Systems Engineering Research Taking Systems Engineering to the Next Level Cihan H Dagli, PhD Professor of Engineering Management and Systems Engineering.

Systems Engineering ResearchTaking Systems Engineering to the Next Level

Cihan H Dagli, PhDProfessor 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]

MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGYRolla, Missouri, U.S.A.

mailto:[email protected]

Outline

• Introduction– Need for Systems Architecting and Engineering– DoD 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 SysEng PhD Student, INCOSE Doctoral Award Recipient)

• Dr. Atmika Singh (Former SysEng PhD Student, Researcher at Clearway Holding)

• Dr. Jason Dauby (Former SysEng PhD Student, INCOSE Doctoral Award Recipient, Researcher at Naval Surface Warfare Center)

• Dr. Nil Kilicay Ergin (Former SysEng PhD Student, Faculty at Pen State University)

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 Provides More Customer Interaction and a Better Product


Wants

Needs Desires

Wishes

Must Haves

Decision Analysis techniques are toolsused to solve complex problems

through a structured process

MultipleMultipleParticipantsParticipants

ConflictingConflictingInterestsInterests

MultipleMultipleObjectivesObjectives

• Consensus• Common Terminology• List of Potential Trades

• Ranked Priorities• Documentation

CompetingCompetingAlternativesAlternatives

Multiple Multiple DisciplinesDisciplines



Wants

Needs Desires

Wishes

Must Haves





MultipleMultipleParticipantsParticipants

ConflictingConflictingInterestsInterests

MultipleMultipleObjectivesObjectives

• Consensus• Common Terminology• List of Potential Trades

• Ranked Priorities• Documentation

CompetingCompetingAlternativesAlternatives

Multiple Multiple DisciplinesDisciplines

• Prognostics & Health Management

• Opportunistic maintenance

• Interactive Tech Manuals

• Prognostics & Health Management

• Opportunistic maintenance

• Interactive Tech Manuals

• Flexible support• Flexible support

• Proactive manufacture

• Proactive supply

• Autonomic distribution

• Spares usage & trends

• Projected spares needs

POLPipelines

CODResupply

GroundDeliveries

Repair&

Overhaul

AircraftVehicle

EquipmentGeneration &Maintenance

IntermediateRepairOEM

• 24/7 Response centers

• Digital Engineering Links

• 24/7 Response centers

• Digital Engineering Links

NetworkNetwork--Centric FutureCentric Future

Effectiveness

• Survivability• Vulnerability• Mission Success

Advanced Supportability

• Supply Chain Mgt.• Maintenance Mgt.

Analysis• Supply Mgt. Analysis

• Operational C4ISR• Communications• Dynamic Systems• System of Systems

• Visualize Scenarios• Immerse Man in Loop

Decision Analysis

• Voice of Customer• Customer Requirements• Expert Judgment

• LCC/TOC• Design to Cost• Best Value

PointEstimates

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igh

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UCAV ATDPh ase II

RR &OEEM D

1998

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1999

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2000

1Q 2Q 3Q 4 Q

2001

1Q 2 Q 3Q 4Q

2002

1Q 2 Q 3Q 4Q

2003

1Q 2 Q 3Q 4 Q

2004

1Q 2Q 3Q 4 Q

2005

1Q 2Q 3Q 4 Q

Ph as e I I Sta r t EM D St ar t

F lt De mo 1 F lt De m o 2 F lt D em o 3 F lt D em o 4

R R&OE St ar t

L ast revisi on:

F lt De mo 5

Pha se II En d

UCAV d ec ision aid s flig ht dem o

D elive r B - 2 w eap ond elive ry GWI S

JSF /UCAV Com m ona lity S t udy

Es tab lish Com mo n Av ion icsD eve lopm e nt G ro up

D EMPC UD S Fo rm at ion Ta xi/F ligh t ( fixe d g eom , pos sep a lgor ithm s)

UDS Coo rd inat ed mo tion , va ria bleg eom e trie s / d ec onf liction alg or ithm s

Glo bal the ate r m ult i-le vel n etw or king de mo

BOL DSTR OKE d em os

S ing le s imu late dve hicle dis trib ute dco ntr ol la b d em o

Re al-t ime so ftwa re ar chite ctu re& d esig n d em o

UCAV d ecis ion aids lab de mo(c ont inge ncy m ana ge me nt)

