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Certified Modeling and Simulation Professional Exam Primer


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Title: Certified Modeling and Simulation Professional Exam Primer

Certified Modeling and Simulation Professional
Exam Primer
Amy Henninger Ed Degnan Jeff Wallace
Tutorial Objectives
  • Understand CMSP Processes for certification and
  • Current processes
  • Future revisions in work
  • Be familiar with the fundamental topics for the
    Current CMSP exam
  • Technology
  • Applications
  • Gain insight on how to best prepare
  • Serve as (open book) reference for the CMSP exam.
  • Pointer to other references for the CMSP exam.

Tutorial Outline
  • Review current processes for CMSP
  • Review proposed future processes for CMSP
  • Review Exam Track for Technology
  • Review Exam Track for Applications

CMSP Mission
  • To develop and maintain an international
    Certification Program for Simulation
    Professionals recognizing standard levels of
    knowledge and functional competency for the
    certified professionals and the industry.

CMSP Vision
  • A worldwide community of Modeling and Simulation
    professionals that values the accomplishments of
    individuals and provides an environment that
  • Encourages and stimulates individual professional
    growth in Modeling and Simulation.
  • Promotes the development and application of
    Modeling and Simulation throughout society.

Taking the Exam Who
  • Education plus Work Experience must be no less
  • Associate Degree and 8 years work experience
  • Bachelor Degree and 6 years work experience
  • Masters Degree and 5 years work experience
  • Doctorate and 3 years work experience
  • Three letters of recommendation

Taking the Exam What
  • Currently only baseline certification is
  • 45 Questions
  • 15 General Questions
  • 15 Questions on Technology (pick 3 sub topics)
  • 15 Questions on Applications (pick 3 sub topics)
  • Exam is open book

Taking the Exam Where and When
  • Where - Exam is on line!
  • When Take it at your convenience, after your
    credentials are approved

Taking the Exam Why and How
  • Why - Certification demonstrates that the MS
    community recognizes you as a leader in the
    profession. As the field matures, contracts will
    eventually request simulation professionals.
    Certification will give you and your company an
  • How Certification process is detailed at

  • How Can I Register for the MSPCC Certification
    Exam? The online application must be completed
    in one sitting, so you will want to gather all
    necessary information (and even have material
    ready to cut and paste) prior to beginning your
    application. The application form will time out
    in about 30 minutes if left unattended. Three
    references are required, and you will need to
    provide their e-mail addresses. Other details are
    covered in the PDF instructions. Make sure that
    you review and meet the specific education work
    experience requirements shown on the Application
    Information PDF.
  • How much does the Certification Cost? The full
    cost will be 250 (non-refundable). In the event
    your credentials are not in order, you will be
    allowed to reapply after one year from date of
    notification. Should you not pass the exam, you
    will be allowed to retake after a six month
    waiting period without paying a second fee. The
    second exam must be completed between six months
    and one year of the first submission. After one
    year, you will be considered a new applicant
    and begin the process from the start.
  • When are the registration Deadlines? No
  • What does the Certification do for me?
    Certification demonstrates that the MS community
    recognizes you as a leader in the profession. As
    the field matures, contracts will eventually
    request simulation professionals. Certification
    will give you and your company an advantage.
  • What are the requirements for Certification?
    Relevant (simulation) work experience and
    educational requirements, three letters of
    recommendation, and a passing grade on the exam.
  • For how long is the Certification valid? The
    certificate is valid for four years.
  • Who are the people on the Certification
    Committee? The nine-member Certification Board
    is made up of 3 representatives from the
    Industry, Government, and Academia fields.
  • Where do I go to take the certification exam?
    After your credentials have been approved, you
    will receive an e-mail with a link to the online
  • Who grades the certification exam? Your
    (anonymous) exam will be reviewed by at least two
    members of the Certification Board.

Official CMSP References
  • Badler, N.I. et. al. Simulating Humans Computer
    Graphics Animation and Control, 1993. human
  • Banks, J., J.S., Carson, B.L., Nelson, and D.M.
    Nicole, Discrete-Event System Simulation, third
    edition. Prentice-Hall, 2000.
  • Carrie, A., Simulation of Manufacturing Systems,
    Chichester New York Wiley, c1988.
  • Cellier F.E., Continuous System Modeling.
    Springer-Verlag, 1991. continuous, advanced,
  • Cloud, D. and L. Rainey, Editors, Applied
    Modeling and Simulation An Integrated Approach
    to Development and Operation, Space Technology
    Series, McGraw Hill, 1998. Decision Making
  • Dutton, J.M. Computer Simulation of Human
    Behavior. New York, Wiley, 1971. human behavior
  • Fishwick, P., Simulation Model Design and
    Execution Building Digital Worlds. Prentice
    Hall, 1995. general, introduction, simulation
  • Fujimoto, R.M., Parallel and Distributed
    Simulation Systems, Wiley Series on Parallel and
    Distributed Computing, 1999. parallel,
  • Gardner F.M. and J.D. Baker, Simulation
    Techniques Set, John Wiley Sons, 1997.
    simulation techniques
  • Gentle, J.E., Random Number Generation and Monte
    Carlo Methods. Springer Verlag, 1998. Monte
    Carlo, random numbers
  • Gould, H. and J. Tobochnik, Introduction to
    Computer Simulation Methods Applications To
    Physical Systems, second edition. Addison Wesley,
    1996. simulation methodology, physical
  • Jain, R., The Art of Computer Systems Performance
    Analysis Techniques for Experimental Design,
    Measurement, Simulation, and Modeling. John Wiley
    Sons, 1991. general, introduction,
    mathematics, statistics, simulation methodology,
    introduction, networks
  • Klir, G.J., Architecture of Systems Problem
    Solving, New York Plenum Press, c1985.
  • Kuhl, F, R. Weatherly and J. Dahmann, Creating
    Computer Simulation Systems An Introduction to
    High Level Architecture. Prentice Hall, 1999.
    HLA, High Level Architecture
  • Law, A.M., and W.D. Kelton, Simulation Modeling
    and Analysis, third edition. McGraw-Hill Series
    in Industrial Engineering and Management Science,
    2000. general, introduction, statistics,
    simulation methodology
  • National Research Council, Virtual Reality
    Scientific and Technological Challenges, National
    Academy Press, 1995. virtual reality, policy
  • National Research Council, Modeling Human and
    Organizational Behavior Application to Military
    Simulations, 1999. human behavior
  • Neyland, David L. Virtual Combat A Guide to
    Distributed Interactive Simulation, Stackpole
    Books, 1997. warfare
  • Zeigler, B.P., H. Praehofer and T.G. Kim, Theory
    of Modelling and Simulation, second edition,
    Academic Press, 2000. modeling
  • The Evolutionary Models in Social Science page,

