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Systems Engineering Research Taking Systems Engineering to the Next Level Cihan H Dagli, Ph. Systems Engineering Research Taking Systems Engineering to the Next Level Cihan H Dagli, Ph. D Professor of Engineering Management and Systems Engineering Professor of Electrical and Computer Engineering Founder and Boeing Coordinator of Systems Engineering Graduate Program INCOSE and IIE Fellow [email protected] . edu MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY Rolla, Missouri, U. S. A.

Outline • Introduction – Need for Systems Architecting and Engineering – Do. D Systems Outline • Introduction – Need for Systems Architecting and Engineering – Do. D Systems Engineering Vision 2020 • Academia Needs • Missouri S&T’s Approach – Smart Systems Architecting – Courses – Industry Cooperation • Future Of Systems Architecting

Research Collaborators • Renzhong Wang (Current Sys. Eng Ph. D Student, INCOSE Doctoral Award Research Collaborators • Renzhong Wang (Current Sys. Eng Ph. D Student, INCOSE Doctoral Award Recipient) • Dr. Atmika Singh (Former Sys. Eng Ph. D Student, Researcher at Clearway Holding) • Dr. Jason Dauby (Former Sys. Eng Ph. D Student, INCOSE Doctoral Award Recipient, Researcher at Naval Surface Warfare Center) • Dr. Nil Kilicay Ergin (Former Sys. Eng Ph. D Student, Faculty at Pen State University)

Introduction The Dynamically Changing Operating Environment – We are increasingly a networked society: • Introduction The Dynamically Changing Operating Environment – We are increasingly a networked society: • Trans-national mega military systems • Asymmetrical threats vs. rapid reaction forces • Trans-national enterprises • Trans-national manufacturing • Globally distributed services and production – We are increasingly dependent on these networks.

Decision Analysis • Voice of Customer • Customer Requirements • Expert Judgment • • Decision Analysis • Voice of Customer • Customer Requirements • Expert Judgment • • Operational C 4 ISR Communications Dynamic Systems System of Systems Courtesy of Dr. Mike Mc. Coy Introduction Effectiveness Advanced Supportability • Survivability • Vulnerability • Mission Success • Supply Chain Mgt. • Maintenance Mgt. Analysis • Supply Mgt. Analysis • LCC/TOC • Design to Cost • Best Value • Visualize Scenarios • Immerse Man in Loop

Introduction (Adopted from An Overview of Global Earth Observation System of Systems (GEOSS), Stefan Introduction (Adopted from An Overview of Global Earth Observation System of Systems (GEOSS), Stefan Falke, Geospatial Intelligence Operating Unit, Northrop Grumman Corporation)

Introduction Super-Efficient , Eco-Friendly, and People Friendly Trans-national Manufacturing Introduction Super-Efficient , Eco-Friendly, and People Friendly Trans-national Manufacturing

Need for Systems Architecting and Engineering • • Systems Engineering: An interdisciplinary approach and Need for Systems Architecting and Engineering • • Systems Engineering: An interdisciplinary approach and means to enable the realization of successful systems. Systems Engineering considers both the business and the technical needs of all stakeholders with the goal of providing a quality product that meets the user needs. System Architecture: The aggregation of decomposed system functions into interacting system elements whose requirements include those associated with the aggregated system functions and their interfaces requirements/definition INCOSE (International Council of Systems Engineers)

Need for Systems Architecting and Engineering Cost and Schedule Performance as a Function of Need for Systems Architecting and Engineering Cost and Schedule Performance as a Function of Systems Engineering Effort *Source: INCOSE Systems Engineering Center of Excellence SECOE 01 -03 INCOSE 2003; & Honour, E. “Understanding Value of Systems Engineering”, INCOSE Conference, June 20 -24, 2004

Need for Systems Architecting and Engineering • Performed by NDIA in conjunction with the Need for Systems Architecting and Engineering • Performed by NDIA in conjunction with the SEI in 2006 -2008 • Surveyed 64 projects at defense contractors to assess: • Data was collected anonymously to encourage honest and accurate reporting. • • Results published at: http: //www. sei. cmu. edu/publications/d ocuments/08. reports/08 sr 034. html *Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

