domain in system engineering. In this chapter, we focused on ultrasonic surgery support,
medical imaging, and health management system of systems engineering. These application domains
discussed here are indeed broad and essential in daily clinical practice and health management. First one is
“Systems of Systems in Medical Ultrasonics”. Current modern ultrasonic systems require an integrated
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system of systems engineering, i.e., integrated hardware system and modern software system. This section
introduces a novel ultrasonic surgery support system in orthopedic surgery. Second one is “System of
Systems in Medical Imaging.” This section introduces an approach to embed medical expert knowledge
into image processing based on fuzzy logic. To demonstrate the effectiveness of the new approach,
applications to human brain MR images and orthopedic kinematic analysis are introduced. Third one is
“System of Systems in Human Health Management”. An idea of health management is introduced and
discussed from the views of system of systems. As its application to human health, sensing and control
technology during sleep is focused on because quality and quantity of sleep has serious influence to our
body health.
The Microgrid as a System of Systems – Phillips, in Chapter 10 , has introduced electric microgrids as a
SoS. The author point souf that microgrids offer exciting possibilities for efficient, uninterruptible power.
A microgrid is a collection of small, non-collocated electric power sources, storage devices, and power
conditioners interconnected to meet the power consumption needs of a designated community. The
general vision is that a microgrid might produce power for a small to mid-size neighborhood, industrial
park, or commercial enclave. A microgrid is different from a power plant primarily because the generators
aren’t collocated, so it can’t easily be operated, managed, or controlled in a unified way by a single
committee. At the same time, a microgrid, because of the interconnectivity, can be operated more
efficiently than independent sources of similar capacity; more of the sources at any given point can be
operated at peak efficiency, transmission losses are minimized, and—perhaps the most enticing
thought—significant co-produced heat can be used for space and water heating and a myriad of industrial
uses because the sources are near the loads. All well and good, but if this is such a great idea, where are all
the microgrids? Unfortunately, fielding a microgrid requires a relatively complex system of systems, only
part of which is the already complicated electric power substrate. In addition, to have significant impact,
microgrids need to operate in conjunction with the primary power grid; this adds to operational difficulty
and forces economic considerations to be taken into account operationally. In this chapter we delineate and
discuss the communication, management, decisionmaking, and other systems needed for microgrid
success.
Intelligent Decision Support System based on Sensor and Computer Networks – Wu, et al. in Chapter
11, have presented an intelligent decision support system based on sensor and computer networks that
incorporates various subsystems for sensor deployment, data routing, distributed computing, and
information fusion. The integrated system is deployed in a distributed environment composed of both
wireless sensor networks for data collection and wired computer networks for data processing. For these
subsystems, we formulate the analytical problems and develop approximate or exact solutions: (i) sensor
deployment strategy based on a two-dimensional genetic algorithm to achieve maximum coverage with
cost constraints; (ii) data routing scheme to achieve maximum signal strength with minimum path loss,
energy efficiency, and fault tolerance; (iii) network mapping method based on dynamic programming to
assign computing modules to network nodes for distributed sensor data processing; and (iv) binary
decision fusion rule that derive threshold bounds to improve system hit rate and false alarm rate. These
subsystems are implemented and evaluated through either experiments or simulations in various
application scenarios. The extensive results demonstrate that these component solutions imbue the
integrated system with the desirable and useful quality of intelligence.
Defense applications of SoS – Dickerson, in Chapter 12, takes on the defense applications of SoS. The
author notes that the defense community continues to move towards increasingly complex weapon
systems that must support joint and coalition operations, the need for system of systems (SoS) engineering
becomes more critical to the achievement of military capabilities. The U.S. Department of Defense (DoD)
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and the U.K. Ministry of Defence (MOD) continue to face a critical challenge: the integration of multiple
capabilities across developing and often disparate legacy systems that must support multiple warfare areas.
Over the past decade, the need for SoS enabled mission capabilities have led to changes in policy for the
acquisition of systems and the assemblage of battle forces. Defense acquisition structures have been
reorganizing in order to integrate acquisition activities in a way that leads to the achievement of
capabilities through a system of systems approach rather than from just the performance of individual
systems or platforms. Earlier efforts to meet the challenge faced by the defense community have included
solutions using Network Centric Operations (NCO) and Network Enabled Capabilities (NEC). Although
progress has been made, future progress can be expected to depend upon the advancement of the practice
of systems engineering at the SoS level. System of Systems Engineering (SoSE) processes and practices
must enable the acquisition of systems of systems that support the integration of multiple capabilities
across multiple warfare areas. Recently there has been extensive debate across a broad community of
government, industry, and academic stakeholders about systems engineering processes and practices at the
SoS level. But whatever form SoSE takes for defense systems, the technical processes of SoSE must
support the realization of mission capabilities attainable from a system of systems that cannot be attained
from a single system. Chapter 12 will focus on SoS initiatives and SoSE technical processes being used for
the development and acquisition of defense systems of systems. It will begin with the Revolution in
Military Affairs that led to Network Centric Warfare then show how the concepts of SoSE and network
enablement are intertwined. Together they not only enable mission capabilities but also are key to the
integration of capabilities. Key SoS initiatives in the DoD are discussed. Chapter 12 will conclude with
some of the current activities and emerging research opportunities in SoSE.
Systems of Air Vehicles – Colgren in Chapter 13, has considered a system of air vehicles as a natural test
bed for a SoS. The author reminds us that a longstanding example of such a system, developed long
before the term system-of-systems was coined, is the international airspace system intended for the safe
and efficient flight control of air vehicles. The International Civil Aviation Organization (ICAO), which
manages the international Air Traffic Control (ATC) system, has been in operation since April 1947. It
was build around pre-existing international agreements and regulations as an agency of the United Nations
(Ref. 1). It codifies the principles and techniques of international air navigation and fosters the planning
and development of international air transport to ensure safe and orderly growth (Ref. 2). It insures
standards, such as the international use of English for communications and the specific frequency ranges
to be used for these and all other required command and control operations. The ICAO Council adopts
standards and recommended practices for its 190 member states concerning air navigation, the prevention
of unlawful interference, and for facilitating border crossings for international civil aviation. In addition,
the ICAO defines the protocols for air accident investigations used by transport safety authorities in the
signatory countries, also commonly known as the Chicago Convention. The United States air traffic
control system is managed by the Federal Aviation Administration (FAA) to provide safe separation of
aircraft within U.S. airspace and in and out of U.S. airports (Ref. 3). While the basic design of the air
traffic control system dates back to the 1950s, this design has been adapted as the demands on the system’s
capacity has continued to rise. The three critical components of the ATC — communications, navigation,
and surveillance — must continuously be modernized to maintain the safety and efficiency of the air
transportation system. Such updates must be accomplished under international agreements, maintaining
global compatibility throughout the ATC system.
System of Autonomous Rovers and Their Applications – Sahin, et al in Chapter 14, present a typical
test bed for system of systems in the realm of mobile robotics. Here a system of autonomous rovers will be
presented in the context of system of systems. In addition, a system of homogenous modular micro robots
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will be presented in the context of system of systems. The chapter starts with the introduction of the
components and their roles in the system of autonomous rovers. Then, each system will be presented
focusing on electrical, mechanical and control characteristics and their capabilities in the system of
autonomous rovers. Robust data aggregation and mine detection are then examined as applications of the
system of autonomous rovers.
Space Applications of System of Systems – Caffall and Michael in Chapter 15, have started by referring
to future applications of SoS via the popular fictional space program on television – “Space…the Final
Frontier”. These are the voyages of the starship Enterprise. Its five-year mission: to explore strange new
worlds, to seek out new life and new civilizations, to boldly go where no man has gone before. The authors
asks the question: How many of us heard these words and dreamed of standing alongside Star Trek’s
Captain James T. Kirk and First Officer Mr. Spock to explore new worlds? Interesting enough, Chief
Engineer Commander Scott performs countless technological miracles as he saves his beloved USS
Enterprise and crew from certain destruction; however, Scotty never mentions the miracle of the
Enterprise as a wonderful system-of-systems. Could it be that future engineers solved all the problems of
system-of-systems? Could it be that Scotty never encountered problems with the system-of-systems
comprising the Enterprise, freeing him up to devote his time on advancing the interplanetary
communications, warp engine, phaser, transporter, deflector shield and other systems of the Enterprise?
Apparently not, at least as evidenced by, for instance, the emergent behavior of the Enterprise as it
interacts with a (fictitious) legacy unmanned scientific probe named Voyager 6 whose onboard systems
have been reconfigured and augmented by an alien race. The author further note that it is up to us to
advance the system-of-systems technology for the development of a space foundation that, indeed, may
lead to the Federation and star ships. While many folks almost exclusively think of space exploration as
the Shuttle Orbiter that has captured worldwide interest for over twenty-five years, space exploration is
multi-faceted. The National Aeronautics and Space Administration (NASA) has a extraordinary history
of space exploration to include the Rovers that continue to explore Mars, space telescopes that can see into
deep space, a human-inhabitable space station that orbits the Earth, unmanned spacecraft that explore the
Sun and the planets in our solar system, and the unforgettable lunar exploration that began on July 20,
1969 as Commander Neil Armstrong stepped onto the Moon’s surface. So, what is the relationship of
space exploration with a system-of-systems concept? Well, let us first consider space exploration
missions that are planned for the next couple of decades.
Airport Operations: A System of Systems Approach – Nahavandi, et al., in Chapter 16, have presented
a SoS approach t airport security. Analysis of airport operations is commonly performed in isolation,
sharing only simple information such as flight schedules. With the increased concern over security in our
airports a new approach is required whereby all aspects of the airport operations are considered. A System
of Systems methodology is proposed and demonstrated through example. This method provides the
decision maker with an improved understanding of the implication of policy decisions, resource
allocations and infrastructure investment strategies, through the capture of emergent behaviours and
interdependencies. New tools and methodologies are required for model development and analysis. These
tools and methods are presented in this paper.
KASERS in SoS Design – Rubin in Chapter 17 has used KASER – Knowledge Amplification by
Structured Expert Randomization as a tool for the SoS design. The author indicates that the U.S. Army
needs robotic combat vehicles that can autonomously navigate the battlefield and carry out planned
missions that necessarily embody unplanned details. On one end of the spectrum lie the simple insect-like
robots that have been popularized by Steel and Brooks [1995]. Their simple behaviors can be evolved
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much in the same manner as a simple program can be created through the use of chance alone. Of course,
more complex behaviors cannot be tractably evolved because the search space here grows exponentially.
What is needed are heuristics to guide the evolutionary process. We can of course program search
strategies and have indeed programmed robots to perform a myriad of complex functions – from the robot
Manny’s (U.S. Army) ability to walk the battlefield to UAVs. What is needed is a means to program more
complex and reliable functionality for constant cost. That is, a System of Systems (SoS) is needed. For
example, one can program a robotic vehicle to sense and avoid an obstacle on the right say. But then, what
is the cost of programming the same robot to sense and avoid an obstacle on the left? It should be less and
is to some extent if object-oriented component-based programs are written. The problem here though is
that the cost is front loaded. The programmer needs to know a priori most of the domain symmetries if he
or she is to capture them in the form of objects. A better solution is to do for symbolic programming what
fuzzy logic did for Boolean logic [Zadeh, 1996; Rubin 1999]. That is, we need the programmer to be able
to constrain the robot’s behavior in the form generalized actions. Then, instances of these generalizations
constitute the desired program. Even search-control heuristics can be acquired through the exercise of this
paradigm. Instead of programming the robot to do a specific action, we program the robot to (heuristically)
search a space of actions for one or more that is consistent with environmental constraints. The writing of
such programs is easier for the human and the constraints that instantiate them serve to render the search
space tractable for the machine.
18 Johnson
1.4 OTHER SOSE ISSUES
In this section, for the benefit of the readers, a wider perspective on system of systems and system of
systems engineering from a recent work by the author [Jamshidi, 2008] will be given.
Standards of SoS – System of systems literature, definitions, and perspectives are marked with great
variability in the engineering community. Viewed as an extension of systems engineering to a means of
describing and managing social networks and organizations, the variations of perspectives leads to
difficulty in advancing and understanding the discipline. Standards have been used to facilitate a common
understanding and approach to align disparities of perspectives to drive a uniform agreement to definitions
and approaches. By having the ICSOS – International Consortium on System of Systems [De Larentis, et
al. 2007] represent to the IEEE and INCOSE for support of technical committees to derive standards for
system of systems will help unify and advance the discipline for engineering, healthcare, banking, space
exploration and all other disciplines that require interoperability among disparate systems [De Larentis
2007].
Open Systems Approach to System of Systems Engineering – Azani [2008] in Jamshidi [2008] has
discussed an open systems approach to SoSE. The author notes that SoS exists within a continuum that
contains ad-hoc, short-lived, and relatively speaking simple SoS on one end, and long lasting, continually
evolving, and complex SoS on the other end of the continuum. Military operations and less sophisticated
biotic systems (e.g., bacteria and ant colonies) are examples of ad-hoc, simple, and short lived SoS, while
galactic and more sophisticated biotic systems (e.g., ecosystem, human colonies) are examples of SoS at
the opposite end of the SoS continuum. The engineering approaches utilized by galactic SoS are at best
unknown and perhaps forever inconceivable. However, biotic SoS seem to follow, relatively speaking,
11
less complicated engineering and development strategies allowing them to continually learn and adapt,
grow and evolve, resolve emerging conflicts, and have more predictable behavior. Based on what the
author already knows about biotic SoS, it is apparent that these systems employ robust reconfigurable
architectures enabling them to effectively capitalize on open systems development principles and
strategies such as modular design, standardized interfaces, emergence, natural selection, conservation,
synergism, symbiosis, homeostasis, and self-organization. Azani [2008] provides further elaboration on
open systems development strategies and principles utilized by biotic SoS, discusses their implications for
engineering of man-made SoS, and introduces an integrated SoS development methodology for
engineering and development of adaptable, sustainable, and interoperable SoS based on open systems
principles and strategies.
SoS Integration – Integration is probably the key viability of any SoS. Integration of SoS implies that