Sample Questions (1)
Which of the following is considered a general
purpose interoperability architecture for
distributed MS? Aggregate Level Simulation
Protocol (ALSP) Distributed Interactive
Simulation (DIS) High Level Architecture
(HLA) Test and Training Network Architecture
(TENA) None of the above Which best
describes a Constructive Model or Simulation?
A broadly used taxonomy for classifying
simulation types. A simulation involving
real people operating real systems. A
simulation involving real people operating
simulated systems. Models and simulations
that involve simulated people operating simulated
systems. Real people stimulate (make inputs) to
such simulations, but are not involved in
determining the outcomes. None of the
above. Which of the following is a true
statement? Analytic game theory is always
used in Wargames. Analytic game theory is
sometimes used in Wargames. Analytic game
theory and Wargames are synonymous. None of
the above. What type of simulation is often
based on differential equations? Discrete
event simulation Continuous simulation
Monte Carlo simulation Cellular automata
simulation None of the above.
Sample Questions (2)
Which of the following choices is true about
queuing models? The arrive process can be
represented by a Poisson distribution A
queuing model is most often implemented with a
discrete event simulation The queue
processing time can be a stochastic process
All of the above None of the above Most
closely predict the underlying probability
distribution for the population from which the
following random sample was extracted (select one
answer) 1, 1.5, 2, 2.1, 2.3, 2.4, 2.8, 2.9, 3,
3, 3.2, 3.3, 3.4, 3.8, 4, 4.2, 4.5, 4.8, 5
Uniform Normal Standard Normal
Chi-squared None of the above Which of the
following is an advantage of using a one factor
at a time or hit and miss experimental
procedure? The optimum combination of all
study variables will be found efficiently
The interaction between factors can be
determined Many factors can be evaluated
simultaneously Tremendous efficiency and
cost savings can be achieved None of the
Proposed Approach and Timeline to Develop Revised
MS Certification Test (in consideration)
MS Certification Overview
MS Application Certification
Prerequisite for Application or Management
Baseline MS Certification
MS Management Certification
MS Certification Overview
Exam 100 Multiple Choice Questions
One year in a MS Position
Award MS Professional Credential
Yearly PU CEU Requirement
Renewal Every X Years
MS Certification Overview
Utilizing Bloom Taxonomy to Determine Mix of
Testable vs. Experiential
Level for Which Testing is Appropriate
1 - Knowledge Recalls data or information
2 - Comprehension Able to understand the meaning
of data or information
Level for Which Experiential is Appropriate
3 - Application Uses information in new
situations solves problems
4 - Analysis Breaks down information and
identifies components
5 - Synthesis Uses old ideas to create new ones
6 - Evaluation Compares and discriminates
between ideas
MS Certification Overview
MS Management Example
Basic Concepts
Understand historic perspective of MS
Historic Aspect of MS
Understand the historic aspects of MS and common
threads that are still valid today
DoD/Military Simulations
Modeling Concepts
Model Types
Model Definition
Know the definition for model
Model Concept
Determine information (and amount) required to
develop a model
Physical Models
Define a physical model and apply principles to a
given situation
Mathematical Models
Define a mathematical model and apply principles
to a given situation
Process Models
Define a process model and provide examples
Combination Models
Define combination models and provide examples
Baseline MS Certification Total of 100
Questions from a 500 Question Bank
MS Application Certification 50 Questions from
a 250 Question Bank for Each Area
Single Test Structure
Multiple Track Test Structure
50 Questions 250 Question Bank
Operational Testing
50 Questions 250 Question Bank
Developmental Testing
25 Questions 125 Question Bank
25 Questions 125 Question Bank
Core Topics 25 Questions 125 Question Bank
25 Questions 125 Question Bank
25 Questions 125 Question Bank
50 Questions 250 Question Bank
Medical Administration
25 Questions 125 Question Bank
Core Topics 25 Questions 125 Question Bank
Primary Care
25 Questions 125 Question Bank
SME Support will be Required to Design the
Structure and Questions for Each Area
MS Management Certification Total of 50
Questions from a 250 Question bank
Procurement Management for MS 10 Questions - 50
Question bank
Integration Management for MS 10 Questions - 50
Question bank
Time Management for MS 5 Questions - 25 Question
Cost Management for MS 10 Questions - 50
Question bank
Risk Management for MS 5 Questions - 25 Question
Scope Management for MS 5 Questions - 25
Question bank
Management of MS Workforce 5 Questions - 25
Question bank
Exam Track for MS Technology
MS Technology
  • Architectures
  • Computing and Networking
  • Conceptual Modeling
  • Human-related Issues
  • Mathematics
  • MS Paradigms
  • Physics
  • Visualization

