Certified Modeling and Simulation Professional Exam Primer

1
Certified Modeling and Simulation Professional
Exam Primer
Amy Henninger Ed Degnan Jeff Wallace
2
Tutorial Objectives

• Understand CMSP Processes for certification and
  re-certification
• 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.

http//www.simprofessional.org/
3
Tutorial Outline

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

http//www.simprofessional.org/
4
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.

5
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.

6
Taking the Exam Who

• Education plus Work Experience must be no less
  than
• 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

7
Taking the Exam What

• Currently only baseline certification is
  available
• 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

8
Taking the Exam Where and When

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

9
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
  advantage.
• How Certification process is detailed at
  http//www.simprofessional.org/certprocess.html

10
CMSP FAQs

• 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
  deadlines.
• 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
  exam.
• Who grades the certification exam? Your
  (anonymous) exam will be reviewed by at least two
  members of the Certification Board.

11
Official CMSP References

• Badler, N.I. et. al. Simulating Humans Computer
  Graphics Animation and Control, 1993. human
  behavior
• 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.
  Manufacturing
• Cellier F.E., Continuous System Modeling.
  Springer-Verlag, 1991. continuous, advanced,
  graduate
• 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
  techniques
• Fujimoto, R.M., Parallel and Distributed
  Simulation Systems, Wiley Series on Parallel and
  Distributed Computing, 1999. parallel,
  distributed
• 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.
  modeling
• 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,
  http//www.cs.iastate.edu/baojie/acad/reference/2
  003-01-27_emss.htm

12
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.
13
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
above
14
Proposed Approach and Timeline to Develop Revised
MS Certification Test (in consideration)
15
MS Certification Overview
MS Application Certification
Prerequisite for Application or Management
Certification
Baseline MS Certification
MS Management Certification
16
MS Certification Overview
Application
Exam 100 Multiple Choice Questions
One year in a MS Position
Award MS Professional Credential
Yearly PU CEU Requirement
Renewal Every X Years
17
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
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MS Certification Overview
MS Management Example
KNOWLEDGE AREA
Management
Competencies
Basic Concepts
 
 
 
Understand historic perspective of MS
 
 
 
Historic Aspect of MS
1.33
1
Understand the historic aspects of MS and common
threads that are still valid today
DoD/Military Simulations
 
 
 
Modeling Concepts
 
 
 
Model Types
 
 
 
Model Definition
2.36
2
Know the definition for model
Model Concept
2.98
3
Determine information (and amount) required to
develop a model
Physical Models
2.91
3
Define a physical model and apply principles to a
given situation
Mathematical Models
2.91
3
Define a mathematical model and apply principles
to a given situation
Process Models
2.47
2
Define a process model and provide examples
Combination Models
2.41
2
Define combination models and provide examples
Testable
Experiential
19
Baseline MS Certification Total of 100
Questions from a 500 Question Bank
20
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
Testing
50 Questions 250 Question Bank
Developmental Testing
25 Questions 125 Question Bank
Business
Education
25 Questions 125 Question Bank
Engineering
Core Topics 25 Questions 125 Question Bank
25 Questions 125 Question Bank
Mathematics
25 Questions 125 Question Bank
Education
50 Questions 250 Question Bank
Medical Administration
25 Questions 125 Question Bank
Medical
Physicians
Core Topics 25 Questions 125 Question Bank
Primary Care
25 Questions 125 Question Bank
Nurses
SME Support will be Required to Design the
Structure and Questions for Each Area
21
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
bank
Cost Management for MS 10 Questions - 50
Question bank
Risk Management for MS 5 Questions - 25 Question
bank
Scope Management for MS 5 Questions - 25
Question bank
Management of MS Workforce 5 Questions - 25
Question bank
22
Exam Track for MS Technology
23
MS Technology

• Architectures
• Computing and Networking
• Conceptual Modeling
• Human-related Issues
• Mathematics
• MS Paradigms
• Physics
• Visualization

24
Architecture(s)

• 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

25
Architecture(s)

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

26
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
  complexity

27
Conceptual Modeling

• Understanding what conceptual models are, and
  their use in MS
• What are the most common conceptual modeling
  techniques
• 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

28
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

29
Mathematics

• 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

30
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

31
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

32
Physics

• The principles underlying the evolution of the
  world must be approximated somehow in models and
  simulations
• 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

33
Visualization

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

34
Exam Track for MS Applications
35
Ways To Study A System
Simulation, Modeling Analysis (3/e) by Law and
Kelton, 2000, p. 4, Figure 1.1
36
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

37
Characterizing a Model
Does the model contain stochastic components?
38
Characterizing a Model
Is time a significant variable?
39
Characterizing a Model
Does the system state evolve continuously or
only at discrete points in time?