Need for Systems Architecting and Engineering PROJECT PERFORMANCE vs. TOTAL SE CAPABILITY 1. 00 Need for Systems Architecting and Engineering PROJECT PERFORMANCE vs. TOTAL SE CAPABILITY 1. 00 15% 12% Best Performance ( x > 3. 0 ) 0. 75 56% 46% 59% Moderate Performance 0. 50 13% 0. 25 39% 29% 31% ( 2. 5 x 3. 0 ) Lower Performance ( x < 2. 5 ) 0. 00 Lower Capability Moderate Capability Higher Capability ( x 2. 5 ) N = 13 ( 2. 5 < x < 3. 0 ) N = 17 (x 3. 0 ) N = 16 Gamma = 0. 32 p = 0. 04 *Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

Need for Systems Architecting and Engineering *Source: Presentation of Joe Elm from Software Engineering Need for Systems Architecting and Engineering *Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

Need for Systems Architecting and Engineering *Source: Presentation of Joe Elm from Software Engineering Need for Systems Architecting and Engineering *Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

Need for Systems Architecting and Engineering • Architectures are fundamental to the success of Need for Systems Architecting and Engineering • Architectures are fundamental to the success of the program • Architecture selection is a search process based on ambiguous information and data • Architecture selection requires assessment methods based on ambiguous key performance parameters to identify compromise architecture • Architecting process is reduction of ambiguity hierarchically

Do. D Systems Engineering Vision 2020 • Design Principles – Platform Based Engineering Using Do. D Systems Engineering Vision 2020 • Design Principles – Platform Based Engineering Using a common core platform to develop many related systems/capabilities – Trusted System Design Developing trusted systems from untrusted components

Do. D Systems Engineering Vision 2020 • Design Framework – Model Based Engineering Using Do. D Systems Engineering Vision 2020 • Design Framework – Model Based Engineering Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing

Do. D Systems Engineering Vision 2020 • Adaptable Do. D Systems – Capability on Do. D Systems Engineering Vision 2020 • Adaptable Do. D Systems – Capability on Demand Real-time Adaptive Systems Rapidly Reconfigurable Systems Pre-planned Disposable Systems

Academia Needs • Systems Architecting Laboratory: Real Engineering Problems and Customer • Environment to Academia Needs • Systems Architecting Laboratory: Real Engineering Problems and Customer • Environment to demonstrate, value of systems engineering and new systems architecting approaches on real systems of various size • Close cooperation with industry honoring propriety nature of information and data • Dissemination channels for new research

Missouri S&T’s Approach Systems Architecting Research Missouri S&T’s Approach Systems Architecting Research

Smart System Architecting • • • How can we assess architectures? How can we Smart System Architecting • • • How can we assess architectures? How can we represent architectures? How can we generate architectures? How can we reduce ambiguity hierarchically? How can we test architectures for correctness? What are the tools of architect?

Smart Systems Architecting C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart Smart Systems Architecting C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design, ” Systems Research Forum , Vol. 3, No. 2 (2009) 101– 119

Do. D Systems Engineering Vision 2020 • Design Framework – Model Based Engineering Using Do. D Systems Engineering Vision 2020 • Design Framework – Model Based Engineering Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing

Smart Systems Architecting 1. What constitutes the “best” in architecture? 2. What is the Smart Systems Architecting 1. What constitutes the “best” in architecture? 2. What is the measure for comparing architectures? 3. We can search for the “best” architecture, as long as we can define “best” 4. Can we associate an aggregate value in evaluating functional architectures? 5. How can we deal with the ambiguity of need requirements and performance measures in the search process? 6. Is there a way to mathematically represent functional architectures? 7. Can we generate architectures through a evolutionary process? 8. Can we integrate the architect in evolutionary architecting process? C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design, ” Systems Research Forum , Vol. 3, No. 2 (2009) 101– 119

Smart Systems Architecting PERFORMANCE PERCEPTIONS SCHEDULE COST RISK FACTS What is the measure for Smart Systems Architecting PERFORMANCE PERCEPTIONS SCHEDULE COST RISK FACTS What is the measure for comparing architectures?

