Subject Description Form
Subject Code
ISE320
Subject Title
System Modeling and Applications
Credit Value
3
Level
3
Pre-requisite/Corequisite/Exclusion
Nil
Objectives
This subject will provide students with
Intended Learning
Outcomes
Subject Synopsis/
Indicative Syllabus
1.
terminologies of system, system classifications, and boundaries;
2.
types of basic information flow in an engineering system, data acquisition
techniques, and graphical programming;
3.
methodologies for improving a system;
4.
deterministic and probabilistic system modeling methodologies and
applications.
Upon completion of the subject, students will be able to
a.
identify a system with fundamental connecting entities and relations;
b.
apply appropriate techniques to examine a system and/or initiate a new
system design plan;
c.
understand various methods of modeling a system that involves
deterministic and probabilistic factors.
1.
System Definitions and Classification
Introduction to system definitions, system classification, and system
components
2.
Introduction to Static and Dynamic System Modeling Tools
Static System Modeling - IDEF0 (Input, Control, Output, Mechanism);
Dynamic System Modeling - Stella (Stock, Flow, Converter).
3.
Deterministic Modeling and Applications
Modeling of mechanical and electrical systems; Linear programming,
integer optimization, nonlinear optimization, and their applications in
modeling
4.
Probabilistic Modeling and Applications
Modeling with probabilistic inputs; Monte Carlo and event-driven
modeling; Applications of probabilistic modeling in business, medical,
18.3.2014
manufacturing, and information systems
Teaching/Learning
Methodology
The emphasis of this subject is on innovation and systematic application
aspects. Thus, an interactive approach is adopted to facilitate learning through
intensive laboratory exercises. Generally, the design of each laboratory
contains two main sections. The first section guides students through step-bystep instructions while the second requires students to make use of the
knowledge learned from the former section to perform a specific task with only
the provision of key technical details. Innovative thinking on improving
system activities is animated. Software packages such as Visio, Stella,
LabVIEW, and Visual Basic are used for problem solving.
Assessment Methods
in Alignment with
Intended Learning
Outcomes
Specific assessment
methods/tasks
%
weighting
Intended subject learning outcomes to
be assessed
a
b
c
1. Individual lab report
40%



2. Group lab report
30%



3. Test
30%



Total
100%
The individual lab report assesses if students can identify a system with
connecting entities and relations. It also assesses if they can examine a system
and initiate a new system design plan and whether they can apply deterministic
and probabilistic modeling methods in real case studies.
The group lab report evaluates if students can identify a system with
connecting entities and relations. It can also assess if they can examine a
system and initiate a new system design plan and whether they can apply
deterministic and probabilistic modeling methods in real case studies.
The test assesses if students can identify a system with connecting entities and
relations, and if they can examine a system and initiate a new system design
plan. Likewise, the test assesses if students understand deterministic and
probabilistic modeling methods and use them to solve real problems. The test
assesses if the students have learned how to analyze a simple system in a
limited time.
Student Study
Effort Expected
Class contact:

Lecture/Tutorial/Test
2 hours/week for 9 weeks
18 Hrs.

Laboratory/Case Study
3 hours/week for 7 weeks
21 Hrs.
Other student study effort:

18.3.2014
Individual lab reports
53 Hrs.

Test preparation
Total student study effort
Reading List and
References
18.3.2014
20 Hrs.
112 Hrs.
1.
Hughes, M 2000, Mastering Systems Analysis and Design, Macmillan
Press Ltd.
2.
Palm, W J 1999, Modeling, Analysis, and Control of Dynamic Systems,
New York: Wiley
3.
Kulkarni V G 1999, Modeling, Analysis, Design, and Control of
Stochastic Systems, New York: Springer
4.
Ghosh, S and Lee S 2000, Modeling and Asynchronous Distributed
Simulation: Analyzing Ccomplex Systems, New York: IEEE Press
5.
Evans, J R and Olson, D L 2002, Introduction to Simulation and Risk
Analysis, Prentice Hall, New Jersey