  • Definitions of the various simulation
    interoperability architectures and techniques
  • Aggregate Level Simulation Protocol (ALSP)
  • Distributed Interactive Simulation (DIS)
  • High Level Architecture (HLA)
  • Simulator Networking (SIMNET)
  • Test and Training Network Architecture (TENA)
  • Definition of Live, Virtual, and Constructive

  • What are the differences between the various
    simulation interoperability architectures and
  • Understand the characteristics of different
  • Real-time v. managed time
  • Reliable v. unreliable
  • Ease-of-use
  • Different simulation execution architectures and
    their differences

Computing and Networking
  • Basic computer science techniques and questions
  • Random number generation
  • Data structure designs
  • Software engineering principles
  • Familiarity with the various computer
    communication protocols encountered in MS
  • Basic computer graphics questions
  • Issues in computing, e.g., computational

Conceptual Modeling
  • Understanding what conceptual models are, and
    their use in MS
  • What are the most common conceptual modeling
  • Basic questions
  • Who are conceptual modeling developers
  • When is conceptual modeling performed in the MS
    development process
  • Where conceptual modeling fits in the context of
    using various MS architectures

Human-related Issues
  • Haptic devices and their use
  • The elements of virtual environments
  • Technology and the impact on the human
  • Human-system/computer interfaces
  • Understand sources of error
  • Different types of interfaces
  • Human performance basics, and their relationship
    to MS

  • Important mathematical models in the history of
    defense-related MS
  • Understand how to follow basic calculations with
    these models
  • Common numerical methods
  • Interpolation
  • Basic differential equation solvers
  • Important probability and statistics techniques
    and result

MS Paradigms
  • There are many different MS paradigms
  • Understand what some of the more common MS
    paradigms are
  • Appreciate the differences in the characteristics
    of the different paradigms
  • The intended use of a system is frequently
    related to the paradigm used

MS Paradigms
  • Timing and synchronization are important features
    of an MS paradigm
  • Understand what the issues are
  • Numerical techniques differ between MS paradigm
  • The complicating issues associated with each
    should be appreciated

  • The principles underlying the evolution of the
    world must be approximated somehow in models and
  • A wide variety of techniques exist to do this
  • The complexity of the calculations and databases
    varies greatly, and has a significant impact on
    the execution performance

  • Basic understanding of different types of
    visualization devices
  • A variety of software techniques exist to
    visualize data
  • Understand the characteristics of the differing
  • Usage of the different visualization techniques
  • Notional understanding of visualization software

Exam Track for MS Applications
Ways To Study A System
Simulation, Modeling Analysis (3/e) by Law and
Kelton, 2000, p. 4, Figure 1.1
Technical Attractions of Simulation
  • Ability to compress time, expand time
  • Ability to control sources of variation
  • Avoids errors in measurement
  • Ability to stop and review
  • Ability to restore system state
  • Facilitates replication
  • Modeler can control level of detail
  • Discrete-Event Simulation Modeling,
    Programming, and Analysis by G. Fishman, 2001,
    pp. 26-27

Characterizing a Model
Does the model contain stochastic components?
Characterizing a Model
Is time a significant variable?
Characterizing a Model
Does the system state evolve continuously or
only at discrete points in time?

MS Applications
  • Discrete Event Simulation
  • Operations Research
  • Quantitative Aspects of MS
  • Science and Research Issues
  • Human Factors in MS
  • Interactive Modeling and Simulation

Discrete-Event Simulation
  • Deterministic vs Stochastic
  • Static vs Dynamic
  • Continuous vs Discrete
  • Components of DES
  • Understand concept of a Clock and different types
    of time management
  • Real-time
  • Scaled real-time
  • Logical-time
  • Understand basic constucts
  • Event List
  • Scheduler
  • Initial conditions and random number streams
  • Arrival times and inter-arrival times

Operations Research
  • Game Theory
  • Queuing Theory
  • Terms
  • Littles Formula
  • M/M/1

Quantitative Aspects of MS
  • Basic probability and statistics definitions
  • Probability Distributions - already covered
  • Confidence Intervals
  • Data types encountered in MS
  • Elements of numerical methods and application of
    common numerical techniques

Science and Research Issues
  • Important statistical quantities/concepts
  • Measures of Central Tendency
  • Central Limit Theorem
  • Probability Distributions
  • Hypothesis Testing
  • Experimental design problems
  • NO Design one factor at a time
  • DoE, Factorials
  • Experimental error types
  • Type I Errors
  • Type II Errors

Human Factors in MS
  • Expectancy
  • Mental Models
  • Cues

Interactive Modeling and Simulation
  • Live, Virtual, Constructive
  • Simulator Sickness

You must be a Certified Modeling and Simulation
Professional if.
You must be a Certified Modeling and Simulation
Professional if.
  • youve ever represented a horse with a sphere
    because it makes the math easier.

You must be a Certified Modeling and Simulation
Professional if.
  • you can remember 17 computer passwords, but you
    cant remember your anniversary.