Smart Systems Architecting Adaptability Robustness Affordability Flexibility Survivability Reliability What is a reasonable approach Smart Systems Architecting Adaptability Robustness Affordability Flexibility Survivability Reliability What is a reasonable approach to find aggregate measure for comparing architectures?

Smart Systems Architecting Super-Efficient , Eco-Friendly, and People Friendly Top level system attributes Smart Systems Architecting Super-Efficient , Eco-Friendly, and People Friendly Top level system attributes

Smart Systems Architecting (SSA) SSA Approach q. Fuzzy Assessment and Computing with words q. Smart Systems Architecting (SSA) SSA Approach q. Fuzzy Assessment and Computing with words q. Evolutionary Algorithms for Architecture q. Canonical Decomposition Fuzzy Comparison (CDFC) q. Self Organizing Maps for Clustering Architecture Families q. Models for Behavior Modeling C. H. Dagli, A. Singh, J. P. Dauby, R. Wang, “Smart systems architecting: computational intelligence applied to trade space exploration and system design, ” Systems Research Forum , Vol. 3, No. 2 (2009) 101– 119

Fuzzy Assessment and Computing with Words Modern large-scale systems are comprised of many interacting Fuzzy Assessment and Computing with Words Modern large-scale systems are comprised of many interacting subsystems and components and exhibit complex behavior. This nonlinear behavior cannot be analyzed using traditional modeling approaches. Fuzzy Cognitive Maps based methodology can be for assessing the inherent value of candidate architectures early in the design lifecycle. A. Singh and C. H. Dagli, “"Computing with words" to support multi-criteria decision making during conceptual design, ” Systems Research Forum, vol. 04, no. 01, p. 85, 2010.

Fuzzy Assessment and Computing with Words The system and its components are represented in Fuzzy Assessment and Computing with Words The system and its components are represented in the form of a directional graph where the nodes represent system components and the arcs represent their interactions. This modeling approach makes use of the “computing with words” (CW) paradigm to use human experience to assign linguistic weights to the arcs based on the strength of influence between connected nodes. An overall value measure for a system can be derived by simulating the resulting graph. Such an approach will facilitate the selection of the best set of architectures or component technologies during the nascent design stages based on the value delivered to the stakeholder. A. Singh and C. H. Dagli, “"Computing with words" to support multi-criteria decision making during conceptual design, ” Systems Research Forum, vol. 04, no. 01, p. 85, 2010.

Evolutionary Algorithms for Architecture Once architecture options have been identified using FCM and CW, Evolutionary Algorithms for Architecture Once architecture options have been identified using FCM and CW, evolutionary algorithms can be employed to find the right combination of technologies to utilize in a system design. Functional architecture chromosome

Canonical Decomposition Fuzzy Comparison (CDFC) The CDFC methodology is a new architecture assessment approach Canonical Decomposition Fuzzy Comparison (CDFC) The CDFC methodology is a new architecture assessment approach offering increased objectivity, fidelity, and defensibility in comparison to traditional approaches. The methodology consists of four elements: • Extensible modeling – facilitates the exchange of data between model resolution levels. • Canonical design primitives – basic representations of system-component technologies. • Comparative analysis – comparison between heuristic and canonical embodiments. • Fuzzy inference – a mapping from system response features to fuzzy sets describing the architecture assessment. J. P. Dauby, “Assessing system architectures: the canonical decomposition fuzzy comparative methodology, ” Ph. D. dissertation, Dept. Eng. Management and Sys. Eng. , Missouri University of Science and Technology, Rolla, MO, 2011.