each system can communicate and interact (control) with the SoS regardless of their hardware, software
characteristics or nature. This means that they need to have the ability to communicate with the SoS or a
part of the SoS without compatibility issues such as operating systems, communication hardware, and so
on. For this purpose, a SoS needs a common language the SoS’s systems can speak. Without having a
common language, the systems of any SoS cannot be fully functional and the SoS cannot be adaptive in the
sense that new components cannot be integrated to it without major effort. Integration also implies the
control aspects of the SoS because systems need to understand each other in order to take commands or
signals from other SoS systems. For further insight, see the work by Cloutier, et al [2008] on network
centric architecture of SoS.
Engineering of SoS – Emerging needs for a comprehensive look at the applications of classical systems
engineering issue in SoSE will be discussed in this volume. The thrust of the discussion will concern the
reality that the technological, human, and organizational issues are each far different when considering a
system of systems or federation of systems and that these needs are very significant when considering
system of systems engineering and management. As we have noted, today there is much interest in the
engineering of systems that are comprised of other component systems, and where each of the component
systems serves organizational and human purposes. These systems have several principal characteristics
that make the system family designation appropriate: operational independence of the individual systems,
managerial independence of the systems; often large geographic and temporal distribution of the
individual systems; emergent behavior, in which the system family performs functions and carries out
purposes that do not reside uniquely in any of the constituent systems but which evolve over time in an
adaptive manner and where these behaviors arise as a consequence of the formation of the entire system
family and are not the behavior of any constituent system. The principal purposes supporting engineering
of these individual systems and the composite system family are fulfilled by these emergent behaviors.
Thus, a system of systems is never fully formed or complete. Development of these systems is
evolutionary and adaptive over time, and structures, functions, and purposes are added, removed, and
modified as experience of the community with the individual systems and the composite system grows and
evolves. The systems engineering and management of these systems families poses special challenges.
This is especially the case with respect to the federated systems management principles that must be
utilized to deal successfully with the multiple contractors and interests involved in these efforts. Please
refer to the paper by Sage and Biemer [2007] and De Larentis, et al. [2007] for the creation of a SoS
Consortium (ICSOS) of concerned individuals and organizations by the author of this chapter. See Wells
and Sage [2008] for more information and challenges ahead in SoSE.
12
SoS Management: The Governance of Paradox – Sauser and Boardman [2008] in Jamshidi [2008] have
presented an SoS approach to the management problem. They note that the study of SoS has moved many
to support their understanding of these systems through the groundbreaking science of networks. The
understanding of networks and how to manage them may give one the fingerprint which is independent of
the specific systems that exemplify this complexity. The authors point out that it does not matter whether
they are studying the synchronized flashing of fireflies, space stations, structure of the human brain, the
internet, the flocking of birds, a future combat system or the behavior of red harvester ants. The same
emergent principles apply: large is really small; weak is really strong; significance is really obscure; little
means a lot; simple is really complex; and, complexity hides simplicity. The conceptual foundation of
complexity is paradox, which leads us to a paradigm shift in the SE (systems engineering) body of
knowledge.
Paradox exists for a reason and there are reasons for systems engineers to appreciate paradox even
though they may be unable to resolve them as they would a problem specification into a system solution.
Hitherto paradoxes have confronted current logic only to yield at a later date to more refined thinking. The
existence of paradox is always the inspirational source for seeking new wisdom, attempting new thought
patterns and ultimately building systems for the “flat world.” It is our ability to govern, not control, these
paradoxes that will bring new knowledge to our understanding on how to manage the emerging complex
systems called System of Systems.
Sauser and Boardman [2008] have established a foundation in what has been learnt about how one
practices project management, establish some key concepts and challenges that make the management of
SoS different from our fundamental practices, present an intellectual model for how they classify and
manage a SoS, appraise this model with recognized SoS, and conclude with grand challenges for how they
may move their understanding of SoS management beyond the foundation.
Deepwater Coastguard Program – One of the earliest realization of a SoS in the United States is the
so-called Deepwater Coastguard Program shown in Figure 1). As seen here, the program takes advantage
of all the necessary assets at their disposal, e.g. helicopters, aircrafts, cutters, satellite (GPS), ground
station, human, computers, etc. – all systems of the SoS integrated together to react to unforeseen
circumstances to secure the coastal borders of the Southeastern United States, e.g. Florida Coast. The
Deepwater program is making progress in the development and delivery of mission effective command,
control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) equipment
[Keeter 2007]. The SoS approach, the report goes on, has “improved the operational capabilities of legacy
cutters and aircraft, and will provide even more functionality when the next generation of surface and air
platforms arrives in service. “ The key feature of the system is its ability to interoperate among all Coast
Guard mission assets and capabilities with those of appropriate authorities both at local and federal levels.
13
Figure 1. A security example of a SoS – Deepwater coastguard configuration in US
Future Combat Missions – Another national security or defense application of SoS is the future combat
mission (FCM). Figure 2) shows one of numerous possible configuration of an FCM. The FCM system is
“envisioned to be an ensemble of manned and potentially unmanned combat systems, designed to ensure
that the Future Force is strategically responsive and dominant at every point on the spectrum of operations
from non-lethal to full scale conflict. FCM will provide a rapidly deployable capability for mounted tactical
operations by conducting direct combat, delivering both line-of-sight and beyond-line-of-sight precision
munitions, providing variable lethal effect (non-lethal to lethal), performing reconnaissance, and
transporting troops. Significant capability enhancements will be achieved by developing multi-functional,
multi-mission and modular features for system and component commonality that will allow for multiple
state-of-the-art technology options for mission tailoring and performance enhancements. The FCM force
will incorporate and exploit information dominance to develop a common, relevant operating picture and
achieve battle space situational understanding” [Global Security Organization, 2007].
14
Figure 2. A defense example of a SoS (Courtesy, Don Walker, Aerospace Corporation)
Systems Engineering for the Department of Defense System of Systems – Dahmann and Baldwin
[2008] in Jamshidi [2008], have addressed the national defense aspects of SoS. Military operations are the
synchronized efforts of people and systems toward a common objective. In this way from an operational
perspective, defense is essentially a ‘systems of systems’ (SoS) enterprise. However, despite the fact that
today almost every military system is operated as part of a system of systems, most of these systems were
designed and developed without the benefit of systems engineering at the SoS level factoring the role the
system will play in the broader system of systems context. With changes in operations and technology, the
need for systems that work effectively together is increasingly visible.
Sensor Networks – The main purpose of sensor networks is to utilize the distributed sensing capability
provided by tiny, low powered and low cost devices. Multiple sensing devices can be used cooperatively
and collaboratively to capture events or monitor space more effectively than a single sensing device
[Sridhar, et. al., 2007]. The realm of applications for sensor networks is quite diverse, which include
military, aerospace, industrial, commercial, environmental, health monitoring, to name a few. Applications
include: traffic monitoring of vehicles, cross-border infiltration detection and assessment, military
reconnaissance and surveillance, target tracking, habitat monitoring and structure monitoring, etc.
Communication capability of these small devices and often with heterogeneous attributes makes
them good candidates for system of systems. Numerous issues exist with sensor networks such as data
integrity, data fusion and compression, power consumption, multi-decision making, and fault tolerance all
make these SoS very challenging just like other SoS. It is thus necessary to devise a fault-tolerant
mechanism with a low computation overhead to validate the integrity of the data obtained from the sensors
(“systems”). Moreover, a robust diagnostics and decision making process should aid in monitoring and
control of critical parameters to efficiently manage the operational behavior of a deployed sensor network.
Specifically, Sridhar, et al [2008]. will focus on innovative approaches to deal with multi-variable
multi-space problem domain as well as other issues, in wireless sensor networks within the framework of a
SoS. In this book, see Chapter 11 by Wu, et. Al. for a SoS approach in treating sensor networks.
Healthcare Systems – Under a 2004 Presidential Order, the US Secretary of Health has initiated the
development of a National Healthcare Information Network (NHIN), with the goal of creating a nationwide
information system that can build and maintain Electronic Health Records (EHRs) for all citizens by 2014.
The NHIN system architecture currently under development will provide a near-real-time heterogeneous
integration of disaggregated hospital, departmental, and physician patient care data, and will assemble and
present a complete current EHR to any physician or hospital a patient consults [Sloane 2006]. The NHIN
will rely on a network of independent Regional Healthcare Information Organizations (RHIOs) that are
being developed and deployed to transform and communicate data from the hundreds of thousands of
legacy medical information systems presently used in hospital departments, physician offices, and
telemedicine sites into NHIN-specified meta-formats that can be securely relayed and reliably interpreted
anywhere in the country. The NHIN “network of networks” will clearly be a very complex SoS, and the
performance of the NHIN and RHIOs will directly affect the safety, efficacy, and efficiency of healthcare in
the US. Simulation, modeling, and other appropriate SoSE tools are under development to help ensure
reliable, cost-effective planning, configuration, deployment, and management of the heterogeneous,
15
life-critical NHIN and RHIO systems and subsystems [Sloane, et al. 2007]. ICSOS represents an invaluable
opportunity to access and leverage SoSE expertise already under development in other industry and
academic sectors. ICSOS also represents an opportunity to discuss the positive and negative emergent
behaviors that can significantly affect personal and public health status and the costs of healthcare in the US
[DeLarentis, et al. 2007].
Global Earth Observation System of Systems – GEOSS is a global project consisting of over 60 nations
whose purpose is to address the need for timely, quality, long-term, global information as a basis for sound
decision making [Butterfield, et al, 2006]. Its objectives are: (i) Improved coordination of strategies and
systems for Earth observations to achieve a comprehensive, coordinated, and sustained Earth observation
system or systems;, (ii) A coordinated effort to involve and assist developing countries in improving and
sustaining their contributions to observing systems, their effective utilization of observations and the
related technologies, and (iii) The exchange of observations recorded from in situ, air full and open
manner with minimum time delay and cost. In GEOSS, the “SoSE Process provides a complete, detailed,
and systematic development approach for engineering systems of systems. Boeing’s new
architecture-centric, model-based systems engineering process emphasizes concurrent development of the
system architecture model and system specifications. The process is applicable to all phases of a system’s
lifecycle. The SoSE Process is a unified approach for system architecture development that integrates the
views of each of a program’s participating engineering disciplines into a single system architecture model
supporting civil and military domain applications” [Pearlman 2006]. ICSoS will be another platform for
all concerned around the globe to bring the progress and principles of GEOSS to formal discussions and
examination on an annual basis.
Satelli
te
Radioson
de
Rad
ar Profil
er
Weather Systems
Integrated Storm
Impact & Response
Measurements &
Analysis
System
Products
Responders’
Information
California Pictures
16
Figure 3. SoS of the GEOSS Project (Courtesy, Jay Pearlaman, Boeing Company)
E-enabling and SoS Aircraft Design via SoSE – A case of aeronautical application of SoS worth noting
is that of E-enabling in aircraft design as a system of a SoS at Boeing Commercial Aircraft Division
[Wilber 2008]. The project focused on developing a strategy and technical architecture to facilitate making
the airplane (Boeing 787, see Figure 4 ) network-aware and capable of leveraging computing and network
advances in industry. The project grew to include many ground based architectural components at the
airlines and at the Boeing factory, as well as other key locations such as the airports, suppliers and
terrestrial Internet Service Suppliers (ISPs).
Wilber [2008] points out that the e-Enabled project took on the task of defining a system of systems
engineering solution to problem of interoperation and communication with the existing, numerous and
diverse elements that make up the airlines’ operational systems (flight operations and maintenance
operations). The objective has been to find ways of leveraging network-centric operations to reduce
production, operations and maintenance costs for both Boeing and the airline customers.
One of the key products of this effort is the “e-Enabled Architecture”. The e-Enabling Architecture is
defined at multiple levels of abstraction. There is a single top-level or “Reference Architecture” that is
necessarily abstract and multiple “Implementation Architectures”. The implementation architectures
map directly to airplane and airline implementations and provide a family of physical solutions that all
exhibit common attributes and are designed to work together and allow re-use of systems components.
The implementation architectures allow for effective forward and retrofit installations addressing a wide
range of market needs for narrow and widebody aircraft.
The 787 “Open Data Network” is a key element of one implementation of this architecture. It
enabled on-board and off-board elements to be networked in a fashion that is efficient, flexible and
secure. The fullest implementations are best depicted in Boeing’s GoldCare Architecture and
design.
Wilber [2008] presented an architecture at the reference level and how it has been mapped into the 787
airplane implementation. GoldCare environment is described and is used as an example of the full
potential of the current e-enabling.
17
Figure 4a. Boeing’s 787 Dreamliner
18
Figure 4b. E-Enabling Sos fro Boeing 787
Figure 4. Application of a SoS for the Boeing 787
A Systems of Systems perspective on Infrastructures – Thissen and Herder [2008] in Jamshidi [2008]
have touched upon a very important application in the service industry. Infrastructure Systems (or
infrasystems) providing services such as energy, transport, communications, and clean and safe water are
vital to the functioning of modern society. Key societal challenges with respect to our present and future
infrastructure systems relate to, among other things, safety and reliability, affordability, and transitions to
sustainability. Infrasystem complexity precludes simple answers to these challenges. While each of the
infrasystems can be seen as a complex system of systems in itself, increasing interdependency among these
systems (both technologically and institutionally) adds a layer of complexity.
One approach to increased understanding of complex infrasystems that has received little attention in the
engineering community thus far is to focus on the commonalities of the different sectors, and to develop
generic theories and approaches such that lessons from one sector could easily be applied to other sectors.
The system of systems paradigm offers interesting perspectives in this respect. The authors present, as an
initial step in this direction, a fairly simple three-level model distinguishing the physical/technological
systems, the organisation and management systems, and the systems and organisations providing
infrastructure related products and services. The authors use the model as a conceptual structure to identify
a number of key commonalities and differences between the transport, energy, drinking water, and ICT
sectors. Using two energy related examples, the authors further illustrate some of the system-of-systems
“the real-time connectivity of the
airplane to the ground, the delivery
of information across the
enterprise…”
19
related complexities of analysis and design at a more operational level. The authors finally discuss a