You must be a Certified Modeling and Simulation
Professional if.
  • youre afraid to drive a car because the width
    of the road is negligible in comparison to the

You must be a Certified Modeling and Simulation
Professional if.
  • every possible combination of 3 letters is a
    meaningful acronym to you.

You must be a Certified Modeling and Simulation
Professional if.
  • youve ever calculated that the World Series
    actually diverges.

You must be a Certified Modeling and Simulation
Professional if.
  • you assume that Halloween and Christmas are the
    same thing because
  • 31 in OCT 25 in DEC

You must be a Certified Modeling and Simulation
Professional if.
  • you've sat at the same desk for four years, but
    youve worked for three different companies.

You must be a Certified Modeling and Simulation
Professional if.
  • whenever someone asks you where you live, you
    respond with What coordinate system do you want
    the answer in?

You must be a Certified Modeling and Simulation
Professional if.
  • you daydream of Super Models made up entirely of
    planar surfaces.

You must be a Certified Modeling and Simulation
Professional if.
  • no matter how simple it seems to you, no matter
    how easy it seems to you, no matter how obvious
    it seems to you, your explanation is always more
    than they really want to know!!!

Certified Modeling and Simulation Professional
Exam Primer Content Prep Material
Interoperability Architectures
Content Prep/Technology/Architectures
  • Interoperability
  • The ability of a model or simulation to provide
    services to, and accept services from, other
    models and simulations, and to use the services
    so exchanged to enable them to operate
    effectively together
  • Why?
  • Allow training with systems that were not
    developed together
  • Standardize interface techniques among multiple
  • Reuse, reuse, reuse!

Milestones of Interoperability
Content Prep/Technology/Architectures
  • Simulator Networking (SIMNET)
  • Collective tank training through identical
    devices and a common message protocol
  • Distributed Interactive Simulation (DIS)
  • Collective vehicle or entity training through a
    standard message protocol
  • Aggregate Level Simulation Protocol (ALSP)
  • Distributed staff training through message
    protocol, time synchronization, and a common
    object model
  • High Level Architecture (HLA)
  • Interoperability services for simulations
    regardless of their level of representation and
  • Test and Training Evaluation Network Architecture
  • Common Training Instrumentation Architecture

Content Prep/Technology/Architectures
  • Project initiated by DARPA in 1983
  • Heavy focus on networking and graphics
  • Applied selective fidelity concept to reduce
  • Initially provided collective training
    environment for tank crews, later extended to
    accommodate aircraft and air defense simulators
  • Still in use today (National Guard, et al.)

Content Prep/Technology/Architectures
Training Audience
  • Autonomous Simulation Nodes
  • Local event and time control
  • No knowledge of other simulators
  • Transmission of Ground Truth
  • Messages contain truth, degradation at the
  • Transmit State Change Only
  • Publish changes and Heartbeats
  • Dead Reckoning Algorithms
  • Common library of methods
  • Time Constraints
  • 100ms lag is desirable, 300ms lag is tolerable

Training Audience
Role Player
Content Prep/Technology/Architectures
JTC-supported CPX architecture
  • Based on the application of SIMNET and DIS
    principles applied to constructive simulations
  • ASCII-based message passing protocol
  • Coordinates advance of simulation time, enforces
    adherence to common object model, arbitrates
    contests over rights to modify shared state
  • Fielded in 1991, the ALSP Joint Training
    Confederation (JTC) supports several annual
    Command Post Exercises

Air Force
Electronic Warfare
Space EW
Response cells
Training audience
What is the High Level Architecture?
Content Prep/Technology/Architectures
  • The High Level Architecture (HLA) is comprised of
    three elements
  • An Interface Specification which describes the
    way compliant simulations interact during
  • An Object Model Template (OMT) Specification
    which specifies the form in which simulation
    elements are described
  • A set of HLA Rules for Federates and Federations
    which define relationships among federating
    compliant simulations
  • These three elements, commonly applicable across
    all U.S. DoD simulations, provide a common
    framework within which specific system
    architectures can be defined

Some HLA Terminology
Content Prep/Technology/Architectures
  • Federation a named set of federate applications
    and a common federation object model that are
    used as a whole to achieve some specific
  • Federation Execution The actual operation, over
    time, of a set joined federates that are
    interconnected by a RunTime Infrastructure (RTI).
  • Federate a member of a federation one
  • Could represent one platform, like a cockpit
  • Could represent an aggregate, like an entire
    national simulation of air traffic flow
  • Could represent a support application like a
    stealth viewer or a data collector

Some More HLA Terminology
Content Prep/Technology/Architectures
  • Object An entity in the domain being simulated
    by a federation that is of interest to more than
    one federate and is handled by the RTI.
  • Interaction An explicit action taken by a
    federate that may have some effect or impact on
    another federate within a federation execution.
  • Attribute A named characteristic of an object
    class or object instance.
  • Parameter A named characteristic of an

Functional View of the High Level Architecture
Content Prep/Technology/Architectures
Live Participants
Data Collectors, Passive Viewers, etc.
Simulation Surrogates
C Java Ada-95 CORBA IDL
Federation Execution Data
Interface Specification
Runtime Infrastructure (RTI)
Federation Management Declaration
Management Object Management Ownership
Management Logical Time Management Data
Distribution Management
What Does the Interface Specification Include?
Content Prep/Technology/Architectures
  • Six HLA Runtime Infrastructure Service Groups
  • Federation Management (20 services)
  • Declaration Management (12 services)
  • Object Management (17 services)
  • Ownership Management (16 services)
  • Time Management (23 services)
  • Data Distribution Management (13 services)
  • The Interface Specification also includes
  • Support Services (29 services)
  • Management Object Model
  • Federation Execution Data (FED)
  • Application Programmers Interfaces (APIs)