Canonical Decomposition Fuzzy Comparison (CDFC) Architecture assessment for airborne wireless systems in conjunction with Canonical Decomposition Fuzzy Comparison (CDFC) Architecture assessment for airborne wireless systems in conjunction with a potential Acquisition Category (ACAT) ID program for the Department of the Navy J. P. Dauby, “The canonical decomposition fuzzy comparative approach to assessing physical architectures, ” INSIGHT, vol. 13, no. 3, pp. 60 -62, Oct. 2010.

Self Organizing Maps for Clustering Architecture Families Architecture solution candidates are described by functional, Self Organizing Maps for Clustering Architecture Families Architecture solution candidates are described by functional, logical, or physical properties including integration sensitivity. The set of properties for each candidate are used as the input vector to a variety of SOM algorithms. The SOM output can identify design features and group potential architectural concepts into families based on common features, sensitivities, or tendencies. This approach facilitates the development of architecture families that exhibit similar behavior as well as identify combinations of technologies that work well together.

Models for Behavior Modeling Motivation Introduce dynamic model analysis into architecture modeling. Facilitate system Models for Behavior Modeling Motivation Introduce dynamic model analysis into architecture modeling. Facilitate system behavior, performance, and effectiveness analysis, architecture evaluation, and functionality verification and validation. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Vol. 14(3), 2011

Models for Behavior Modeling Requirement Analysis and Specification Start Requirements Analysis Desired Behavior Refinement Models for Behavior Modeling Requirement Analysis and Specification Start Requirements Analysis Desired Behavior Refinement Architecture Analysis and Evaluation End Behavior analysis Functionality verification Architecture refinement & reconfiguration Modeling Formal Model Sys. ML Diagrams Model Transformation Executable model CPN Simulation Interactive GUI External Application Behavior as modeled Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Vol. 14(3), 2011

Models for Behavior Modeling • OMG (Object Management Group), Semantics of a Foundational Subset Models for Behavior Modeling • OMG (Object Management Group), Semantics of a Foundational Subset for Executable UML Models, Version 1. 0 Beta 3, ptc/2010 -03 -14, http: //www. omg. org/spec/FUML/1. 0/Beta 3/, 2010 a • Foundational UML Reference Implementation, http: //portal. modeldriven. org/project/foundational. UML – Specify and demonstrate the semantics required to execute activity diagrams and associated timelines per the Sys. ML v 1. 0 specification – Specify the supporting semantics needed to integrate behavior with structure and realize these activities in blocks and parts represented by activity partitions Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Vol. 14(3), 2011

Models for Behavior Modeling • “Behavioral Formalism” refers to a formalized framework for describing Models for Behavior Modeling • “Behavioral Formalism” refers to a formalized framework for describing behavior, such as state machines, Petri nets, data flow graphs, etc. – UML/Sys. ML, modeling language weak in executable semantics – Supplemented by Semantics of a Foundational Subset for Executable UML Models • Software that implemented behavioral formalism – CORE, IBM Rational Rhapsody, CPN Tools, etc. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Vol. 14(3), 2011

Models for Behavior Modeling • Combined usage of related tools. – Three basic functions Models for Behavior Modeling • Combined usage of related tools. – Three basic functions of a model: • Specification (of a system to be built), – UML and Sys. ML • Presentation (of a system to be explained to other people, or ourselves), – Do. DAF products • Simulation. – Petri nets, DEVS (Discrete Event Specification System – x. UML, XTUML, VM, Business Process Modeling Notation/Business Process Execution Language BPMN /BPEL • Extract key information from simulation to support architecture evaluation and analysis. • Refine the architecture design based on analysis results. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Vol. 14(3), 2011

Do. DAF 2. 0 Architecture Viewpoints and Do. DAF-described Models Do. D Architecture Framework Do. DAF 2. 0 Architecture Viewpoints and Do. DAF-described Models Do. D Architecture Framework Version 2. 0 Volume I

Architecture Presentation Techniques Do. D Architecture Framework Version 2. 0 Volume I Architecture Presentation Techniques Do. D Architecture Framework Version 2. 0 Volume I

Architecture Analytics Do. D Architecture Framework Version 2. 0 Volume I Architecture Analytics Do. D Architecture Framework Version 2. 0 Volume I