number of key research and engineering challenges related to infrastructure systems, with a focus on the
potential contributions of systems of systems perspectives.
A System of Systems View of Services – Tien [2008] in Jamshidi [2008] has covered a very important
applications of SoS in our today’s global village – service industry. The services sector employs a large
and growing proportion of workers in the industrialized nations, and it is increasingly dependent on
information technology. While the interdependences, similarities and complementarities of manufacturing
and services are significant, there are considerable differences between goods and services, including the
shift in focus from mass production to mass customization (whereby a service is produced and delivered in
response to a customer’s stated or imputed needs). In general, a service system can be considered to be a
combination or recombination of three essential components – people (characterized by behaviors,
attitudes, values, etc.), processes (characterized by collaboration, customization, etc.) and products
(characterized by software, hardware, infrastructures, etc.). Furthermore, inasmuch as a service system is
an integrated system, it is, in essence, a system of systems which objectives are to enhance its efficiency
(leading to greater interdependency), effectiveness (leading to greater usefulness), and adaptiveness
(leading to greater responsiveness). The integrative methods include a component’s design, interface and
interdependency; a decision’s strategic, tactical and operational orientation; and an organization’s data,
modeling and cybernetic consideration. A number of insights are also provided, including an alternative
system of systems view of services; the increasing complexity of systems (especially service systems),
with all the attendant life-cycle design, human interface, and system integration issues; the increasing need
for real-time, adaptive decision making within such systems of systems; and the fact that modern systems
are also becoming increasingly more human-centered, if not human-focused – thus, products and services
are becoming more complex and more personalized or customized.
System of Systems Engineering in Space Exploration – Jolly and Muirhead [2008], in Jamshidi [2008]
have covered SOSE topics that are largely unique for Space Exploration with the intent to provide the
reader a discussion of the key issues, the major challenges of the twenty-first century in moving from
systems engineering to SOSE, potential applications in the future, and the current state-of-the-art. Specific
emphasis is placed on how software and electronics are revolutionizing the way space missions are being
designed, including both the capabilities and vulnerabilities introduced. The role of margins, risk
management, and interface control are all critically important in current space mission design and
execution – but in SOSE applications they become paramount. Similarly, SOSE space missions will have
extremely large, complex, and intertwined command and control and data distribution ground networks,
most of which will involve extensive parallel processing to produce tera-to-petabytes of products per day
and distribute them worldwide.
Robotic swarms as a SoS – As another application of SoS, a robotic swarm is considered by Sahin [2008]
in Jamshidi [2008]. A robotic swarm based on ant colony optimization and artificial immune systems is
considered. In the ant colony optimization, the author has developed a multi agent system model based on
the food gathering behaviors of the ants. Similarly, a multi agent system model is developed based on the
human immune system. These multi agent system models, then, tested on the mine detection problem. A
modular micro robot is designed to perform to emulate the mine detection problem in a basketball court.
The software and hardware components of the modular robot are designed to be modular so that robots can
be assembled using hot swappable components. An adaptive TDMA communication protocol is
developed in order to control connectivity among the swarm robots without the user intervention.
20
Communication & Navigation in Space SoS – Bahsin and Hayden [2008] in Jamshidi [2008] have taken
upon the challenges in communication and navigation for space SoS. They indicate that communication
and navigation networks provide critical services in the operation, system management, information
transfer, and situation awareness to the space system of systems. In addition, space systems of systems are
requiring system interoperability, enhanced reliability, common interfaces, dynamic operations, and
autonomy in system management. New approaches to communications and navigation networks are
required to enable the interoperability needed to satisfy the complex goals and dynamic operations and
activities of the space system of systems. Historically space systems had direct links to Earth ground
communication systems, or they required a space communication satellite infrastructure to achieve higher
coverage around the Earth. It is becoming increasingly apparent that many systems of systems may include
communication networks that are also systems of systems. These communication and navigation networks
must be as nearly ubiquitous as possible and accessible on the demand of the user, much like the cell phone
link is available at any time to an Earth user in range of a cell tower. The new demands on communication
and navigation networks will be met by space Internet technologies. It is important to bring Internet
technologies, Internet Protocols (IP), routers, servers, software, and interfaces to space networks to enable
as much autonomous operation of those networks as possible. These technologies provide extensive
savings in reduced cost of operations. The more these networks can be made to run themselves, the less
humans will have to schedule and control them. The Internet technologies also bring with them a very
large repertoire of hardware and software solutions to communication and networking problems that
would be very expensive to replicate under a different paradigm. Higher bandwidths are needed to support
the expected voice, video, and data transfer traffic for the coordination of activities at each stage of an
exploration mission.
Existing communications, navigation, and networking have grown in an independent fashion with
experts in each field solving the problem just for that field. Radio engineers designed the payloads for
today’s “bent pipe” communication satellites. The Global Positioning satellite (GPS) system design for
providing precise Earth location determination is an extrapolation of the LOng RAnge Navigation
(LORAN) technique of the 1950’s where precise time is correlated to precise position on the Earth. Other
space navigation techniques use artifacts in the RF communication path (Doppler shift of the RF and
transponder-reflected ranging signals in the RF) and time transfer techniques to determine the location and
velocity of a spacecraft within the solar system. Networking in space today is point-to-point among ground
terminals and spacecraft, requiring most communication paths to/from space to be scheduled such that
communications is available only on an operational plan and is not easily adapted to handle
multidirectional communications under dynamic conditions.
Bahsin and Hayden [2008] begins with a brief history of the communications, navigation, and
networks of the 1960s and 1970s in use by the first system of systems, the NASA Apollo missions; it is
followed by short discussions of the communication and navigation networks and architectures that the
DoD and NASA employed from the 1980s onwards. Next is a synopsis of the emerging space system of
systems that will require complex communication and navigation networks to meet their needs.
Architecture approaches and processes being developed for communication and navigation networks in
emerging space system and systems are also described. Several examples are given of the products
generated in using the architecture development process for space exploration systems. The architecture
addresses the capabilities to enable voice, video, and data interoperability needed among the explorers
during exploration, while in habitat, and with Earth operations. Advanced technologies are then described
that will allow space system of systems to operate autonomously or semi-autonomously.
National Security – Perhaps one of the most talked-about application areas of SoSE is national security.
After many years of discussion the goals, merits and attributes of SoS, very few tangible results or solutions
21
have appeared in this or other areas of this technology. It is commonly believed that, “Systems Engineering
tools, methods, and processes are becoming inadequate to perform the tasks needed to realize the systems of
systems envisioned for future human endeavors. This is especially becoming evident in evolving national
security capabilities realizations for large-scale, complex space and terrestrial military endeavors.
Therefore the development of Systems of Systems Engineering tools, methods and processes is imperative
to enable the realization of future national security capabilities,” [Walker 2007]. In most SoSE applications,
heterogeneous systems (or communities) are brought together to cooperate for a common good and
enhanced robustness and performance. “These communities range in focus from architectures, to lasers, to
complex systems, and will eventually cover each area involved in aerospace related national security
endeavors. These communities are not developed in isolation in that cross-community interactions on
terminology, methods, and processes are done,” [Walker 2007]. The key is to have these communities
work together to guarantee the common goal of making our world a safer place for all.
Electric Power Systems Grids as SoS – Hiskens and Korba [2008] in Jamshidi [2008] provide an
overview of the systems of systems that are fundamental to the operation and control of electrical power
systems. Perspectives are drawn from industry and academia, and reflect theoretical and practical
challenges that are facing power systems in an era of energy markets and increasing utilization of
renewable energy resources (see also Duffy, et al. [2008]). Power systems cover extensive geographical
regions, and are composed of many diverse components. Accordingly, power systems are large-scale,
complex, dynamical systems that must operate reliably to supply electrical energy to customers. Stable
operation is achieved through extensive monitoring systems, and a hierarchy of controls, that together seek
to ensure total generation matches consumption, and that voltages remain at acceptable levels. Safety
margins play an important role in ensuring reliability, but tend to incur economic penalties. Significant
effort is therefore being devoted to the development of demanding control and supervision strategies that
enable reduction of these safety margins, with consequent improvements in transfer limits and
profitability.
SoS Approach for Renewable Energy – Duffy, et. al. [2008] in Jamshidi [2008] have detailed the SoS
approach to sustainable supply of energy. They note that over one-half of the petroleum consumed in the
United States is imported, and that percentage is expected to rise to 60% by 2025. America’s
transportation system of systems relies almost exclusively on refined petroleum products, accounting for
over two-thirds of the oil used. Each day, over eight million barrels of oil
are required to fuel over 225
million vehicles
that constitute the U.S. light-duty transportation fleet. The gap between U.S. oil
production and transportation oil needs is projected to grow, and the increase in the number of light-duty
vehicles will account for most of that growth. On a global scale, petroleum supplies will be in increasingly
higher demand as highly-populated developing countries expand their economies and become more
energy intensive. Clean forms of energy are needed to support sustainable global economic growth while
mitigating impacts on air quality and the potential effects of greenhouse gas emissions. USA’s growing
dependence on foreign sources of energy threatens her national security. As a nation, the authors assert
that, we must work to reduce our dependence on foreign sources of energy in a manner that is affordable
and preserves environmental quality.
Sustainable Environmental Management from a System of Systems Engineering Perspective –
Hipel, et al. [2008] in Jamshidi [2008] have provided a rich range of decision tools from the field of SE are
described for addressing complex environmental SoS problems in order to obtain sustainable, fair and
responsible solutions to satisfy as much as possible the value systems of stakeholders, including the
natural environment and future generations who are not even present at the bargaining table. To better
22
understand the environmental problem being investigated and thereby eventually reach more informed
decisions, the insightful paradigm of a system of systems can be readily utilized. For example, when
developing solutions to global warming problems, one can envision how societal systems, such as
agricultural and industrial systems, interact with the atmospheric system of systems, especially at the
tropospheric level. The great import of developing a comprehensive toolbox of decision methodologies
and techniques is emphasized by pointing out many current pressing environmental issues, such as global
warming and its potential adverse affects, and the widespread pollution of our land, water and air systems
of systems. To tackle these large-scale complex systems of systems problems, systems engineering
decision techniques that can take into account multiple stakeholders having multiple objectives are
explained according to their design and capabilities. To illustrate how systems decision tools can be
employed in practice to assist in reaching better decisions for benefiting society, different decision tools
are applied to three real-world systems of systems environmental problems. Specifically, the Graph Model
for Conflict Resolution is applied to the international dispute over the utilization of water in the Aral Sea
Basin; a large-scale optimization model founded upon concepts from cooperative game theory, economics
and hydrology is utilized for systematically investigating the fair allocation of scarce water resources
among multiple users in the South Saskatchewan River Basin in Western Canada; and multiple criteria
decision analysis methods are used to evaluate and compare solutions to handling fluctuating water levels
in the five Great Lakes located along the border of Canada and the USA [Wang, et. al. 2007].
Transportation Systems – The National Transportation System (NTS) can be viewed as a collection of
layered networks composed by heterogeneous systems for which the Air Transportation System (ATS) and
its National Airspace System (NAS) is one part. At present, research on each sector of the NTS is generally
conducted independently, with infrequent and/or incomplete consideration of scope dimensions (e.g.
multi-modal impacts and policy, societal, and business enterprise influences) and network interactions (e.g.
layered dynamics within a scope category). This isolated treatment does not capture the higher level
interactions seen at the NTS or ATS architecture level; thus, modifying the transportation system based on
limited observations and analyses may not necessarily have the intended effect or impact. A systematic
method for modeling these interactions with a system-of-systems (SoS) approach is essential to the
formation of a more complete model and understanding of the ATS, which would ultimately lead to better
outcomes from high-consequence decisions in technological, socio-economic, operational and political
policy-making context [De Laurentis 2005]. This is especially vital as decision-makers in both the public
and private sector, for example at the inter-agency Joint Planning and Development Office (JPDO) which is
charged with transformation of air transportation, are facing problems of increasing complexity and
uncertainty in attempting to encourage the evolution of superior transportation architectures [De Laurentis
and Callaway 2006].
Keating [2008] in Jamshidi [2008] has also provided insight into this important aspect of SoS. Emergent
behavior of a SoS resembles the slow down of the traffic going through a tunnel, even in the absence of any
lights, obstacles or accident. A tunnel, automobiles and the highway, as systems of a SoS, have an
emergent behavior or property in slowing down [Morley, 2006]. Fisher [2006] has noted that a SoS can not
achieve its goals depends on its emergent behaviors. The author explores “interdependencies among
systems, emergence, and interoperation” and develops maxim-like findings such as these: 1. Because they
cannot control one another, autonomous entities can achieve goals that are not local to themselves only by
increasing their influence through cooperative interactions with others. 2. Emergent composition is often
poorly understood and sometimes misunderstood because it has few analogies in traditional systems
engineering. 3. Even in the absence of accidents, tight coupling can ensure that a system of systems is
unable to satisfy its objectives. 4. If it is to remain scalable and affordable no matter how large it may
23
become, a system’s cost per constituent must grow less linearly with its size. 5. Delay is a critical aspect of
systems of systems.
1.5 CONCLUSIONS
This chapter is written to serve as an introduction to the book. We also gave an up-to-date state of the art in
systems of systems and systems of systems engineering based on other works of the author. The subject
matter of this book is an unsettled topic in engineering in general and in systems engineering in particular.
Attempt has been made to cover as many open questions in both theory and applications of SoS and SoSE.
It is our intention that this book would be the beginning of much debate and challenges among and by the
readers of this book. The book is equally intended to benefit industry, academia or government. A sister
volume, by the author, on the subject is under press at the present time and can give readers further insight
into SoS [Jamshidi 2008].
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SystemS of Systems: A ConTROL THEORETIC VIEW