40
MS Applications

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

41
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

42
Operations Research

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

43
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

44
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

45
Human Factors in MS

• Expectancy
• Mental Models
• Cues

46
Interactive Modeling and Simulation

• Live, Virtual, Constructive
• Simulator Sickness

47
You must be a Certified Modeling and Simulation
Professional if.
48
You must be a Certified Modeling and Simulation
Professional if.

• youve ever represented a horse with a sphere
  because it makes the math easier.

49
You must be a Certified Modeling and Simulation
Professional if.

• you can remember 17 computer passwords, but you
  cant remember your anniversary.

50
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
  length.

51
You must be a Certified Modeling and Simulation
Professional if.

• every possible combination of 3 letters is a
  meaningful acronym to you.

52
You must be a Certified Modeling and Simulation
Professional if.

• youve ever calculated that the World Series
  actually diverges.

53
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

54
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.

55
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?

56
You must be a Certified Modeling and Simulation
Professional if.

• you daydream of Super Models made up entirely of
  planar surfaces.

57
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!!!

58
Certified Modeling and Simulation Professional
Exam Primer Content Prep Material
59
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
  systems
• Reuse, reuse, reuse!

60
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
  operation
• Test and Training Evaluation Network Architecture
  (TENA)
• Common Training Instrumentation Architecture
  (CTIA)

61
SIMNET
Content Prep/Technology/Architectures

• Project initiated by DARPA in 1983
• Heavy focus on networking and graphics
  technologies
• Applied selective fidelity concept to reduce
  costs
• 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.)

62
DIS
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
  receiver
• 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
63
ALSP
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

Army
Navy
CBS
RESA
AWSIM
Marines
Air Force
MTWS
AFSAF
ALSP
AMP
JQUAD
Electronic Warfare
LAD
PSM
Logistics
CSSTSS
TACSIM
Space EW
Intelligence
Response cells
Training audience
64
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
  operation
• 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

65
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
  objective.
• 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
  application
• Could represent one platform, like a cockpit
  simulator
• 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

66
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
  interaction.

67
Functional View of the High Level Architecture
Content Prep/Technology/Architectures
Federation
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
68
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)

69
HLA RTI Services CategoriesSlide courtesy of DMSO
Content Prep/Technology/Architectures
Category
Functionality
Create and delete federation executions Join and
resign federation executions Control checkpoint,
synchronization
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
70
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

71
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

72
Sample encapsulation of data within a UDP
datagram within an IP packet
Content Prep/Technology/Computing and Networking
73
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
  surfaces
• Animation studies with ways to represent and
  manipulate motion
• Rendering studies algorithms to reproduce light
  transport
• Imaging studies image acquisition or image
  editing

74
Physical Phenomenology
Content Prep/Technology/Computing and Networking
75
Conceptual Modeling
Content Prep/Technology/Conceptual Modeling

• A conceptual model captures ideas in a problem
  domain
• 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
  occur
• Such differing interpretations could easily cause
  the software projects that are based on the
  interpretation of the concepts to fail

76
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
  Modeling
• 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
  situations
• In ER notation, the conceptual model is described
  with an ER Diagram in which entities represent
  concepts, cardinality and optionality represent
  relationships between concepts

77
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78
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
  system
• The term Human-System Interface is often used in
  the context of computer systems and electronic
  devices
• 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)

79
Haptics
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
  user
• 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

80
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
81
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
  techniques

82
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

83
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

84
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
85
External Processes Analogy Pacemaker
Content Prep/Technology/Mathematics

• External process monitors and interacts with an
  object (i.e., a pacemaker monitors the hearts
  rhythm)
• The electric current makes the heart beat within
  a certain range

External Process Synchronous or Asynchronous
Monitor and Control
86
Internal Events Analogy Heart Attack
Content Prep/Technology/Mathematics

• Internal occurrence without pre-established time
  scale
• 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
87
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
88
Physics
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

89
F ma and Physics Different Levels of
Representation
Content Prep/Technology/Physics
Heading, Speed, Altitude
3-dimensional space position, velocity, even
tactics
Aerodynamic quantities (e.g., lift drag),
thrust, stick responses, etc
Joint Data Alternatives November 21, 2006
90
Definitions
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

91
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
  entities.
• Statistical counters to store statistical
  information about system performance.