R eal- tim e d istr ibu ted pr oce ssin g

AJ/L P I LO S C2 Dem o

Sof twar e reu se me tr ics tr ac king

Dem o of O M P & Int ellig entMa inte na nce A ids /PMT & I MSS

L ab & flig ht d em o - O MP/ miss ion /veh icles yste ms inte gr atio n

AT3 o r PL AID te st o n U CAV

So ftwa re re use m etr ics t ra cking

SAR f ligh t te sto n UC AV

M ult i-se nso r m ult i-so ur ce dat a fu sio n

AJ/L P I BLO S C2 -AJ G PS Dem o

A ir tra ffic mg t d em o

Decis ion aids forope ra tor ha nd offlab dem o

Aut om ate d d yna m ic m issio nr epla nn ing fligh t te st d em o

Dro p m ult iple pr e- plan ne d sm a ll

bo mb s fr om M BR with fu ll SM S

U DAS A lgo rith mic Con tr ol F ligh t T est Dem o( M ulti- Vehic le Co or din ate d F ligh t, C ollisio nAv oida nce , Se nso r P la nn er , Aut oro ute r)

L os s of com m co ntin ge ncy fligh t d em oSu pplie r soft war e p ro duc tivity de mo

PH M/O M P

Fa llb ac k:

Su p p lier s

B oe in g

U CAV AT D RR &OE

G o v’t S&T

U CAV AT D Ph as e I I

U CAV AT D Ph as e I

} UT P

Pri ma ry: F ully inte gr ate ds oftw are fu nct iona lity.

De cre ase d s oft war ef unc tion ality .

Unmanned C ombat Air V ehicleAdvanced Technology Demonstration

UCAV - A TD

Phase II - Affordabi lity / LCC P lan

Pr e pared by:

David M cC aughey (B oeing )

C oncurred by:

Ku rt Bau sch (B oeing)

S teve Ras t (S AI C)

App roved b y:

Ph il P anag os (Boeing)

Lt Col Mic hae l Leah y (U SA F)

AffordabilityPlan

$ $

HistoricalRegression

SupplierOptions

Un

it C

os

t

JSF

UCAV

1/3 the cost of JSFO

&S

Co

st F-16

UCAV

75% Reduction from F-16

Time

$

CostTargets

Time

$

Time

$

Development /Investment Plans

Time

$

Focus:• System Cost

Drivers

• Figures of Merit

• Effectiveness & Affordability Balance

• Investment Planning

CostUncertaintySimulation

LCC Probability

0%

20%

40%

60%

80%

100%

EMDProdO&S

$$

Kurt Bausch314-232-6917

PointEstimates

Ris

k F

act

or

Lo

wM

edH

igh

UCA V AT DPha se I

UCAV ATDPh ase II

RR &OEEM D

1998

1 Q 2 Q 3Q 4Q

1999

1Q 2 Q 3Q 4Q

2000

1Q 2Q 3Q 4 Q

2001

1Q 2 Q 3Q 4Q

2002

1Q 2 Q 3Q 4Q

2003

1Q 2 Q 3Q 4 Q

2004

1Q 2Q 3Q 4 Q

2005

1Q 2Q 3Q 4 Q

Ph as e I I Sta r t EM D St ar t

F lt De mo 1 F lt De m o 2 F lt D em o 3 F lt D em o 4

R R&OE St ar t

L ast revisi on:

F lt De mo 5

Pha se II En d

UCAV d ec ision aid s flig ht dem o

D elive r B - 2 w eap ond elive ry GWI S

JSF /UCAV Com m ona lity S t udy

Es tab lish Com mo n Av ion icsD eve lopm e nt G ro up

D EMPC UD S Fo rm at ion Ta xi/F ligh t ( fixe d g eom , pos sep a lgor ithm s)

UDS Coo rd inat ed mo tion , va ria bleg eom e trie s / d ec onf liction alg or ithm s

Glo bal the ate r m ult i-le vel n etw or king de mo

BOL DSTR OKE d em os

S ing le s imu late dve hicle dis trib ute dco ntr ol la b d em o

Re al-t ime so ftwa re ar chite ctu re& d esig n d em o

UCAV d ecis ion aids lab de mo(c ont inge ncy m ana ge me nt)