SystemS of Systems: A ConTROL THEORETIC VIEW

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AbstractThis paper considers the notion of SoS as an evolution of the standard notion of systems, provides a clear distinction to the standard notion of composite systems and aims to provide an abstract and generic definition that is detached from the particular domain as well as a classification of the families of SoS. We present a new abstract definition of the notion of System of Systems as an evolution of the notion of Composite Systems, empowered by the concept of autonomy and participation in tasks usually linked to games. Control theoretic concepts and methodologies are used to provide the characterization of the notion of “systems play” that is used as the evolution of the notion of the interconnection topology. In this set up the subsystems in SoS act as autonomous intelligent agents in a multi-agent system that is defined by a central task and possibly a game.

 

     I.          Introduction

      The concept of “System of Systems” has emerged in many and diverse fields of applications and describes the integration of many independent, autonomous systems, frequently of large dimensions, which are brought together in order to satisfy a global goal and under certain rules of engagement. These complex multi-systems exhibit features well beyond the standard notion of system composition represent a synthesis of systems which themselves have a degree of autonomy, but this composition is subject to a central task and related rules. The term has been linked to problems of complex nature, but so far it has been used in a very loose way, by different communities with no special effort to give it a precise definition and link it to the rigorous methodologies concepts and tools of the Mathematical System Theory. Establishing the links with the traditional approaches is essential, if we are to transfer and appropriately develop powerful and established analytical tools to a field that is still unstructured and where little progress has been made in developing a generic and unifying methodology.

The main objective is to place the concept of “Systems of Systems” within the standard framework of Systems Theory that is suitable for some subsequent further formal development (mathematical formulation). Such systems emerge in different and diverse domains and their classification, is also crucial, since different domains may require alternative modeling tools. A central part of our effort is to explain the difference of SoS from that of Composite Systems which leads to the generalization of the standard notion of interconnection topology (linked to composite systems) to the new notion of “systems play”. We introduce the notion of the integrated system, as a system with intelligence and explain the context of the new notion of “systems play” which provides the global compositions leading to what we refer as SoS.  The description of the systems play then emerges as a central task or game and ways this may be characterized is defined.

   II.         The Notion of a System

The development of a systems framework for general systems is not a new activity [1, 2, 30]. However, such developments have been influenced predominantly by the standard engineering paradigm and as a result they failed to cope with new paradigms such as those of the business processes, data systems, biological systems, and emerging complex systems paradigms. Our task here is to reconsider existing concepts and notions from the general Systems area [1], detach them from the influences of specific paradigms, generalise them appropriately to make them relevant for the new challenges and then use them to define the notion of “System of Systems”. We follow a conceptual systems approach that may lead to formal notions as described in [2]. Our work relies on existing methodologies, but aims at redefining notions, concepts and introduce new ones reflecting the needs of the new paradigms.

 

    Definition (2.1): A system is an interconnection, organisation of objects which are embedded in a given environment.

 

This definition is general and uses as fundamental elements the primitive notions of: objects, connectivities – relations (topology), and environment and it is suitable for the study of “soft”, and “hard” systems. The concept of a system refers to the level of reality (physical or manmade construction) and this is an essential observation, to distinguish it from the notion of system model, which referrers to the sphere of abstraction. An object is a general unit (abstract, or physical) defined in terms of its attributes and the possible relations between them. For a given object, we define its environment as the set of objects, signals, events, structures, which are considered topologically external to the object, and are linked to the object in terms of a structure, relations between their attributes. The existence of the objects environment implies crossings of the imaginary boundary and such crossings indicate the connectivities of the object to objects in its environment. The set of objects in a system are related between themselves and to the system environment through relationships referred to as interconnection topologies. The internal linking between the objects of the system defines the internal interconnection structure, whereas that part expressing the links of the objects to the system’s environment will be called external interconnection topology. The internal and external interconnection topology structure may be fixed or evolving and their nature gives to the system a specific character and identity. The nature of the external interconnection topology is crucial in defining the embedding of the system in its environment and it is the central notion in characterising the difference between composite systems and system of systems. If denotes the interconnection topology, a the system aggregate (collection of objects) and by * the action of ona we may represent the system as

 

 

 

 

 

 

 

 

 

 

 

Figure 1: Description of an embedded system

 

An aggregate of systems leads to the creation of new forms of systems which may be either described within the framework of composite systems, or demonstrate additional features which add complexity to the description and may be referred to as system of systems. The term system of systems (SoS) has been used in the literature in different ways [7], [8]. Most definitions ([7], [9], [10]) describe features or properties of complex systems linked to specific examples. The class of systems exhibiting behaviour of Systems of Systems typically exhibit aspects of the behaviour met in complex systems; however, not all complex problems fall in the realm of systems of systems. Problem areas characterized as System of systems exhibit features such as [8]: Operational Independence of Elements; Managerial Independence of Elements; Evolutionary Development; Emergent Behaviour; Geographical Distribution of Elements; Inter-disciplinary Study; Heterogeneity of Systems; etc. The definitions that have been given so far [10], contain elements of what the abstract notion should have, but they are more linked to specific features and are linked to areas of applications. A literature survey and discussions on these definitions are given in [8], [9]. A more generic definition that captures the key features and which is a good basis for further development is given below [8]:

   Definition (2.2): (i) Systems of systems are large-scale integrated systems which are heterogeneous and independently operable on their own, but are networked together for a common goal. The goal, as mentioned before, may be cost, performance, robustness, etc.

(ii) A System of Systems is a “super system” comprised of other elements which themselves are independent complex operational systems and interact among themselves to achieve a common goal. Each element of a SoS achieves well-substantiated goals even if they are detached from the SoS.

The above definitions are descriptive and they capture crucial features of what the notion should involve; however, they do not answer the question, why the new notion different than that of composite systems. The distinctive feature of our approach is that we treat the notion of System of Systems (SoS) as an evolution of the standard notion in engineering of Composite Systems (CoS) [13]. Making the transition from CoS to SoS requires to identify the commonalities and differences between the two notions. We note:

 

  • Both CoS and SoS are compositions of simpler objects, or systems.
  • Both CoS and SoS are embedded in the environment of a larger system.
  • The objects, or sub-systems in CoS do not have their independent goal, they are not autonomous and their behaviour is subject to the rules of the interconnection topology.
  • The interconnection rule in CoS is expressed as a graph topology.
  • The subsystems in SoS may have their own goals and some of them may be autonomous, semi-autonomous, or organised as autonomous groupings of composite systems.
  • There may be a connection rule expressed as a graph topology for the information structures of the subsystems .
  • The SoS has associated with it a global operational task where every subsystem enters as an agent with their individual Operational Set, Goals.

  III.       A New Characterisation  for the System of Systems

Developing a generic definition for SoS that transcends specific domains of applications is essential for the development of systems engineering framework [14]. In the system representation of Figure (1) [2], the system appears as an autonomous agent (internal system structure together with its inputs and outputs), having its operational instructions and goals and a pair of information vectors expressed by the input and output influences vectors. Additional properties may be

 

introduced by assuming that the system under consideration has the control, modelling and supervisory capabilities integrated within it which enable the system to act as an agent with independence capabilities and act as a player in games. We may represent such systems as illustrated in Figure (2).  Such a form of the system will referred to as an integrated system. The latter term is used to distinguish it from systems which have no integrated control and information processing capabilities and which may be referred to as simple systems. If such a system is embedded in a larger system (Composite, or System of Systems) relations with other systems may be defined in two different ways:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure (2): Representation of an Integrated System

 

  • An interconnection topology of the graph type defined on the set of input-, output- influences subsystem information structures.
  • A global game where every subsystem enters as an agent with their individual Operational Set, Goals.

The distinguishing feature of the SoS is that the subsystems participate in the composition as intelligent agents with a relative autonomy and may act as players in a game. The latter property requires that the systems entering the composition are of the integrated type, since this requires capabilities for control, estimation, modelling and supervisory capabilities. Features, such as large dimensionality, heterogeneity, network structure, Operational, Adaptability, Emergent Behavior etc may be also present in the case of CoS as well. We define:

Definition (3.1): Consider a set of systems Σ = {i, i=1,2,…,μ} and let  be an interconnection rule defined on the information structures of i systems. The action of  on Σ, called a Composite System, or the composition of Σ under.

The information structure of each system is defined by the pair of the input and output influence vectors and the interconnection rule may be represented by a graph topology [2], [11]. The resulted system is embedded in a larger system and it is treated as new system with its own system boundary. In the above definition the systems considered are simple and not necessarily integrated. This definition may now be extended as follows:

Definition (3.2): Consider a set of integrated systems Σ = {i, i=1,2,…,μ},  be an interconnection rule defined on the information structures of i systems and let c = Σ* be the resulting composite system. If  is a general rule of operations, referred to as “systems play” that is defined on the systems i then the action of  on c is a new system c* = Σ* ●  which will be called a System of Systems, or the,  composition of Σ.