92
Scheduling Algorithm
Content Prep/Applications/DES
93
Queuing
Content Prep/Applications/DES
94
Dequeing
Content Prep/Applications/DES
95
Expectancy
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)

96
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).

97
Live, Virtual, Constructive
Content Prep/Applications/Interactive MS
Report of the Defense Science Board Task Force
on Simulation, Readiness and Prototyping January,
1993
98
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
  team).
• 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.

99
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.

100
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

101
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
  tellers
• at a grocery store in several check out lines,
  and jockeying
• for medical treatment in a hospital emergency
  room
• machines waiting for repair
• office copiers under maintenance contract
• factory jobs waiting at several stages of
  production
• cars waiting to get on the freeway

102
Example Application of Queuing Theory
Content Prep/Applications/Operations Research
103
Content Prep/Applications/Operations Research
Different Kind of Queue Systems
104
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
  distribution
• B is a symbol for the type of service
  distribution
• 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,
  deterministic

E.g., G/M/1, M/M/1, M/D/1, M/M/1
105
Littles Law
Content Prep/Applications/Operations Research
System
Arrivals
Departures

• 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
  tasks

106
Visualizing Littles Law
Content Prep/Applications/Operations Research
Arrivals
Packet
Departures
1 2 3 4 5 6 7 8
Time
J Shaded area 9 Same in all cases!
107
M/M/1
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

108
Statistical Concepts Measures of Central
Tendency
Content Prep/Applications/Quantitative Aspects of
MS

• 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,
  0.
• Whats the mean?
• Whats the median?
• Whats the mode?

109
Statistical Concepts Central Limit Theorem
Content Prep/Applications/Quantitative Aspects of
MS
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.
110
Statistical Concepts Probability Distributions
Content Prep/Applications/Quantitative Aspects of
MS
Great resource on probability distributions
http//www.causascientia.org/math_stat/Dists/Compe
ndium.pdf
111
Statistical Concepts Outliers
Content Prep/Applications/Quantitative Aspects of
MS

• 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

112
Experimental DesignNO Design! (aka One Factor
at a Time)
Content Prep/Applications/Quantitative Aspects of
MS

• 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
  determined
• Time and effort may be wasted by obtaining too
  much or too little data
• Conclusions can be wrong or misleading

113
Experimental DesignAdvantages of Deliberate DOE
Factorials
Content Prep/Applications/Quantitative Aspects of
MS
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
  achieved

114
Statistical Concepts Hypothesis Testing
Content Prep/Applications/Quantitative Aspects of
MS

• 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
  known.
• compute the Acceptance Region (Confidence
  Interval) or Rejection Region based upon the
  sampling distribution.
• Accept or Reject the Null Hypothesis H0

115
Experimental Error Types Type I Type II
Errors
Content Prep/Applications/Quantitative Aspects of
MS
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.
116
Quantitative Aspects Confidence Intervals
Content Prep/Applications/Quantitative Aspects of
MS

• 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
  mean.

117
Quantitative Aspects Data Types
Content Prep/Applications/Quantitative Aspects of
MS

• Koperski, K. (1998). Spatial databases. Retrieved
  May 30, 2002 from the World Wide Web
  http//www.cs.sfu.ca/people/GradStudents/koperski/
  personal/research/spatialDB.html
• 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
  l
• 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
  /klink/gis.notes/ncgia/u10.html
• Slone, M. (n.d.). 6-DOF contact dynamics
  simulation system. Retrieved January 22, 2002
  from the World Wide Web http//astrionics.msfc.na
  sa.gov
• Amery, J., and Streid, H. FLIGHT SIMULATION
  VISUAL REQUIREMENTS and A NEW DISPLAY SYSTEM.
  http//www.rickleephoto.com/mosaicfresnel.htm

118
Quantitative Aspects Numerical Integration
Methods
Content Prep/Applications/Quantitative Aspects of
MS

• 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