Executable Modeling Formalisms • The chosen of executable modeling language depends on the system Executable Modeling Formalisms • The chosen of executable modeling language depends on the system to be modeled, the abstraction level to work on, and the system behavior of interest. • Many modern distributed systems can be best specified by discrete event models because – The behavior of these systems is driven only by events that occur at discrete time points. • Discrete-event models* represent the operation of a system as a chronological discrete sequence of events. Each event occurs at an instant in time and marks a change of state in the system. • An executable architecture is a dynamic model that defines the precise event sequences, the conditions under which event is triggered and information is produced or consumed, and the proprieties of producers, consumers and other resources associated with the operation of the system. * Banks, J. Discrete-event System Simulation. Pearson Prentice Hall, Upper Saddle River, NJ. 2005.

Colored Petri Nets (CPNs) Places carry makers, called tokens, which mark the state of Colored Petri Nets (CPNs) Places carry makers, called tokens, which mark the state of a system. Arcs tell how actions modify the state and when they occur 1. 2. 3. Transitions describe the actions of the system 1 1`”data” When certain conditions hold, transitions will be fired, causing a change in the placement of tokens and thus the change of system states. Combining a well-defined mathematical foundation, an interactive graphical representation, and the capabilities to carry out simulations and formal verifications. The same models can be used to check both the logical or functional correctness of a system and for performance analysis. CPNs are very flexible in token definition and manipulation.

Executable Semantics Place (w tokens) CPN System Conditions Input Data/Information Control signals Resources Other Executable Semantics Place (w tokens) CPN System Conditions Input Data/Information Control signals Resources Other Discrete Event System Specification State Transition Place (w tokens) Event Effects Action / Activity (a set of actions) Output Data/Information Control signals Resources Other • Time Delay • Post conditions Transition State Relationships between CPN Artifacts, System Entities and Discrete Event System Specifications

Models for Behavior Modeling Meta-Architecture System 1 System 4 Net-Centric Architecture System 2 Robust Models for Behavior Modeling Meta-Architecture System 1 System 4 Net-Centric Architecture System 2 Robust G I G Adaptable System n-1 Modular Flexible System 3 Interoperable Dynamically Changing Meta-Architecture for Complex Systems System n

Models for Behavior Modeling • For modeling the meta-architecture – Multi-agent based modeling • Models for Behavior Modeling • For modeling the meta-architecture – Multi-agent based modeling • Agents • Environment • Interactions • For modeling sub-system architectures – Cognitive architectures N. Kilicay-Ergin “Architecting System of Systems: Artificial Life Analysis of Financial Market Behavior”, Ph. D Dissertation Dept. Eng. Management and Sys. Eng. , Missouri University of Science and Technology, Rolla, MO, 2007

System-of-Systems System Level Behavior Agent Level Dynami cs Semantics Meta-architecture Agent 1= System 1 System-of-Systems System Level Behavior Agent Level Dynami cs Semantics Meta-architecture Agent 1= System 1 Cognitive Level* Selection Criteria Agent 2= System 2 Agent 3= System 3 Agent n= System n Perception Meta-management *Sloman’s H-Cogaff architecture, Deliberative Reasoning Action Environment Level Sub-system architectures Reactive Mechanism 2000 Mechanism Modules Computational Intelligence Toolbox Short-term memory Neural Networks Long-term Associative memory Genetic Algorithm Imitation Attention filter Learning Classifiers Reinforcement Learning Bias Swarm Intelligence

Missouri S&T’s Approach Degrees and Graduate Certificates Missouri S&T’s Approach Degrees and Graduate Certificates

Systems Engineering Degrees • MS in Systems Engineering – Architected based on a need Systems Engineering Degrees • MS in Systems Engineering – Architected based on a need statement of invited Boeing RFP in 1998. – Since the inception of the program on Spring 2000 semester 410 engineers have received their M. S. degrees. – Ten courses – six core and four engineering specialization- are required for the degree. • Ph. D in Systems Engineering – One graduate from Boeing Seattle out of four graduates since 2006 – Fifteen students currently in the program