In the above definition the notion of SoS emerges as an evolution of CoS since the systems are assumed to be integrated, ie having capabilities for information processing and thus they are capable to act as agents and participate in games of some type. We assume an interconnection topology defined on the information structures of the components, but this may not necessarily be strong and some sub-systems may be entirely autonomous. Note that the transition from the CoS to SoS involves moving from simple to integrated systems as far as the subsystems, and the introduction of the new notion of “systems play” which emerges as a generalization of the notion of topological composition. The nature of the applications defines the systems play, which frequently may be expressed as a game defined on intelligent agents. If not all subsystems are integrated we may define:

Definition (3.3): Consider a set of systems Σ = {i, i=1,2,…,ρ; ’i, i=1,2,…,σ}, where the i, i=1,2,…,ρ} subset is integrated and the {’i, i=1,2,…,σ} subset is simple. We consider  to be an interconnection rule defined on the information structures of sub-systems of Σ and let c = Σ*  be the resulting composite system. If  is a systems play that is defined on the integrated systems i then the action of  on c is a new system ’’c = Σ* ●  which will be called a Weak System of Systems, or the weak , composition of Σ.

The essence of the new definition is that SoS emerges as a two dimensional notion. At the lower level it appears as a composite system with some interconnection topology defined on the subsystems, which are now assumed to possess information processing capabilities. It is the latter property that allows these subsystems to act as agents and SoS to emerge as a multi-agent system (MAS) composed of multiple interacting intelligent agents (the subsystems). This multi-agent systems view allows SoS to act as vehicle to solve problems which are difficult or impossible for an individual agent. The multi-agent dimension of SoS has important characteristics such as [16]:

  • Autonomy: the agents are at least partially autonomous
  • Local views: no agent has a full global view of the system, or the system is too complex for an agent to make practical use of such knowledge
  • Decentralization: there is no designated single controlling agent, but decision and information gathering is distributed.

It is these properties that allow SoS to develop “self-organization” capabilities and find the best solution to the problems defined on them.

  IV.       Classification of  SoS

The major challenge in the development of a unifying approach to the study of SoS is the quantitative characterisation of the new notion of the systems play. Taking into account that SoS problems emerge in many and diverse domains, it is clear that some classification of the general SoS family into sub-families with common characteristics is essential before we embark to the characterisation of notions such as systems play and subsequently address issues of design, re-design and then study of emergence for such systems. The classification of SoS may be achieved according to different criteria such as the origin:

(i) Physical, or natural SoS (N-SoS)

(ii) Engineered or Constructed SoS (E-SoS)

The first category involves problems of the natural world, and social-economic problems and are the results of evolution of physical, or socio-economic processes. Problems such as the “ecosystem” of a geographical region, and issues such as “social plenomena” are typical examples. The common characteristic of these classes is that they are the results of a “natural evolution” and they are not the by-products of some notion of design. Of course, there are grey areas between the two classes such is the case “global economy” where evolution is accompanied by some effort to intervene and affect the economic processes (government policies etc). It is important to note that in N-SoS some “goals”, “principles” drive the development of the system play, whereas in E-SoS the “goal” is driven some coordination effort. This leads to another way of classifying SoS based on structural and operational characteristics. This classification refers to the mechanisms defining the relations between the subsystems. We may distinguish the following distinct classes:

(a) Goal Driven and Unstructured (GU-SoS)

(b) Goal Driven with Central Coordination (GC-SoS)

In GU-SoS class the central goal for the system operation is set, as well as the environment within which the system operations will take place. In this case the nature of the system play is entirely defined by the set goal. In such cases the goal may define a form of a game where the intelligent agents may participate. Typical examples are problems related to “eco-systems”, where there is no coordinated human interference. A further classification for this class is into:

  • Pure Goal Driven (P-GU-SoS)
  • Goal and Scenario Driven (S-GU-SoS)

In the P-GU-SoS class the subsystems, as intelligent agents, interpret the central goal, may assign to themselves sug-goals and they then develop actions and self-organisation to achieve the central goal, which may be expressed as optimization of a performance index, subject to satisfaction of their individual goals as well. In S-GU-SoS a scenario linked to the goal is given, the subsystems as intelligent agents undertake roles which aim to optimize a central performance index and satisfy their own particular goals. Clearly, in all such cases appropriate games have to be defined.

The GC-SoS class on the other hand has the same features as the P-GU-SoS and similar subclasses with the additional feature the existence of coordination. The presence of coordination imposes a structure to the interpretation of the goal by the subsystem and the development of appropriate scenarios to achieve the central goal and partial goal. Coordination is common to E-SoS and may be viewed as an interpreter for the development of operational activities. The nature of coordination also introduces special features to SoS characterization since it introduces a structure to the resulted systems play. Coordination is a form of organization and there may be different types such as “Hierarchical”, “Heterarchical” and “Holonic” [19]. Such forms of organization structure the systems play and the development of scenarios. Note that in N-SoS self-organisation has evolved and there is no coordination; the evolved structure may look like an optimal scenario and acts as a natural substitute for the coordination. Man-made systems usually involve coordination which drives the development of the system play. These classes define sub-families of SoS; further classification may be introduced by the nature of the origins of the overall SoS. Types of SoS where the subsystems are of the engineering type without human action involvement are referred to as “hard”. Systems involving human presence and behaviour will be referred to as “soft” and those involving  a mixture of the two types will be called “hybrid”.

   V.         Methodologies for Systems Play

The system-wide coordination of real-world systems of systems is a challenging and open problem. The development of a description for the systems play depends on the nature of the particular SoS. In the following we outline different methodologies may provide the required framework for such task. In the following, we will investigate several methods that have emerged in different domains to manage systems of systems which involve: Co-Operative Control, market based coordination techniques, population control methodologies, and coalition games. Each of these methodologies provide formal descriptions of the notion of systems play.

 

  1. Co-Operative Control

The notion of Co-Operative Control has been used in a number of ways in the literature. A typical case describing a class of SoS very close to technological problems is the Vehicle Formation Problem  [17],[18] defined as the control of the formation of  k vehicles that are performing a shared task; the task depends on the relationship between the locations of the individual vehicles and the task defines the scenario that has to be realized. It is assumed that the vehicles are able to communicate with the other vehicles in carrying out the task and they have capabilities to control their position in the effort to perform the task. Each vehicle is described as a rigid body moving in space and a state vector xi may be associated with each one; by x = (x1,.., xN) we may represent the complete state for the set of N vehicles. The collection of all individual states defines the state of the system and the execution of the assigned task requires the assignment of additional states that can make the system an SoS. The development of the scenario, task is handled by introducing  for each vehicle an additional discrete state, αi, is introduced which defines the role of the vehicle in the task and this is  represented as an element of a discrete set  . The definition of  depends on the specific cooperative control problem.

It is assumed that the vehicles are able to communicate with some set of other vehicles and the set of possible communication channels is represented by a graph G. The nodes of the graph represent the individual vehicles and a directed edge between two nodes represents the ability of a vehicle to receive information from another vehicle. Given a collection of vehicles with state x and roles α, we may define a task or scenario in terms of a performance function J the optimization of which is equivalent to the completion of the task. Clearly, such problems may also have constraints which make the problem a constrained optimization problem. The execution of the scenario requires a strategy and for this case this expressed as an assignment of the inputs ui for each vehicle and a selection of the roles of the vehicles. For SoS the problems of interest are those involving cooperative tasks that can be solved using a decentralized strategy.

 

  1. Market-Economics Based Coordination Techniques

The distinguishing feature of SoS is that there are autonomous units with their own management and control functions that are coupled by resource flows which need to be balanced, over appropriate periods of time depending local or global storage capacities. The performance of the subsystem consumption and production is influenced by availability of these resources [27]. To perform an arbitration of these flows requires economic balancing mechanisms [20], [21]. The management of the resource flows may be expressed as a network management problem, given that the resource flows define some generic network structure within which we define the flows. Clearly, the overall system performance and behaviour is influenced by discrete decisions taken. Two different approaches  that can be used for the management of such flow-coupled SoS are: economics-driven coordination and market-based mechanisms. In both cases, the coordinator has only limited information about the behaviour and the constraints of the local units which perform a local optimization of their operational policies.

In the economics-driven coordination, it is assumed that the control of SoS involves the setting of production/consumption constraints or references between the global SoS coordinator and the controllers of individual systems. The SoS coordinator utilizes simplified models of the sub-systems, and a model of the connecting networks to compute references or constraints on the exchanged flows. The resulting optimization is based on the dynamic price profiles for the resources that are consumed or produced by the subsystems over the planning horizon. An alternative approach is to use mechanisms employing the concepts of economic markets to distribute limited resources between subsystems. The market is defined as a population of agents consisting of producers selling goods and consumers buying these goods [20], where the consumers’ demand depends on the usefulness or utility of a good for the completion of its task. The prices of the resources which are set by the market affect the utility and, thus, the demand side. The goal of a market-based coordination mechanism is to generate equilibrium between the producers and the consumers such that the overall supply equals the overall demand. A popular mechanism to compute such equilibria is an auction and many different kinds of auction mechanisms have been developed [20]. Market-based mechanisms are inherently decentralized and can thus be mapped directly to systems with autonomous subsystems.

 

  1. Population control methods

Population control refers to systems that comprise a large number of semi-independent subsystems, which macroscopically are viewed in terms of their emergent behaviour. Such systems are used in ecology to capture the fluctuations in the populations of interacting species and the relevant models use continuous variables to capture populations and differential equations to capture their evolution. There are extensions to hybrid models [23] and to delay and/or stochastic differential equation models. Of special interest is the class of mixed-effect models [22], which address the evolution of a heterogeneous population of individuals, which deploy ordinary differential equations, but with parameters linked to appropriate probability distributions. Population systems dynamics are gaining in importance, as man-made systems become increasingly complex and larger-scale and control of the emergent behaviour of large collections of semi-autonomous subsystems becomes an issue. Such methods are primarily motivated by biological applications, but have potential for the engineering field of SoS. These methods need to be adapted and extended, if they are to be made applicable to engineered SoS.

 

  1. Coalition Games

The basic idea of SoS is to consider the overall system as a set of subsystems that are controlled by local controllers or agents which may exchange information and cooperate. This feature demonstrates the link of SoS to distributed and decentralized control schemes with the additional property that the interaction between the subsystems may indicate a time-varying coupling. It is this special feature that indicates the links to a rather new category of management and control schemes referred to as coalitional management schemes [24]. In this paradigm different agents cooperate when there is enough interaction between the controlled systems and they work in a decentralized fashion when there is little interaction. A coalition is a temporary alliance or partnering of groups in order to achieve a common purpose or to engage in a joint activity [26]. A coalition of systems is a temporary system of systems built to achieve a common objective. Coalition building is the process by which parties come together to form a coalition. Forming coalitions requires that the groups have similar values, interests, and goals which may allow members to combine their resources and become more powerful than when they each acted alone.

 

  VI.       Conclusion

The new definition for the SoS is the starting point for the development of methodology that may lead to systematic design. Examining the rules of composition of the subsystems and their coordination as agents in a larger system defines a challenging new area for research and requires links across many disciplines. Examining in detail the special features of the different classes of SoS is crucial in the effort to provide a quantitative formulation of the notion of “systems play” which may take different forms in the different classes. This is also crucial in quantifying the notion of emergence in the SoS context. The potential for applications is well beyond the traditional engineering field, when powerful modeling tools are defined that may allow the study of design and decision problems of the respective classes of SoS. It is worth mentioning at this point that the majority of  SoS are products of “physical”, or “technological “ evolution, rather than products of systematic design and understanding evolutionary processes leading to the formation of SoS is crucial.

VII.      References

 

[1] M.D. Mesarovic and Y. Takahara, 1974 , “General Systems Theory: Mathematical Foundations” Academic Press, New York.

[2] N. Karcanias, 2004. “System concepts for general processes: specification of a new framework”, Systems and Control Centre Res. Rep., Nov. 2004.

[3] G.J.Klir, 1972, Trends in general systems theory, New York, Wiley-Interscience.

[4] Maier, M.W., “Architecting Principles for System of Systems,” Systems Engineering, Vol. 1, No. 4, 1998, pp. 267-284.

[5] N. Karcanias, 1994, Global Process Instrumentation:  Issues and Problems of a Systems and Control Theory Framework, Measurement, Vol. 14, pp 103-113.

[6] N. Karcanias, 1995, Integrated Process Design: A Generic Control Theory/Design Based Framework, Comp. in Industry, Vol. 26, pp.291-301.

[7] DeLaurentis D., “System of Systems Definition and Vocabulary,” School of Aeronautics and Astronautics, Purdue Univ., West Lafayette, IN, 2007.

[8] M. Jamshidi, 2008 “System of Systems Engineering –Innovations for the 21st Century” Wiley Ser. in Systems Engin., 2008 John Wiley & Sons, Inc.