R eal- tim e d istr ibu ted pr oce ssin g

AJ/L P I LO S C2 Dem o

Sof twar e reu se me tr ics tr ac king

Dem o of O M P & Int ellig entMa inte na nce A ids /PMT & I MSS

L ab & flig ht d em o - O MP/ miss ion /veh icles yste ms inte gr atio n

AT3 o r PL AID te st o n U CAV

So ftwa re re use m etr ics t ra cking

SAR f ligh t te sto n UC AV

M ult i-se nso r m ult i-so ur ce dat a fu sio n

AJ/L P I BLO S C2 -AJ G PS Dem o

A ir tra ffic mg t d em o

Decis ion aids forope ra tor ha nd offlab dem o

Aut om ate d d yna m ic m issio nr epla nn ing fligh t te st d em o

Dro p m ult iple pr e- plan ne d sm a ll

bo mb s fr om M BR with fu ll SM S

U DAS A lgo rith mic Con tr ol F ligh t T est Dem o( M ulti- Vehic le Co or din ate d F ligh t, C ollisio nAv oida nce , Se nso r P la nn er , Aut oro ute r)

L os s of com m co ntin ge ncy fligh t d em oSu pplie r soft war e p ro duc tivity de mo

PH M/O M P

Fa llb ac k:

Su p p lier s

B oe in g

U CAV AT D RR &OE

G o v’t S&T

U CAV AT D Ph as e I I

U CAV AT D Ph as e I

} UT P

Pri ma ry: F ully inte gr ate ds oftw are fu nct iona lity.

De cre ase d s oft war ef unc tion ality .

Unmanned C ombat Air V ehicleAdvanced Technology Demonstration

UCAV - A TD

Phase II - Affordabi lity / LCC P lan

Pr e pared by:

David M cC aughey (B oeing )

C oncurred by:

Ku rt Bau sch (B oeing)

S teve Ras t (S AI C)

App roved b y:

Ph il P anag os (Boeing)

Lt Col Mic hae l Leah y (U SA F)

AffordabilityPlan

$ $

HistoricalRegression

SupplierOptions

Un

it C

os

t

JSF

UCAV

1/3 the cost of JSFO

&S

Co

st F-16

UCAV

75% Reduction from F-16

Time

$

CostTargets

Time

$

Time

$

Development /Investment Plans

Time

$

Focus:• System Cost

Drivers

• Figures of Merit

• Effectiveness & Affordability Balance

• Investment Planning

CostUncertaintySimulation

LCC Probability

0%

20%

40%

60%

80%

100%

EMDProdO&S

$$

Kurt Bausch314-232-6917

Introduction

Courtesy of Dr. Mike McCoy

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

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)

*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

Cost and Schedule Performance as a Function of Systems Engineering Effort

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/08sr034.html

*Source: Presentation of Joe Elm from Software Engineering Institute Carnegie Mellon University

http://www.sei.cmu.edu/publications/documents/08.reports/08sr034.html

http://www.sei.cmu.edu/publications/documents/08.reports/08sr034.html



39%

46%

15%

29%

59%

12%

31%

13%

56%

BestPerformance( x > 3.0 )

ModeratePerformance( 2.5 x 3.0 )

Lower Performance( x < 2.5 )

Lower Capability

( x 2.5 )N = 13

Moderate Capability

( 2.5 < x < 3.0 )N = 17

HigherCapability

(x 3.0 )N = 16

Gamma = 0.32p = 0.04

1.00

0.75

0.50

0.25

0.00

PROJECT PERFORMANCE vs. TOTAL SE CAPABILITY






• 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

DoD Systems Engineering Vision 2020

• Design Principles– Platform Based Engineering

Using a common core platform to develop many related systems/capabilities

– Trusted System DesignDeveloping trusted systems from untrusted components


• Design Framework– Model Based Engineering

Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing


• Adaptable DoD Systems– Capability on Demand

Real-time Adaptive SystemsRapidly Reconfigurable SystemsPre-planned Disposable Systems

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

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 systems architecting: computational intelligence applied to trade space exploration and system design,” Systems Research Forum ,Vol. 3, No. 2 (2009) 101–119


• Design Framework– Model Based Engineering

Using modeling and simulation for rapid, concurrent, integrated system development and manufacturing

Smart Systems Architecting1. 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

PERFORMANCE

SCHEDULE

RISK

PERCEPTIONS

COST

FACTS

What is the measure for comparing architectures?