HLA RTI Services CategoriesSlide courtesy of DMSO
Content Prep/Technology/Architectures
Create and delete federation executions Join and
resign federation executions Control checkpoint,
Federation Management
Establish intent to publish and subscribe to
object attributes and interactions
Declaration Management
Create and delete object instances Control
attribute and interaction publication Create and
delete object reflections
Object Management
Transfer ownership of object attributes
Ownership Management
Coordinate the advance of logical time and its
relationship to real time
Time Management
Supports efficient routing of data
Data Distribution Mgmt
Random Number Generation
Content Prep/Technology/Computing and Networking
  • A random number generator (often abbreviated as
    RNG) is a computational or physical device
    designed to generate a sequence of numbers or
    symbols that lack any pattern, i.e. appear random
  • Computer-based systems for random number
    generation are widely used, but often fall short
    of this goal
  • They may meet some statistical tests for
    randomness intended to ensure that they do not
    have any easily discernible patterns
  • Pseudo-random number generators (PRNGs) are
    algorithms that can automatically create long
    runs (for example, millions of numbers long) with
    good random properties but eventually the
    sequence repeats exactly (or the memory usage
    grows without bound)
  • Random numbers uniformly distributed between 0
    and 1 can be used to generate random numbers of
    any desired distribution by passing them through
    the inverse cumulative distribution function
    (CDF) of the desired distribution

Computer Communication Techniques
Content Prep/Technology/Computing and Networking
  • The Internet protocol suite (commonly TCP/IP) is
    the set of communications protocols that
    implement the protocol stack on which the
    Internet and most commercial networks run
  • It is named for two of the most important
    protocols in it the Transmission Control
    Protocol (TCP) and the Internet Protocol (IP),
    which were also the first two networking
    protocols defined
  • The Internet protocol suitelike many protocol
    suitescan be viewed as a set of layers
  • Each layer solves a set of problems involving the
    transmission of data, and provides a well-defined
    service to the upper layer protocols based on
    using services from some lower layers

Sample encapsulation of data within a UDP
datagram within an IP packet
Content Prep/Technology/Computing and Networking
Computer Graphics Basics
Content Prep/Technology/Computing and Networking
  • Computer graphics broadly studies the
    manipulation of visual and geometric information
    using computational techniques
  • A broad classification of major subfields in
    computer graphics might be
  • Geometry studies ways to represent and process
  • Animation studies with ways to represent and
    manipulate motion
  • Rendering studies algorithms to reproduce light
  • Imaging studies image acquisition or image

Physical Phenomenology
Content Prep/Technology/Computing and Networking
Conceptual Modeling
Content Prep/Technology/Conceptual Modeling
  • A conceptual model captures ideas in a problem
  • The conceptual model is explicitly chosen to be
    independent of implementation details, such as
    concurrency or data storage
  • The aim of conceptual model is to express the
    meaning of terms and concepts used by domain
    experts to discuss the problem, and to find the
    correct relationships between different concepts
  • The conceptual model attempts to clarify the
    meaning of various usually ambiguous terms, and
    ensure that problems with different
    interpretations of the terms and concepts cannot
  • Such differing interpretations could easily cause
    the software projects that are based on the
    interpretation of the concepts to fail

Conceptual Modeling
Content Prep/Technology/Conceptual Modeling
  • A conceptual model can be described using various
    notations, such as UML or OMT for object
    modeling, or IE or IDEF1X for Entity Relationship
  • In UML notation, the conceptual model is often
    described with a class diagram in which classes
    represent concepts, associations represent
    relationships between concepts and role types of
    an association represent role types taken by
    instances of the modeled concepts in various
  • In ER notation, the conceptual model is described
    with an ER Diagram in which entities represent
    concepts, cardinality and optionality represent
    relationships between concepts

(No Transcript)
Human-System Interfaces (HSI)
Content Prep/Technology/Human-related Issues
  • To work with a system, users have to be able to
    control the system and assess the state of the
  • The term Human-System Interface is often used in
    the context of computer systems and electronic
  • The user interface of a mechanical system, a
    vehicle or an industrial installation is
    sometimes referred to as the Human-Machine
    Interface (HMI)
  • HMI is a modification of the original term MMI
    (Man-Machine Interface)
  • In practice, the abbreviation MMI is still
    frequently used although some may claim that MMI
    stands for something different now
  • Another abbreviation is HCI, but is more commonly
    used for Human-computer interaction than
    Human-computer interface
  • Yet another term used is Operator interface
    console (OIC)

Content Prep/Technology/Human-related Issues
  • Haptic, from the Greek ?f? (Haphe), means
    pertaining to the sense of touch (or possibly
    from the Greek word ?ptes?a? haptesthai meaning
    contact or touch)
  • Haptic technology refers to technology which
    interfaces the user via the sense of touch by
    applying forces, vibrations and/or motions to the
  • This mechanical stimulation may be used to assist
    in the creation of virtual objects (objects
    existing only in a computer simulation), for
    control of such virtual objects, and to enhance
    the remote control of machines and devices
  • This emerging technology promises to have wide
    reaching applications
  • Haptic technology has made it possible to
    investigate in detail how the human sense of
    touch works, by allowing the creation of
    carefully-controlled haptic virtual objects