[9] Luskasik, S.J., “Systems, Systems of Systems, and the Education of Engineers,” Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, Vol. 12, No. 1, pp. 11-60, 1998.

[10] Maier, M.W., “Architecting Principles for System of Systems,” Systems Engineering, Vol. 1, No. 4, 1998, pp. 267-284.

[11] N. Karcanias ,2008, “Structure evolving systems and control in integrated design”, Annual Reviews in Control 32 (2008) 161–182

[12] M. Wooldridge, 2002, “An Introduction to MultiAgent Systems”, John Wiley & Sons Ltd, ISBN 0-471-49691-X

[13] N. Karcanias and A.G. Hessami, “Complexity and the notion of Systems of Systems: Part (I) General Systems and Complexity,” Proc. of the 2010 World Automation Congress International Symp. on Intelligent Automation and Control (ISIAC) 19-23 September 2010, Kobe Japan.

[14] N. Karcanias and A.G. Hessami, “Complexity and the notion of Systems of Systems: Part (II) Defining the notion of Systems of Systems,” Proc. of the 2010 World Automation Congress International Symp. on Intelligent Automation and Control (ISIAC) 19-23 September 2010, Kobe Japan.

[15] E. Alonso, N. Karcanias and A. G. Hessami, “Symmetries, groups and groupoids for Systems of Systems,” Proceedings of the 7th IEEE International Systems Conference (SysCon 2013), Piscataway, NJ: IEEE Press. Orlando, FL, 15-18 April, 2013.

[16] E. Alonso, “Actions and Agents,” K. Frankish and W. Ramsey (eds.), The Cambridge Handbook of Artificial Intelligence, Chapter 5. Cambridge, England: Cambridge University Press. 2013.

[17] R.M. Murray, 2007, “Recent Research in Cooperative Control of Multi-Vehicle Systems”, 2007 International Conference on Advances in Control and Optimization of Dynamical Systems.

[18] W. B. Dunbar, R. M. Murray. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 42(4):549–558, 2006.

[19] H. Van Brussel, L. Bongaorts, J. Wyns, P.l Valckenaers, and T. Van Ginderachter,. “Trends and Perspectives: A Conceptual Framework for Holonic Manufacturing: Identification of Manufacturing Holons” Journal of Manufacturing Systems , 18, pp35-52. 1999.

[20] S. de Vries and R.V. Vohra : Combinatorial auctions: A survey.

    INFORMS Journal on Computing 15(3), pp. 284-309, 2003.

[21] H. Voos: Resource allocation in continuous production using        market-based multi-agent systems. Proc. 5th International     Conference on Industrial Informatics, pp. 1085-1090, 2007.

[22] A. Milias-Argeitis, S. Summers, J. Stewart-Ornstein, I. Zuleta, D.Pincus, H. El-Samad, M. Khammash, and J. Lygeros: In silico feedback for in vivo regulation of a gene expression circuit.  Nature Biotechnology 29, pp. 1114–1116, 2011.

[23] C. Cassandras and J. Lygeros (Eds.): Stochastic Hybrid Systems, No. 24 in Control Engineering, Boca Raton: CRC Press, 2006.

[24] M. Nourian, P. Caines, R. Malhame, M. Huang: Nash, Social, and Centralized Solutions to Consensus Problems via Mean Field Control Theory. IEEE Transactions on Automatic Control, 2012.

[25] J.P. Aubin: Advances in dynamic games. Annals of the International Society of Dynamic Games 7, pp. 129–162, 2005.

[26] W. Saad, Z. Han, M. Debbah, A. Hjorungnes, and T. Basar: Coalitional game theory for communication networks. IEEE Signal Processing Magazine, Special Issue on Game Theory 26(5), pp. 77–97, 2009.

[27] R.S. Pindyck and D.L. Rubinfeld: Microeconomics. Prentice Hall, 7th edition, 2009.

 

IEEE SMC 2013 Conference (SMC2013:S01), October 13-16, 2013,  Manchester

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Business Studies


Business Studies

Section one: True or false – questions 

1          The Performance Excellence Award criteria are based on components of the Malcolm Baldrige National Quality Award, and are geared toward China’s unique business environment.

True

False

  1. ISO typically focuses on measuring product quality and driving process improvement and cost savings throughout the organization.

True

False

3          ISO 9000:2000 is a comprehensive business performance framework.

True

False

4          Six Sigma initiatives fulfill in part many of the elements of ISO 9000:2000.

True

False

5          The competitive strategy level is where organizational strategy and Six Sigma must align, because it is at this level that the tools of Six Sigma can be most effectively applied.

True

False

6          Six Sigma can have a significant impact on the cost of quality because of its focus on financial return.

True

False

7          Six Sigma is not a substitute for continuous improvement.

True

False

8          The criteria for performance excellence consist of a hierarchical set of categories, items, and areas to address.

True

False

9          Six Sigma is focused on improvement with little financial accountability whereas TQM requires a verifiable return on investment and focus on the bottom line.

True

False

10        Six Sigma is owned by business leader champions.

True

False

11        Six Sigma methods are most applicable to conformance problems because the processes that create the problems can be easily identified, measured, analyzed, and changed.

True

False

12        Both Six Sigma and lean approaches are driven by customer requirements, focus on real dollar savings, have the ability to make significant financial impacts on the organization, and can easily be used in non-manufacturing environments.

True

False

13        A process is a sequence of linked activities that is intended to achieve some result, such as producing a good or service for a customer within or outside the organization.

True

False

14        Assembly of products in a manufacturing plant is an example of a support process.

True

False

15        Flowcharts enable management to study and analyze processes prior to implementation.

True

False

16        Process control is the responsibility of those who directly accomplish the work.

True

False

17        Control is the activity of ensuring conformance to the requirements and taking corrective action when necessary to correct problems and maintain stable performance.

True

False

18        Control in manufacturing starts with purchasing and receiving processes.

True

False

 

19        Benchmarking encourages employees to continuously innovate.

True

False

20        Reengineering focuses on improving the existing procedures rather than eliminating them and reinventing the process.

True

False

21        Statistics is a science concerned with the collection, organization, analysis, interpretation, and presentation of data.

True

False

22        Innovation is built upon strong research and development (R&D) processes.

True

False

 

Section two: Multiple Choice questions

 

  1. The _____ is a simple adaptation of the scientific method for process improvement.
a. Juran quality trilogy
b. Taguchi loss function
c. Deming cycle
d. quincunx experiment

 

  1. The term _____ is based on a statistical measure that equates to 3.4 or fewer errors or defects per million opportunities.
a. Pareto analysis
b. Six Sigma
c. Quincunx
d. Quality trilogy

 

  1. Which of the following is the biggest cost in any customer service turnover model?
  2. Lost productivity costs
  3. Separation processing costs
  4. Replacement hiring costs

 

  1. New hire training costs

 

  1. Which of the following is one of the three major activities in process management that focuses on maintaining consistency in output by assessing performance and taking corrective action when necessary?
a. Design
b. Improvement
c. Process mapping
d. Control

 

  1. Value-creation processes are sometimes called _____ processes.
a. core
b. support
c. job enrichment
d. quality circle

 

  1. Value-creation processes differ from support processes in that value-creation processes:
a. provide the infrastructure for production or deliver processes to create or deliver the actual product.
b. rarely align with the organization’s core competencies and strategic objectives.
c. generally do not add value directly to the product or service.
d. are driven by external customer needs.

 

7          In service applications, the term _____ is generally used to describe a nonconformance.

a. error
b. unit
c. cycle
d. trend

 

8          A(n) _____ characterizes the presence or absence of nonconformances in a unit of work.

a. variable
b. indicator
c. attribute 
d. error

 

9          Variable data are _____.

a. discrete
b. not measurable
c. nonrandom
d. continuous

 

10        In pre-control, which zone covers the nominal specification of a process?

a. Gray zone
b. Yellow zone
c. Red zone
d. Green zone

 

11        Statistical process control relies on _____.

a. process capability studies
b. dashboards
c. control charts
d. metrology

 

12        Control limits are often confused with _____.

a. pre-control lines
b. center lines
c. specification limits
d. three sigma limits

Section three:

Study Case  

  1. What is meant by operational steps?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

 

Essay questions

 

 

  1. Describe how a Six Sigma project may lead to increase in customer satisfaction

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

 

 

 

 

 

 

  1. Explain how the Baldrige Criteria, ISO 9000:2000, and Six Sigma approach process management

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

  1. Describe the concepts on which the core philosophy of six sigma is based

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

  1. What are the 10 important considerations for data collection suggested by the Juran institute?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

  1. Differentiate between value-creation processes and support processes

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

  1. Describe the various elements of a control system

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

 

 

 

 

 

  1. Discuss the differences between attributes and variables data

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

  1. Construct a flow chart that illustrates the decision process for selecting the appropriate control chart(s) for monitoring a given process

 

………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                  Good Luck         

 

Project Deliverable 2: Business Requirements


Subject or discipline: IT, Web
Title: Project Deliverable 2: Business Requirements
Number of sources: 3
Provide digital sources used: No
Paper format: APA
# of pages: 8
Spacing: Double spaced
# of words: 2200
Paper details:
This assignment consists of two (2) sections: a business requirements document and a Gantt chart or project plan. You must submit both sections as separate files for the completion of this assignment. Label each file name according to the section of the assignment for which it is written. Additionally, you may create and / or assume all necessary assumptions needed for the completion of this assignment.

Procuring quality business requirements is an important step toward the design of quality information systems. Completion of a quality requirements document allows user needs and expectations to be captured so that infrastructure and information systems can be designed properly. Using the requirements document provided in the course shell, you are to speculate on the needs of the company. You must consider current and future requirements; however, assumptions should be realistic and carefully considered.

Section 1: Business Requirements Document

1. Write an eight to ten (8-10) page original business requirements document for the project plan using the template provided. Note: The template can be found in the Student Center of the online course shell.

a. Describe the project including the following:

i. Describe the scope and analyze how to control the scope.

ii. Identify possible risks, constraints, and assumptions.

iii. Describe the integration with other systems and infrastructure. Note: Database and interface design, security, and networking should be considered.

iv. Define relevant terms that will be used throughout project.

b. Use at least two (2) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.

Your assignment must follow these formatting requirements:

•Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
•Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.

Section 2: Revised Gantt Chart / Project Plan

Use Microsoft Project or an open source alternative, such as Open Project, to:

2. Update the Gantt chart or project plan (summary and detail) template, from Project Deliverable 1: Project Plan Inception, with all the project tasks.

The specific course learning outcomes associated with this assignment are:

•Apply integrative information technology solutions with project management tools to solve business problems.
•Use technology and information resources to research issues in information technology.
•Write clearly and concisely about strategic issues and practices in the information technology domain using proper writing mechanics and technical style conventions.

Milestone #1: Gun Control


Milestone #1: Gun Control

Section #3 – Group #3

INTRODUCTION

Gun control is a pressing issue in today’s society as more and more accidents are occurring. The regulation of gun ownership and safety has been a serious political and social debate. Owning guns has impacted our environment in a negative way and needs to be controlled before more accidental and illegal deaths occur. Our main focus in this study is questioning if the possessions of guns contributes to more deaths in our country. According to Tita, over 200,000 people die every year from homicides, suicides or misfortunes relating to small firearms (2).  This study will explain why guns are a result of endless deaths and will elaborate on the reasons why people use firearms to commit homicides and suicides. The purpose of this analysis is to document the importance of setting up gun control measures. It will highlight the factors propelling perpetrators to gain access to weapons. Also, it focuses on the possession of such weapons as the primary causation of deaths.

Problem Definition

Our hypothesis is that the possession of guns has significantly contributed to more deaths in our country. In 2012, Follman stated that America has over 300 million firearms possessed by individuals (2). The increase of these weapons seems to be growing faster than the country’s population. Increasing gun availability will have an increase in crime in today’s society. We believe that we are correct when saying this because with the right people have to owning guns will increase the chances of them getting into the hands of the wrong person. The law is not stringent on people purchasing guns and obtaining required license permits. Likewise, guns sold in black markets are increasing over the years. It is legal to purchase a firearm even without a permit or any legal consent available. Over eight states allow consent to people carrying firearms in their bars, hence, implying that an intoxicated person may use the weapon ‘‘in self-defense’’. Additionally, in states like Louisiana, permit holders can carry their pistols in places of worship. As a result, such laws encourage people including criminals and individuals of unstable minds to purchase and carry guns in several locations. According to Sanger- Katz, most suicides in America are a result from the ownership of guns. It is the second most common cause of fatalities in the United States with cases affecting people between the age of 15 and 34. Incidences like this could decrease if fewer people owned the deadly weapon. On the other hand, mass shootings in movie theaters and schools are recently becoming a norm. Most of the cases claim that the perpetrators were not of sound mind. However, had they not possessed a firearm it would not have resulted in such adverse effects.