Adaptability

Affordability

Survivability

Robustness

Flexibility

Reliability

What is a reasonable approach to find and aggregate measure for comparing architectures?


Super-Efficient , Eco-Friendly, and People Friendly

Top level system attributes


Smart Systems Architecting (SSA) SSA Approach

Fuzzy Assessment and Computing with words

Evolutionary Algorithms for Architecture

Canonical Decomposition Fuzzy Comparison (CDFC)

Self Organizing Maps for Clustering Architecture Families

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

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.

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.

Fuzzy Assessment and Computing with Words

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.

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.

Evolutionary Algorithms for ArchitectureOnce 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

Assess Threat

Assess Resource

Assess Status

PickWaveform

Generate Waveform

Amplify Waveform

Steer Antenna

Radiate Power

Gather Intel

Receive Signal

Library Lookup

Locate Target

Resolve Ambig

ThermManag

Prime Power

Blank/ EMI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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)

Decomposition Using Canonical

Primitives

Comparative Analysis: Isolated vs. Integrated

Performance

Fuzzy Feature Interpretation

Architecture Assessment

1

2

3

4

Physical Architecture

1

3

2 4

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, 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 ModelingMotivation

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



ModelingRequirement Analysis

and Specification

Requirements Analysis

Formal Model

SysML Diagrams

Executable model

CPN

Simulation

Interactive GUI

Architecture Analysis and Evaluation

Architecture refinement & reconfiguration

Functionality verification

Behavior analysis

Start

End

Model Transformation

Behavior as modeled

Behavior as modeled

Desired BehaviorDesired

Behavior

Refinement

External Application

• 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/Beta3/, 2010a

• Foundational UML Reference Implementation, http://portal.modeldriven.org/project/foundationalUML– Specify and demonstrate the semantics required to execute activity

diagrams and associated timelines per the SysML v1.0 specification – Specify the supporting semantics needed to integrate behavior with

structure and realize these activities in blocks and parts represented by activity partitions



http://portal.modeldriven.org/project/foundationalUML

• “Behavioral Formalism” refers to a formalized framework for describing behavior, such as state machines, Petri nets, data flow graphs, etc.– UML/SysML, 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.



• Combined usage of related tools. – Three basic functions of a model:

• Specification (of a system to be built), – UML and SysML

• Presentation (of a system to be explained to other people, or ourselves),– DoDAF products

• Simulation. – Petri nets, DEVS (Discrete Event Specification System – xUML, X

TUML, 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.



DoDAF 2.0Architecture Viewpoints and DoDAF-described Models

DoD Architecture Framework Version 2.0 Volume I

Architecture Presentation Techniques


Architecture Analytics


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 a system.

Transitions describe the actions of the system

Arcs tell how actions modify the state and when they occur

1. Combining a well-defined mathematical foundation, an interactive graphical representation, and the capabilities to carry out simulations and formal verifications.

2. The same models can be used to check both the logical or functional correctness of a system and for performance analysis.

3. CPNs are very flexible in token definition and manipulation.

11`”data”

When certain conditions hold, transitions will be fired, causing a change in the placement of tokens and thus the change of system states.

Executable Semantics

Event Conditions Effects

TransitionPlace

(w tokens)

Place (w tokens)

TransitionState State

CPNCPN

Discrete Event System Specification

Discrete Event System Specification

SystemSystem

Output Data/Information Control signals Resources Other

•Time Delay•Post conditions

Input Data/Information Control signals Resources Other

Action /Activity (a set of actions)

Relationships between CPN Artifacts, System Entities and Discrete Event System Specifications

G I G

Net-Centric Architecture

Robust

Interoperable

Adaptable

Flexible Modular

System 1

Meta-Architecture

Dynamically Changing Meta-Architecture for Complex Systems

System 2

System 3

System 4

System n

System n-1


• 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”, PhD Dissertation Dept. Eng. Management and Sys. Eng., Missouri University of Science and Technology, Rolla, MO, 2007


Swarm Intelligence

ReinforcementLearning

Genetic Algorithm

Neural Networks

ComputationalIntelligenceToolbox

Short-termmemory

Long-termAssociative memory

Attention filter BiasImitation

MechanismModules

Reactive Mechanism

Deliberative Reasoning

Meta-managementP

erce

pti

on

Act

ion

Agent 1= System 1 Agent 2= System 2 Agent 3= System 3 Agent n= System n

CognitiveLevel*

AgentLevel

EnvironmentLevel

System Level Behavior

LearningClassifiers

Dynamics

Semantics Selection Criteria

System-of-Systems

Meta-architecture

Sub-system architectures

*Sloman’s H-Cogaff architecture,

2000

Missouri S&T’s Approach

Degrees and Graduate Certificates

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.