Systems Engineering MS Degree Curriculum Core Courses Systems Architecture Sys. Eng 469 – Systems Systems Engineering MS Degree Curriculum Core Courses Systems Architecture Sys. Eng 469 – Systems Architecting Systems Engineering and Analysis Sys. Eng 368 – Systems Engr. and Analysis I Systems Engineering – Information Based Design Sys. Eng 468 – Systems Engr. and Analysis II Complex Systems Management Economic Decision Analysis Sys. Eng 413 Economic Analysis for Systems Engineering Mgt. Sys. Eng 412 Complex Engineering Systems Program Mgt. Organizational Behavior and Management Sys. Eng 411 Systems Engineering Capstone

Systems Engineering Graduate Certificates Systems Engineering Graduate Certificate Network Centric Graduate Certificate Computational Intelligence Systems Engineering Graduate Certificates Systems Engineering Graduate Certificate Network Centric Graduate Certificate Computational Intelligence Graduate Certificate Model Based Systems Engineering Graduate Certificate (In Approval Process ) • Software Architecting and Engineering Graduate Certificate • •

Systems Engineering Graduate Certificate Sys. Eng 368 Systems Engineering and Analysis I Sys. Eng Systems Engineering Graduate Certificate Sys. Eng 368 Systems Engineering and Analysis I Sys. Eng 468 Systems Engineering and Analysis II Sys. Eng 413 Economic Analysis for Systems Engineering Sys. Eng 469 Systems Architecting Students completing these four courses with a minimum grade of B in each course are admitted to the M. S. degree program in Systems Engineering without taking the GRE.

Network Centric Systems Graduate Certificate Core Courses: • Sys. Eng/Cp. E 419 Network-Centric Systems Network Centric Systems Graduate Certificate Core Courses: • Sys. Eng/Cp. E 419 Network-Centric Systems Architecting and Engineering • Cp. E/Sys. Eng 449 Network-Centric Systems Reliability and Security Communications Engineering Elective Courses (select two): • Cp. E 317 Fault Tolerant Digital Systems • Cp. E 319 Digital Network Design • Cp. E 349 Trustworthy, Survivable Computer Networks • Cp. E/Sys. Eng 348 Wireless Networks • Cp. E /Sys. Eng 443 Wireless Adhoc and Sensor Networks • Cp. E 448 High Speed Networks • CS 483 Computer Security • CS 486 Mobile and Sensor Data Management

Computational Intelligence Graduate Certificate Core Courses: §Cp. E 358/EE 367/Sys. Eng 367 Computational Intelligence Computational Intelligence Graduate Certificate Core Courses: §Cp. E 358/EE 367/Sys. Eng 367 Computational Intelligence and select one of the following: §CS 347 Introduction to Artificial Intelligence §CS 348 Evolutionary Computing §Sys. Eng 378/CS 378/EE 368 Introduction to Neural Networks and Applications Elective Courses (Select two courses not taken as a core course): §EE/Cp. E/Sys Eng 301 Evolvable Hardware §CS 347 Introduction to Artificial Intelligence §CS 348 Evolutionary Computing §CS 447 Advanced Topics in Artificial Intelligence §CS 448 Advanced Evolutionary Computing §Sys. Eng/Cp. E/EE 458 Adaptive Critic Designs §CS/Sys. Eng/Cp. E 404 Data Mining and Knowledge Discovery §EE 337 Neural Networks for Control §Sys. Eng 378/CS 378/EE 368 Introduction to Neural networks and Applications §Cp. E/Sys. Eng/EE 457 Markov Decision Processes §Sys. Eng 478 Advanced Neural Networks

Model Based Systems Engineering Graduate Certificate Sys. Eng 433 Distributed Systems Modeling Sys. Eng Model Based Systems Engineering Graduate Certificate Sys. Eng 433 Distributed Systems Modeling Sys. Eng 435 Model Based Systems Engineering Sys. Eng 479 Smart Engineering Systems Design Emgt 374 Engineering Design Optimization