VARIABLES

 

Variables Resources Measurement
Gun Availability http://swacj.org/swjcj/archives/6.3/4%20-%20Guns%20and%20Violent%20Crime.pdf This will help determine the reason gun ownership value increased fatalities (Follman 4).

 

 

 

Video Games http://videogames.procon.org/ Engagement in video games helps predict the probability of children copying the violent nature (Olson and Kutner).
Movies http://www.cnn.com/2015/08/05/us/movie-theater-shootings/ Storylines presented in movies focus on violence and shootings, also giving people a false representation of what the consequences in real life would be for these actions (Martinez).
Depression http://swacj.org/swjcj/archives/6.3/4%20-%20Guns%20and%20Violent%20Crime.pdf This will help predict the probability of gun owners using their weapons to kill while under depression and mental disorders (Leclerc and Wortley 18).
Family Disruptions http://swacj.org/swjcj/archives/6.3/4%20-%20Guns%20and%20Violent%20Crime.pdf Family disruptions can affect offending behaviors such as large family size, abuse and parental conflict. This can help identify the chances of a person committing a crime due to family issues. (Condry 70).
Parenting http://academicworks.cuny.edu/gc_etds/600/?utm_source=academicworks.cuny.edu%2Fgc_etds%2F600&utm_medium=PDF&utm_campaign=PDFCoverPages Poor child nurturing makes a child prone to a violent nature (Nemeth22).
Nightly activity

 

http://batten.virginia.edu/research/keep-kids-inside-juvenile-curfews-and-urban-gun-violence Some states in the United States instituted curfews to reduce night activities especially for the younger people (Carr and Doleac 13- 15).

 

Drugs http://www.huffingtonpost.com/emily-crockett/war-on-drugs-gun-violence_b_2624873.html Drug addicts are likely to engage in vicious acts including killing and are inclined to purchasing guns due to their environment (Crockett).
Involvement in Gangs http://www.latimes.com/nation/nationnow/la-na-nn-gun-violence-schools-20140610-story.html Being in gangs has a direct relation to gun violence (Tita 4).
Age Structure http://swacj.org/swjcj/archives/6.3/4%20-%20Guns%20and%20Violent%20Crime.pdf Most gun related cases involve youths between the age of 15 and 35 (Males).
Unemployment http://swacj.org/swjcj/archives/6.3/4%20-%20Guns%20and%20Violent%20Crime.pdf This is a predicting variable to determine susceptibility to gun violence (Greenfield).
Suicide http://www.catb.org/esr/guns/point-blank-summary.html Suicides that are caused by gun possession shows how they lead to deaths (Sanger-Katz).
Gun Violence in Schools https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/rdcng-gn-vlnc/index-en.aspx This is a measure to identify incidences of deaths caused by firearms (Vartabedian).

DATA

The data we will collect in this study will be through online research. The data will come from scholarly journals, books and research reports outlining previous studies. We will obtain the data by conducting through a desk research. It will be valid because it is recent information with most of the sources not exceeding five years. Furthermore, they are peer-reviewed articles and books. Some of the limitations while collecting the information include finding recent data and locating specific information suitable for the study.

 

Milestone 2

The objective is to apply statistical critical thinking to existing studies that address the questions you posed in Milestone 1. By applying critical thinking, you will get a better understanding of the credibility of the conclusions drawn from these studies.

Specifically, in this milestone, you will identify between 3 and 5 studies that address the questions you stated in Milestone 1. These studies can appear in the general news media, e.g., NY Times, CNN, etc., academic journals, e.g., search on google scholar, trade journals or websites, government or non-government organization sites, e.g. bls, noaa, etc., to name a few.

In writing your report, make sure to include all sections, including those from Milestone 1. In other words, Milestone 2 is your complete project report.

To provide you with some guidance, let us consider an example. Assume that I am interested in the relationship between milk consumption and bone loss (osteoporosis). My hypothesis is that consuming more milk will lead to stronger bones. This hypothesis is based completely on anecdotal evidence as I grew up in a household where milk consumption was encouraged to get enough calcium and other nutrients. Indeed, milk does contain significant calcium, and calcium is important for healthy bones. But, what does the evidence say?

As a first step, I will identify a few studies (you should select between 3 and 5 studies) that appear to address my problem.

Studies

To illustrate, I have chosen three studies that illustrate different aspects of analysis. Note that I am using these studies as an illustration of the type of studies you may use, but not necessarily as the studies I would use to answer my question. Studies that provide details on data collection, statistical techniques used, analyses performed, and limitations identified generally will be more amenable for critical analysis than studies that only briefly state the results and conclusions. As such, please select the most appropriate studies for your question.

  1. USDA recommendations for dairy consumption: http://www.fns.usda.gov/sites/default/files/milk.pdf
  2. Bone health and dairy consumption as stated on the website of the Dairy Farmers of Canada website: https://www.dairynutrition.ca/scientific-evidence/bone-health-and-osteoporosis/synopsis-bone-health-and-osteoporosis
  3. A 2014 research paper that studied the impact of milk consumption on mortality and fractures in men and women in Sweden: http://www.bmj.com/content/349/bmj.g6015

Now that we have identified the three studies, let us examine them critically. Specifically, how valid are the conclusions from the studies? Would we trust one study over another? Why? To answer such questions, we will proceed in steps, asking several questions in each step that will help answer these questions.

Data Sources and Data

One of the key factors that determine the validity of your results, at least statistically, is the way data is generated. Using anecdotal data will invariably result in weaker conclusions compared to data from a randomized controlled experiment. In this section, we ask a series of questions related to data and their sources. This is summarized in the table below:

 

Question Study 1 Study 2 Study 3
How was the data collected or produced? No data is provided. While no data is provided, references to research studies are provided. Researchers collected the data from a population of Swedish men and women based on a prospective observational study.
How reliable are the sources? Was the research being sponsored by an organization? Have you considered the effect of bias if so? Is there a conflict of interest between the sponsor and the objectives of the study? There appears to be no apparent bias. Though, USDA recommendations are based on a panel that can consist of industry representatives. This site belongs to the Dairy Farmer’s association of Canada. There is a possibility of bias. Though, the references they provide may not be biased. These appear to be independent researchers with no apparent connection to the dairy industry.
What is the scope of data? For what population will the results from this data be valid? Is the sample reflective of this population? How large is the sample? It is not clear, though the statements appear to indicate that it applies to all. It is not clear, though the presentation appears to indicate that the results are valid for all. The sample comes from a population of Swedish men and women. It is not clear if the sample is representative of the population. The sample consists of 61,433 women and 45,339 men.
How was the sample generated? Is this a probability sample? No evidence to make a conclusion There is no mention, though exploring one of the references (http://www.ncbi.nlm.nih.gov/pubmed/10759135) indicates that the results are based on a meta study of other studies. This study is based on a cohort group, where all men and women in the chosen county were selected as part of the sample. It is not clear how the counties were chosen, but the researchers appear to use cohorts from previous studies.
How current is the data?  When was it collected and over what time period?  Has data quality improved in more recent studies as compared to older studies? There is no mention The meta study was published in 2000. This study was published in 2014, and data collection was stopped in 2010. This study references Study 2 also.
Is the data raw data, or summarized data? N/A The references are meta studies, that is, a summary of other studies. So, while individual studies may have used raw data, this study uses summarized data. This study uses raw data.
Have errors in data been removed? N/A There is no mention. While there is no explicit mention, the researchers appear to have corrected for non-response.
Have the data been validated and tested? Is there independent verification of the data? N/A Unable to determine The researchers do use a previous cohort and indicate that repeat measurements from previous studies has increased their accuracy of measurements.

Now that you have summarized the sources of data, what conclusions can you make? Are results from one study likely to have more validity than the others?

Now, let us examine the assumptions and limitations of the studies.

Assumptions and Limitations

  • What assumptions have been made? For example, most studies have an implicit assumption that the results of the study will be valid for the population from which the samples are drawn. Approaches like random sampling ensure that such assumptions are reasonable. For Studies 1 and 2, the basic assumption is that the results stated there are universally applicable. For Study 3, the authors caution about the implications of the study. Further, given their sample, the implications of the study might be restricted to Swedish men and women.
  • What are the limitations of the study? How does this impact the reliability and validity of the conclusions? For example, what can you say about the conclusions presented in Study 1? If you agree to the conclusions, on what basis? If you disagree, why? What about Study 3?

Analysis and Presentation

  • What statistical techniques have been used to analyze data?
  • What type of study was it? Observational, experiment, etc.?
  • Are the graphs, tables, and charts drawn correctly, or are they misleading?

Conclusions

  • Are the conclusions valid in terms of scope and recommendations? Did the authors note any limitations of the study?
  • Are the researchers making conclusions that cannot be supported by their data?
  • Are the results independently corroborated?

What is your conclusion based on the studies at which you have looked? What can you now say about Disraeli’s statement?

This article might be interesting reading: https://www.brainpickings.org/2014/01/03/baloney-detection-kit-carl-sagan/

REFERENCES

Remember to cite and reference all tables, graphs, external references, and data sources. Use any standard citation format like APA, MLA, etc.

 

 

ENGR 411-01, 411-02, LAB 5 Instrumentation and Process Control Lab (Spring 2016) Page 1 of 3 Page 1 of 3


LAB 5
ENGR 411-01, 411-02 Instrumentation and Process Control Lab (Spring 2016) Page 1 of 3
Page 1 of 3
Proportional Plus Integral (PI) Control of Liquid Level in a Tank
INTRODUCTION:
This lab provides a hands-on experience with designing a P and PI controller for liquid level control in a tank using Simulink. A closed-loop control system is modeled in Simulink using common blocks and the performance of a P and PI controller are investigated further by changing the gains.
BACKGROUND:
Simulink provides a powerful tool for simulating dynamic systems in a short time using block diagrams. Students will get a hands-on training in building a simple closed-loop control system in this lab.
OBJECTIVE:
To design a liquid-level control system.
Figure 1: Liquid-level control system.
Figure 2: Block diagram for level control system.
PROCEDURE:
Figure 1 shows a schematic of the system. The block diagram of the level control system is given in Figure 2. Students should identify all the blocks in the closed-loop control system first. In figure 2, 𝐻𝑠𝑝′(𝑠) is the Laplace transform of the liquid level set point (system input i.e. the desired liquid level in tank) and 𝐻′(s) is the Laplace transform of liquid level (system output i.e. the actual liquid level in tank).
Definitions of parameters and their numerical values are given in Table I.
LAB 5
Report Due Thursday April 21
Apr. 7, 2015 ENGR 411-01, 411-02 Instrumentation and Process Control Lab (Spring 2016) Page 2 of 3
Page 2 of 3
Table 1: System Parameters.
Parameter
Description
Numerical value
units
Km
Sensor-transmitter gain
50
%/m
Gc(s)
Controller transfer function
NA
NA
KIP
IP transducer gain
0.12
psi/%
Kv
Control valve gain
1.03×10-1
m3/min psi
KP
Process gain
6.37
min/m2
τ
Process time constant
5
min
(1) Use Mason’s Gain Formula (Forward Gain)/(1-Loop Gain) to show that when 𝐺𝑐(𝑠)= 𝐾𝑐, the overall system transfer function is (ignoring 𝑄1′):
𝐻′(𝑠)𝐻𝑠𝑝′(𝑠)= 𝐾𝑐 𝐾𝐼𝑃𝐾𝑣𝐾𝑚𝐾𝑃𝜏𝑠+1+𝐾𝑐 𝐾𝐼𝑃𝐾𝑣𝐾𝑚𝐾𝑃
(2) For the following 3 cases 𝐾𝑐= 20; 𝐾𝑐=8; 𝐾𝑐=4 (proportional only controller):
 Simulate the system versus time (simulation time = 10 minutes) when system input ℎ𝑠𝑝′= step input of 1.0 meter for the above three proportional gains.
 Provide plots of ℎ′(𝑡) vs. 𝑡 for the above 𝐾𝑐 gains.
 Try to plot the three cases using MATLAB plot command on a single graph; to do so look under “Sinks” in the Simulink Library Browser and drag a “To Workspace” block to your simulation window; connect the system output to that block. After the simulation, data values are available as “simout.data” and the time values are available as “simout.time”. Note that each time you do a simulation, the data is over written, so after each simulation save the data in a new variable; e.g. after first simulation: t1 = simout.time; h1 = simout.data; after second simulation t2 = simout.time; h2 = simout.data, etc. then in MATLAB type plot(t1,h1,t2,h2,t3,h3).
 Make sure your plots have proper titles and axes labels, you may write them by hand if don’t know how to do it by the software.
 IMPORTANT: Make observations, state your observations and write your conclusions.
(3) Add a saturation block right before the 𝐾𝐼𝑃 block (see figure 2) to limit 𝑃′ to fall between -100% and +100% and repeat part (2) above.
 How do the results compare to those of part 2? Write your observations and conclusions.
LAB 5
Report Due Thursday April 21
Apr. 7, 2015 ENGR 411-01, 411-02 Instrumentation and Process Control Lab (Spring 2016) Page 3 of 3
Page 3 of 3
(4) Now use a PI-controller, i.e. set 𝐺𝑐(𝑠)=𝐾𝑐(1+1𝜏𝐼𝑠), use Mason’s gain formula to show that for the disturbance input 𝑄1′: 𝐻′(𝑠)𝑄1′(𝑠)= 𝐾𝑝𝜏𝑠+11+ 𝐾𝑂𝐿𝜏𝑠+1 (1+1𝜏𝐼𝑠)
where 𝐾𝑂𝐿=𝐾𝑐𝐾𝐼𝑃𝐾𝑣𝐾𝑃𝐾𝑚, assume 𝐻𝑠𝑝′(𝑠)=0
(5) To study system response to disturbance input when under PI control, do the following: Plot ℎ′(𝑡) versus time for simulation time = 20 minutes where ℎ𝑠𝑝′(𝑡)=0 and disturbance 𝑄1′(𝑠)= 1𝑠 (positive unit step) for the six cases below (no saturation block):
a. 𝐾𝑐= 5 and 𝜏𝐼= 2 min; 0.5 min and 0.2 min. (No saturation block.) Preferably, plot the three curves in a single graph.
b. 𝜏𝐼= 0.5 min and Kc = 2; 5 and12.5. (No saturation block.) Preferably, plot the results in a single graph.
Make observations, state them and make conclusions.
Note: A positive unit step at 𝑄1′ would require negative flow into the tank to cancel the effect of the disturbance 𝑄1′. While physically not possible, it is convenient for analysis of the response plot. Cause of this problem is ℎ𝑠𝑝′(𝑡) set to 0 for simplifying the analysis. Usually, the disturbance occurs at non zero values of liquid height where controller counter acts by reducing the inflow 𝑄2′.
(6) To study system response under PI control to a change in set point:
a. repeat cases (5) a and b above for the same 𝐾𝐶’s and 𝜏𝐼’s given above but with 𝑄1′=0 and 𝐻𝑠𝑝′=1𝑠 (unit step of magnitude 1 meter). Simulation time 20 seconds and no saturation block.
b. Now put the ±100% saturation block back in before 𝐾𝐼𝑃 block, run simulations and try to find the best values of 𝐾𝑐 and 𝜏𝐼 by trial and error that would provide fastest response (shortest rise time) and least overshoot. Report your result.
As usual, make observations and try to explain.
REPORT (Due Apr. 21):
Short, individual report that explicitly addresses items (1) to (6) above. State your observations and make conclusions for each case as necessary. Regarding the plots, please don’t forget to write titles and label the axes. Your report should include the following:
Introduction (half a page).
Problem definition (half a page).
A brief explanation of the experiments (1 page).
Models/calculations/simulation results (2 pages).
Conclusions (half a page).
List of references as necessary.
Appendix (if necessary).