• PhD in Systems Engineering– One graduate from Boeing Seattle out of four graduates since 2006– Fifteen students currently in the program

Curriculum Core Courses

Systems

Systems ArchitectureSysEng 469 – SystemsArchitecting

Systems Engineering and AnalysisSysEng 368 – SystemsEngr. and Analysis I

Systems Engineering – Information Based Design

SysEng 468 – SystemsEngr. and Analysis II

Complex Systems Management

Economic Decision AnalysisSysEng 413 Economic Analysis for Systems Engineering

Systems Engineering Mgt.SysEng 412 Complex Engineering Systems Program Mgt.

Organizational Behavior and Management

SysEng 411 Systems Engineering Capstone

Systems Engineering MS Degree

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

SysEng 368 Systems Engineering and Analysis ISysEng 468 Systems Engineering and Analysis IISysEng 413 Economic Analysis for Systems EngineeringSysEng 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.

Systems Engineering Graduate Certificate

Network Centric Systems Graduate CertificateCore Courses: • SysEng/CpE 419 Network-Centric Systems Architecting and Engineering

• CpE/SysEng 449 Network-Centric Systems Reliability and Security

Communications Engineering Elective Courses (select two):

• CpE 317 Fault Tolerant Digital Systems

• CpE 319 Digital Network Design

• CpE 349 Trustworthy, Survivable Computer Networks

• CpE/SysEng 348 Wireless Networks

• CpE /SysEng 443 Wireless Adhoc and Sensor Networks

• CpE 448 High Speed Networks

• CS 483 Computer Security

• CS 486 Mobile and Sensor Data Management

Core Courses:CpE 358/EE367/SysEng 367 Computational Intelligence

and select one of the following: CS 347 Introduction to Artificial IntelligenceCS 348 Evolutionary ComputingSysEng 378/CS 378/EE 368 Introduction to Neural Networks and Applications

Elective Courses (Select two courses not taken as a core course):EE/CpE/Sys Eng 301 Evolvable HardwareCS 347 Introduction to Artificial IntelligenceCS 348 Evolutionary ComputingCS 447 Advanced Topics in Artificial IntelligenceCS 448 Advanced Evolutionary ComputingSysEng/CpE/EE 458 Adaptive Critic DesignsCS/SysEng/CpE 404 Data Mining and Knowledge DiscoveryEE 337 Neural Networks for ControlSysEng 378/CS 378/EE 368 Introduction to Neural networks and ApplicationsCpE/SysEng/EE 457 Markov Decision ProcessesSysEng 478 Advanced Neural Networks

Computational Intelligence Graduate Certificate

SysEng 433 Distributed Systems ModelingSysEng 435 Model Based Systems EngineeringSysEng 479 Smart Engineering Systems DesignEmgt 374 Engineering Design Optimization

Model Based Systems Engineering Graduate Certificate

CS 308 Object Oriented Analysis and DesignCs 309 Software Requirements EngineeringSysEng 435 Model Based Systems EngineeringSysEng 470 Software Intensive Systems Architecting

Software Architecting and Engineering Graduate Certificate

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

1. As an integrated global society, we depend on complex, distributed engineering systems that can adapt to the dynamically changing needs of society.

2. These systems are seen in health care, infrastructure, transportation, energy, defense, security, environmental, manufacturing, communications and supply chain systems, among others.

3. 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/

Future Research Needs

http://cser.mst.edu

http://cser.mst.edu/

http://cser.mst.edu/

Recent Publications1. 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 “

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.

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

Concluding Remarks

Are we there yet?

Systems Engineering Research Taking Systems Engineering to the Next Level Cihan H Dagli, PhD Professor of Engineering Management and Systems Engineering.

Documents

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systems engineering

trusted systems

engineering architectures

computer engineering

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pen state university

system architecture