Haptics Example
Content Prep/Technology/Human-related Issues
With CyberGrasp you formly reach into your 
computer and grasp computer-generated or
tele-manipulated objects. The CyberGrasp is a
lightweight, force-reflecting exoskeleton that
fits over a CyberGlove and adds resistive force
feedback to each finger With the CyberGrasp
force feedback system, users are able to feel the
size and shape of computer-generated 3D objects
in a simulated virtual world
Modeling Methods
Content Prep/Technology/Mathematics
  • There are many types of modeling methodologies
  • Ideally, understand which methodologies solve
    various problem types
  • Distinct phenomenology frequently have more than
    one way, or method, of being modeled
  • What are some of the more common modeling

Modeling Methods
Content Prep/Technology/Mathematics
  • The intended use of a system in which a model is
    a component is important information
  • Usage can be used to select which of several
    different techniques represents a given phenomena
  • Understanding and documenting the assumptions
    required for a model to be a valid representation
    of a phenomena is not a mature practice

Modeling Methods
Content Prep/Technology/Mathematics
  • Four Major Types
  • Internal Processes
  • External Processes
  • Internal Events
  • External Events
  • Intermix of all four is required
  • Implementing in a scalable manner is key

Internal Processes Analogy The Heart Beat
Content Prep/Technology/Mathematics
  • Atria pump blood to ventricles, which contract
  • Nonstop contractions are driven by the heart's
    electrical system

Internal Process Synchronous or Asynchronous
Intrincsic Capabilities
External Processes Analogy Pacemaker
Content Prep/Technology/Mathematics
  • External process monitors and interacts with an
    object (i.e., a pacemaker monitors the hearts
  • The electric current makes the heart beat within
    a certain range

External Process Synchronous or Asynchronous
Monitor and Control
Internal Events Analogy Heart Attack
Content Prep/Technology/Mathematics
  • Internal occurrence without pre-established time
  • Certain factors cause the occurrence. Blood flow
    is restricted, or the nerve system, which
    controls the heart, malfunctions

Internal Occurrence Irregular Time Scale
Intrinsic Capabilities
External Events Analogy Defibrillation
Content Prep/Technology/Mathematics
  • External event changes a passive objects state
    (i.e., a defibrillator is used for resuscitation)
  • External electrical shock is applied to the heart
  • Foundational representation method

External Occurrence Irregular Time Scale
Monitor and Control
Content Prep/Technology/Physics
  • The choosing appropriate representation of
    relevant physics is important
  • The data requirements to support high fidelity
    physics can be daunting, but necessary
  • Understand what factors influence the choice of
    the physics representations

F ma and Physics Different Levels of
Content Prep/Technology/Physics
Heading, Speed, Altitude
3-dimensional space position, velocity, even
Aerodynamic quantities (e.g., lift drag),
thrust, stick responses, etc
Joint Data Alternatives November 21, 2006
Content Prep/Applications/DES
  • Discrete-Event Simulation Model
  • Stochastic some state variables are random
  • Dynamic time evolution is important
  • Discrete-Event significant changes occur at
    discrete time instances
  • Monte Carlo Simulation Model
  • Stochastic
  • Static time evolution is not important

Components of DES
Content Prep/Applications/DES
  • Entity an object or component explicitly
    represented in a model permanent or temporary.
    E.g., customers and servers.
  • Attributes properties or characteristics of
    entities. E.g., priority of customers, average
    speed of servers.
  • Event an instantaneous occurrence that may
    change the state of the system. E.g., service
    completion, arrival event.
  • Simulation clock a variable whose value
    represents the simulated time.
  • List (or set) an ordered list of associated
  • Statistical counters to store statistical
    information about system performance.

Scheduling Algorithm
Content Prep/Applications/DES
Content Prep/Applications/DES
Content Prep/Applications/DES
Content Prep/Applications/Human Factors
  • Generally, it has been shown that expectancy can
    be influenced by
  • stimulus frequency,
  • response frequency,
  • stimulus repetitition, and
  • response repetition
  • Repetition Effect - when a person performs a task
    many times, repetition of the same stimulus or
    response tends to lead to faster performance.
  • when subjects are asked to perform a task under
    uncertain conditions, their judgment is usually
    influenced by expectation.
  • (Bertelson, 1961 Hyman, 1953 LaBerge and
    Tweedy, 1964)

Related Constructs
Content Prep/Applications/Human Factors
  • Mental Models - Deeply ingrained assumptions,
    generalizations, or even pictures or images that
    influence how we understand the world and how we
    take action.
  • Cues - The stimuli of human simulator operator
    visual, aural, haptic or kinesthetic sensors
    which emanate in Synthetic Natural or Operational
    Environments (eg changes in visual displays,
    moving platforms, etc).

Live, Virtual, Constructive
Content Prep/Applications/Interactive MS
Report of the Defense Science Board Task Force
on Simulation, Readiness and Prototyping January,
LVC(DoD 5000.59-M, January 1998)
Content Prep/Applications/Interactive MS
  • Live, Virtual, and Constructive Simulation. A
    broadly used taxonomy for classifying simulation
    types. The categorization of simulation into
    live, virtual, and constructive is problematic,
    because there is no clear division between these
    categories. The degree of human participation in
    the simulation is infinitely variable, as is the
    degree of equipment realism. This categorization
    of simulations also suffers by excluding a
    category for simulated people working real
    equipment (e.g., smart vehicles).
  • Live Simulation. A simulation involving real
    people operating real systems.
  • Virtual Simulation. A simulation involving real
    people operating simulated systems. Virtual
    simulations inject human-in-the-loop in a central
    role by exercising motor control skills (e.g.,
    flying an airplane), decision skills (e.g.,
    committing fire control resources to action), or
    communication skills (e.g., as members of a C4I
  • Constructive Model or Simulation. Models and
    simulations that involve simulated people
    operating simulated systems. Real people
    stimulate (make inputs) to such simulations, but
    are not involved in determining the outcomes.