Engineering Questions


  • In closed- die forging process, what are the function of flush? In general, what will happen if there is too little or too much flush?

 

  • During sheet metal forming processes, i.e. bending and deep drawing, the springback is often seen and it needs to be considered in designing the tools and the processes. Please describe the concept of springback, and explain the reasons of causing springback? How will the springback affect the stresses in the part?

 

  • What is heat affected zone (HAZ) in welding process? Please describe it and drew a schematic figure of the HAZ. Sometimes high cycle fatigue cracks initiate in the neighbor out side the HAZ, what can be the cause?

 

  • Work hardening during rolling processes affects the anisotropy of the rolled products. In general, what is the difference of the anisotropy severity between hot rolled and cold rolled products? Please explain why?

 

  • Please briefly describe the friction welding process. Is friction welding fusion welding or solid state welding process? What are its advantages and drawbacks?

 

  • Please briefly describe the sand casting and investment casting processes?

 

  • What is the physical meaning of Taguchi Loss Function? (the equation is given below)

Loss cost= K [(Y – T)]^2 +α^2]

 

Replacement cost

Where K= —————————

(LSL – T)^2

 

  • During a flat rolling process, what are the reasons of causing poor flatness of rolled metal sheets? Please explain two methods for improved flatness of the rolled metal sheets.

 

  • What are the three phases at the liquid and solid interface during an immersion oil quenching process? How will the three phases affect the cooling severity?
  • During an open-die forging (upsetting) process of a cylindrical billet, please explain why does the obtained part geometry have a barrel shape? How to reduce the severity of the barrel shape (more straight)?

 

  • Please explain why is the workpiece surface temperature higher during a grinding process than that of a lathe or milling machining process? Please explain through the cutting tool geometry aspect.

 

 

  • Using a lath machining process as an example, heat is generated from the plastic deformation of the chip and the friction between the workpiece and the cutter. The cutter contact with workpiece, and heat flux flows into the cutter. The temperature change in the cutter generates internal stresses, which may cause stress fatigue. Please explain how will the thermal conductivity, specific heat, and coefficient of thermal expansion of the cutter material affect the stress magnitude in the cutter caused by the heat flux mentioned above?

 

  • In a closed-die forging process, what are the function of a flash?

CRQ10 Aviation professionalism, behavior and ethnics


YouTube: https://www.youtube.com/watch?v=24JKyLosC6Q

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1-Discuss the professionalism, behavior and ethnics of the decisions made by each of the following stakeholders in the video.  Did they do the right thing, did they act in a professional manner, what they did that you would consider good or not up to standard etc.  Please discuss each in detail and give examples used in the video.   [10 –points each]

  1. Pilots of Alaska 261
  2. Maintenance person and dispatch the pilots called when they needed help
  3. The mechanic that reported Alaska Airlines to the DOT (John Liotine)

2-Under similar circumstances, would you do the same thing that John Liotine did and report the company to the DOT or the FAA?  Please explain your answer and the reasoning behind your answer IN DETAIL.  “I don’t know” is not an option for this question. [15-points]

CRQ11 

YouTube: https://www.youtube.com/watch?v=QMLvT91iLEk

1-Compare and contrast the actions from a professionalism and ethical behavior of the employees between this incident and the Alaska Airlines 261.  Briefly describe the events or elements and discuss at least two issues that you think were unethical or unprofessional.  Be specific as to if it was an ethical or professionalism characteristic.  [20-points]

2-Briefly discuss two (2) or more factors that you learned for the first time, or factors that surprised you, or changed your mind about certain aspects of aircraft safety or maintenance after watching this video?   [10-points]

 

3-What was the primary probable cause of the accident?  [5-points]

CRQ12

YouTube: https://www.youtube.com/watch?v=x_yHtfGH0nI

1-List AND describe a minimum of five (5) required checks covered in this video.  [15-points]

2-Briefly discuss two (2) or more factors that you learned for the first time, or factors that surprised you, or changed your mind about certain aspects of aircraft safety or maintenance after watching this video?   [10-points]

3-As a pilot or manager, what concerns do you have with the maintenance personal or maintenance operations in your company?  In other words, what should you be really paying attention to or checking on to ensure that your aircraft are properly maintained and are safe to operate?  Please explain in detail [10-points]

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Making an Interpretation of one of the essays provided in the paper instructions


Subject or discipline: English 101
Title: Making an Interpretation of one of the essays provided in the paper instructions
Number of sources: 1
Provide digital sources used: No
Paper format: MLA
# of pages: 4
Spacing: Double spaced
# of words: 1100
Paper details:
Comments from Support Team:

Making an Interpretation

Although we are all familiar with the essay form, we may not be comfortable analyzing essays as arguments. However, essays, like all forms of writing, implicitly or explicitly take a stand, make an argument. To grow as critical readers – and thinkers – we must be able to analyze and make our own interpretations of what a given piece of writing is trying to teach us, to persuade us.

For this reason, your first essay in EN105 asks you to develop an interpretation of one of the following essays:

Annie Dillard, “Living Like Weasels”
Zora Neale Hurston, “How It Feels to Be Colored Me”
Benjamin Franklin, “Arriving at Perfection”
As DiYanni explains in the Introduction to One Hundred Great Essays, an interpretation is not a summary; in fact, interpreting what an essay means can only happen once the reader has not only an accurate grasp of the content but has also gone further to observe details, connect those details, and make inferences about the author’s argument based on those details.

Your interpretation, then, will not be a summary of your selected essay; instead, it will be your argument as to a primary meaning and persuasive purpose of the essay. Like any piece of writing, an essay can have multiple interpretations; thus, your interpretation should be arguable, debatable, forcing you to support it with enough analysis of the text to reveal to your own readers the validity of your interpretation.

Like all of your essays in 105, your essay will need a clear focus (a thesis statement expressing your interpretation), supported by well-developed arguments, a clear organization, and a writing style that is professional and free from error.

Length: Approximately 1,000 words (about 4 pages, double-spaced, 1 inch margins, Times New Roman font).

Style/Format: This, as all essays in EN105, should be formatted according to MLA (Modern Language Association) guidelines for scholarship in the humanities:

12 point, Times New Roman font, double-spaced.
1-inch margins top, bottom, and sides.
Although no cover page is needed, you should include your name, my name, the course number/title, and date at the upper left-hand corner of the manuscript.
To view a sample MLA-formatted paper, see p. 252 in Easy Writer.
File format: Please submit your essay in Rich Text Format (RTF). This is available in most word processing programs; it will ensure maximum document accessibility for all operating platforms.

Titles: Include a descriptive title at the beginning of your essay that tips your readers off to your thesis. Do not format your title with quotation marks, boldface, underlining or italics. Quotation marks or underlining are only appropriate if the title borrows words from another source.

Use of essays for future courses: Please understand that your essay may be used—anonymously—as a sample for future EN105 students and instructors unless you expressly request that it not be used. Your work, of course, will only be used for educational purposes.

Grading: See the “Grading and Assessment” content item under Course Information.

Essays needed:

Annie Dillard, “Living Like Weasels”

Zora Neale Hurston, “How It Feels to Be Colored Me”

Benjamin Franklin, “Arriving at Perfection”

Cultural Anthropology Mini-Ethnography, Spring 2015


Cultural Anthropology Mini-Ethnography, Spring 2015

Cultural Anthropology-Spring 2015
Mini-Ethnography – 100 points
TlTh1,2:15-lz49
By nowyou’ve all read about how anthropologists do what they do {ch. 3) and have read or seen
some examples of ethnographic fieldwork (Nanook of the North, most of the essays in Ferraro). Now it’s your
turn to conduct a mini-ethnographic field project and report on your findings.
Requirements:
1) observe or participate in a gathering, event or ceremony that involves 10 or more people. choosing
a good gathering, event or ceremony is an important first step to doing well on this assignmen! so here are
some tips: a) choose something that the participants don’t do every day b) pick something with way more
than 10 people – the more people to observe, the better c) pick something that takes a good amount of time
to complete – the more you have to report on, the better d) pick an event where you have some level of
access – you can move around and see the event from different perspectives, tatk to participants, gather
information easily. You cannot do your ethnography on something that you’ve attended in the past – you
must observe/participate in something new.
2i Either during the event or soon after, document what is happening or has happened. Taking notes
is a good idea; video and/or photographs are good ideas as well. you should be abte to describe the event in
great detail, describe the participants in detail, and have specific thoughts about how this event reflects on the
culture of the participants. Because this is a mini-ethnography, recording more information than you think
you’ll need is suggested. lf the event or gathering has literature associated with it (a program, a leaftet, a
poster, etc), getting one of those is a good idea as well.
3) write your mini-ethnography. To get an idea of how anthropologists describe events, review the
articles in the Ferraro book and think about the ethnographic films we’ve watched this semester. you mav
a Make sure you address
the following in detail:
1) What is the event – give brief introductory description of what it is you attended or
participated in
2) Describe the setting, when and where did this event take place and why did it take place
there and not someplace else?
3) Describe the participants. Who are they? Age? Sex? Ethnicity? What do they all have in
common? How are they different? What culture (s) and sub-culture(s) do they belong to? Why
are they there (asking one or more participants would be a good way to answer this)
4) Describe in detail what happened at the event. Who did what? Why do you think they did
what they did? what were the reactions of the other participants?
5) What meaning does the event have for those who participate in it {again, asking
participants this question would be a good way to address it)
6) What were your impressions of the event? What do you think is the event’s m’eaning or
purpose {this may differ significantly from what those who participated told you). How would __
an anthropologist analyze this event and how would they characterize it as a par!-“oJ broader

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