Software Architecting and Engineering Graduate Certificate CS 308 Object Oriented Analysis and Design Cs Software Architecting and Engineering Graduate Certificate CS 308 Object Oriented Analysis and Design Cs 309 Software Requirements Engineering Sys. Eng 435 Model Based Systems Engineering Sys. Eng 470 Software Intensive Systems Architecting

Research Cooperation • • DARPA Manufacturing Experimentation and Outreach (MENTOR) Program supplier to Boeing Research Cooperation • • DARPA Manufacturing Experimentation and Outreach (MENTOR) Program supplier to Boeing Research and Technology- Awarded, Duration: One year Department of Defense Systems Engineering Research Center- University Affiliated Research Center SERC-UARC at Stevens Institute of Technology Project “Agile Systems Engineering: Experiential and Active Learning Approach”, Duration: 05/15/2010 to 7/31/2011 Department of Defense University Affiliated Research Center for Systems Engineering Research Joint Proposal with Steven’s Institute of Technology, University of Southern California and other participating universities. October 2008 – October 2013 The Boeing Company, Systems Engineering MS Degree Program for Italian Engineers: Under Industrial Return Project Italian 767 Tanker Transport, BOEING Industrial Participation Program Duration: 2006 – 2009

Future Research Needs 1. 2. 3. As an integrated global society, we depend on Future Research Needs 1. 2. 3. As an integrated global society, we depend on complex, distributed engineering systems that can adapt to the dynamically changing needs of society. These systems are seen in health care, infrastructure, transportation, energy, defense, security, environmental, manufacturing, communications and supply chain systems, among others. Adaptability within these systems is critical. We need to push the boundaries of research in Complex Adaptive Systems and respond to the continuous global change in systems needs. http: //complexsystems. mst. edu/ http: //cser. mst. edu

Recent Publications 1. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Recent Publications 1. Renzhong Wang and Cihan H. Dagli, “Executable System Architecting Using Systems Modeling Language in Conjunction with Colored Petri Nets in a Model Driven Systems Development Process. ” Journal of Systems Engineering, Article first published online: 4 March 2011 2. Dauby, J. P. Dagli, C. H. , "The Canonical Decomposition Fuzzy Comparative Methodology for Assessing Architectures, " Systems Journal, IEEE , vol. 5, no. 2, pp. 244 -255, June 2011 3. Aaron A. Tucker, Gregory T. Hutto and Cihan H. Dagli “ Application of Design of Experiments to Flight Test: A Case Study” Journal of Aircraft Vol. 47, No. 2, March-April 2010 4. Atmika Singh and Cihan H Dagli ““ Computing with words” to Support Multi-Criteria Decision-Making During Conceptual Design” Systems Research Forum Vol. 4, No. 1 (2010) 85 -99.

Recent Publications • C. H. Dagli, Atmika Singh, Jason P. Dauby and Renzhong Wang Recent Publications • C. H. Dagli, Atmika Singh, Jason P. Dauby and Renzhong Wang “ Smart Systems Architecting: Computational Intelligence Applied to Trade Space Exploration and System Design”, Systems Research Forum Vol. 3, No. 2 (2009) 101– 119. • A. A. Tucker and C. H. Dagli, "Design of Experiments as a Means of Lean Value Delivery to the Flight Test Enterprise”, Journal of Systems Engineering, volume 12, Number 3, 2009. Pp. 201 - 217. • M. Rao, S. Ramakrishnan, and C. Dagli, “Modeling and simulation of net centric system of systems using systems modeling language and colored Petri-nets: A demonstration using the global earth observation system of systems, ” Systems Engineering, vol. 11, 2008, pp. 203 -220.

Concluding Remarks Most biological systems do not forecast or schedule They respond to their Concluding Remarks Most biological systems do not forecast or schedule They respond to their environment — quickly, robustly, and adaptively As engineers, let us don’t try and control the system. Design the system so that it controls and adapts itself to the environment created by dynamically changing needs

Are we there yet? Are we there yet?