Simulator Sickness
Content Prep/Applications/Interactive MS
  • Motion Sickness - Group of unpleasant symptoms
    experienced when the brain receives conflicting
    visual and motion cues.
  • Cue Conflict - occurs when there is a disparity
    between senses or within a sense. The two
    primary conflicts thought to be at the root of
    simulator sickness occur between the visual and
    vestibular senses. In a fixed-base simulator,
    the visual system senses motion while the
    vestibular system senses no motion. Thus,
    according to the cue conflict theory, a conflict
    results. In a moving-base simulator, the visual
    stimuli experienced may not correspond exactly to
    the motion which the vestibular system registers.
    Thus, a conflict can still result.

Game Theory
Content Prep/Applications/Operations Research
Game Theory is the branch of mathematics in which
games are studied that is, models describing
human behavior.
  • Classes of games
  • Symmetric game
  • Perfect information
  • Dynamic game
  • Repeated game
  • Signaling game
  • Cheap talk
  • Zero-sum game
  • Mechanism design
  • Stochastic game
  • Nontransitive game
  • Theorems
  • Minimax theorem
  • Purification theorems
  • Folk theorem
  • Revelation principles
  • Arrows theorem
  • Games
  • Prisoners dilemma, Travelers dilemma,
    Volunteers dilemma
  • Coordination game
  • Chicken
  • Dollar auction
  • Battle of the sexes
  • Stag Hunt
  • Matching pennies
  • Ultimatum game
  • Minority game
  • Rock-paper-scissors
  • Pirate game, Dictator game
  • Public goods game
  • Bargaining problem
  • Blotto games
  • War of attrition

Queuing Theory
Content Prep/Applications/Operations Research
  • trade offs
  • cost of waiting
  • cost of providing service
  • inherent randomness
  • arrivals
  • service time
  • without randomness we have an engineering problem
    in capacity and scheduling
  • people waiting for tickets at a box office each
    gets his own ticket versus one person gets
    tickets for a group
  • in a bank for a teller with one line and several
  • at a grocery store in several check out lines,
    and jockeying
  • for medical treatment in a hospital emergency
  • machines waiting for repair
  • office copiers under maintenance contract
  • factory jobs waiting at several stages of
  • cars waiting to get on the freeway

Example Application of Queuing Theory
Content Prep/Applications/Operations Research
Content Prep/Applications/Operations Research
Different Kind of Queue Systems
Descriptions of Queuing Systems
Content Prep/Applications/Operations Research
  • calling population (finite, infinite)
  • system structure (servers, queues)
  • system discipline
  • order of service
  • FIFO
  • LIFO
  • priority
  • rank as a property of the arriving unit
  • triage priority assigned by the server
  • random
  • round robin
  • queue behavior
  • balk or reject
  • renege
  • jockey
  • collude
  • be patient
  • distribution of arrivals Poisson
  • distribution of service times exponential
  • notation A/B/s
  • A is a symbol for the type of arrival
  • B is a symbol for the type of service
  • s is the number of servers
  • additional parameters sometimes appear
  • standard symbols
  • G for a general distribution
  • M for Poisson arrivals or exponential service
    times exponentially distributed service time
    means time already spent does not change the
    remaining time expected
  • D for a constant distribution, that is,

E.g., G/M/1, M/M/1, M/D/1, M/M/1
Littles Law
Content Prep/Applications/Operations Research
  • Littles Law Mean number tasks in system mean
    arrival rate x mean response time
  • Applies to any system in equilibrium, as long as
    nothing in black box is creating or destroying

Visualizing Littles Law
Content Prep/Applications/Operations Research
1 2 3 4 5 6 7 8
J Shaded area 9 Same in all cases!
Content Prep/Applications/Operations Research
  • The average inter-arrival time is t 1/l and t
    is exponentially distributed.
  • The average service time is x 1/m and x is
    exponentially distributed.
  • Single Server
  • Solve
  • L average number in queuing system
  • Lq average number in the queue
  • W average waiting time in whole system
  • Wq average waiting time in the queue

Statistical Concepts Measures of Central
Content Prep/Applications/Quantitative Aspects of
  • Mean Arithmetic Average
  • Find the mean of 2, 3, 6, 8, 9
  • Median value which divides the distribution
    into exactly two halves (50 scores lie below the
    median and 50 scores lie above the median)
  • Find the median of 5, 8, 12, 3, 9
  • Find the median of 34, 29, 26, 37, 31, 34
  • Mode value which occurs the most frequently
  • Find the mode of 6, 7, 7, 3, 8, 5, 3
  • The number of home runs the Boston Red Sox hit in
    eight consecutive games were 2, 3, 0, 3, 4, 1, 3,
  • Whats the mean?
  • Whats the median?
  • Whats the mode?

Statistical Concepts Central Limit Theorem
Content Prep/Applications/Quantitative Aspects of
The central limit theorem states that given a
distribution with a mean µ and variance s², the
sampling distribution of the mean approaches a
normal distribution with a mean (µ) and a
variance s²/N as N, the sample size, increases.
The amazing and counter-intuitive thing about the
central limit theorem is that no matter what the
shape of the original distribution, the sampling
distribution of the mean approaches a normal
distribution. Furthermore, for most
distributions, a normal distribution is
approached very quickly as N increases. Keep in
mind that N is the sample size for each mean and
not the number of samples. Remember in a sampling
distribution the number of samples is assumed to
be infinite. The sample size is the number of
scores in each sample it is the number of scores
that goes into the computation of each mean.
Statistical Concepts Probability Distributions
Content Prep/Applications/Quantitative Aspects of
Great resource on probability distributions
Statistical Concepts Outliers
Content Prep/Applications/Quantitative Aspects of
  • An outlier is an observation that lies an
    abnormal distance from other values in a random
    sample from a population.
  • Possible sources of outliers
  • recording and measurement errors
  • incorrect distribution assumption
  • unknown data structure
  • novel phenomenon

Experimental DesignNO Design! (aka One Factor
at a Time)
Content Prep/Applications/Quantitative Aspects of
  • Requires excessive number of experiments to
    study the effects of all input factors
  • The optimum combination of all study variables
    may never be revealed (hit miss approach)
  • The interaction between factors can not be
  • Time and effort may be wasted by obtaining too
    much or too little data
  • Conclusions can be wrong or misleading

Experimental DesignAdvantages of Deliberate DOE
Content Prep/Applications/Quantitative Aspects of
1, 1
-1, 1
0, 1
0, 0
1, 0
-1, 0
0, -1
-1, -1
1, -1
  • Many factors can evaluated simultaneously
  • Helps to understand the interactive
    relationships of the study factors on performance
  • The optimum combination can be revealed with
    the DOE approach
  • Tremendous efficiency and cost savings can be

Statistical Concepts Hypothesis Testing
Content Prep/Applications/Quantitative Aspects of
  • In general, hypothesis testing involves the
    following steps
  • specify the Null Hypothesis (H0). For example H0
    Mean 0
  • specify the Alternative Hypothesis (HA). For
    example HA Mean ltgt 0
  • compute the (appropriate) test statistic based on
    the sample data. The sampling distribution (if
    the Null Hypothesis is true) is assumed to be
  • compute the Acceptance Region (Confidence
    Interval) or Rejection Region based upon the
    sampling distribution.
  • Accept or Reject the Null Hypothesis H0

Experimental Error Types Type I Type II
Content Prep/Applications/Quantitative Aspects of
The null hypothesis is rejected when it is in
fact true that is, H0 is wrongly rejected.
In a hypothesis test, a type II error occurs when
the null hypothesis H0, is not rejected when it
is in fact false.
Quantitative Aspects Confidence Intervals
Content Prep/Applications/Quantitative Aspects of
  • The concept of a confidence interval is quite
    difficult for beginning statistics students, and
    sometimes for beginning statistics teachers! For
    example, assume that our population parameter of
    interest is the population mean. What is the
    meaning of a 95 confidence interval in this
    situation? Many students want to say that a 95
    confidence interval means that there is a 95
    chance that the confidence interval contains the
    population mean. But any particular confidence
    interval either contains the population mean, or
    it doesnt. The confidence interval shouldnt be
    interpreted as a probability.
  • The correct interpretation is based on repeated
    sampling. If samples of the same size are drawn
    repeatedly from a population, and a confidence
    interval is calculated from each sample, then 95
    of these intervals should contain the population

Quantitative Aspects Data Types
Content Prep/Applications/Quantitative Aspects of
  • Koperski, K. (1998). Spatial databases. Retrieved
    May 30, 2002 from the World Wide Web
  • Krippendorff, K. (n.d.). Degree of freedom. Web
    Dictionary of Cybernetics and Systems On-Line.
    Retrieved January 22, 2002 from the World Wide
    Web http//pespmc1.vub.ac.be/asc/degree_freed.htm
  • Nyerges, T. L. (1997). Spatial databases as
    models of reality. Retrieved May 30, 2002 from
    the World Wide Web http//www.geog.ubc.ca/courses
  • Slone, M. (n.d.). 6-DOF contact dynamics
    simulation system. Retrieved January 22, 2002
    from the World Wide Web http//astrionics.msfc.na
  • Amery, J., and Streid, H. FLIGHT SIMULATION

Quantitative Aspects Numerical Integration
Content Prep/Applications/Quantitative Aspects of
  • How much paint would you need to give the Statue
    of Liberty a fresh coat? She is 151 feet tall and
    her waist is 35' across, so a first approximation
    is that it would require the same amount of paint
    you would need to paint a 151x35x35 room.
    Counting the four walls and the ceiling, that
    would make a surface area of 22,365 square feet.
    One gallon of paint covers about 350 square feet,
    so by this estimate, we might require 64 gallons
    of paint.
  • However, it may be more precise to estimate each
    piece on its own. We can approximate the parts
    below the neck with a 95 feet tall cylinder whose
    radius is 17 feet. The head is roughly a sphere
    of radius 15 feet. The arm is another 42' long
    cylinder with a radius of 6', the tablet is a
    24'x14'x2' box. Adding all the surface areas
    gives roughly 15385 square feet, which requires
    an approximate 44 gallons of paint.
  • To further improve our estimate, we would measure
    the folds in her cloth, how non-spherical her
    head really is and so on. This process is called
    numerical integration.
  • Riemann sums, Simpson's rule, Taylor polynomials,
    Euler's method
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