Download 1 M.Sc./P.G. Diploma Course in Industrial Automation Document 1

M.Sc./P.G. Diploma Course in Industrial Automation
Document 1: Eligibility Requirements
1.1 The degree of the Bachelor of Science of Engineering of University of
Moratuwa in a relevant field of specialization; the relevancy of the field to be
judged by the Faculty and approved by the Senate of University of Moratuwa.
OR
1.2 Any other Engineering degree of at least four years duration, in a relevant field
of specialization, from a recognized university; the recognition of the university, the
acceptability of the course, and the relevancy of the field to be judged by the
Faculty and approved by the Senate of University of Moratuwa.
OR
1.3 Any other Science degree (preferably with Mathematics or Physics as a subject) of
at least four years duration from a recognized university, AND a minimum of one
year of appropriate experience in a relevant field after obtaining such degree; the
recognition of the university, the acceptability of the course, and the relevancy of
the experience to be judged by the Faculty and approved by the Senate of
University of Moratuwa.
OR
1.4 Any other Science degree (preferably with Mathematics or Physics as a subject) of
at least three years duration from a recognized university, AND a minimum of two
years of appropriate experience in a relevant field after obtaining such degree; the
recognition of the university, the acceptability of the course, and the relevancy of
the experience to be judged by the Faculty and approved by the Senate of
University of Moratuwa.
OR
1.5 At least the Associate Membership (satisfying the educational requirements for
Corporate Membership or similar graduate membership) of a recognized
professional engineering institute in a relevant field AND a minimum of one year of
appropriate experience after obtaining such membership; the acceptability of the
Associate Membership status of the candidate, the recognition of the institute and
the relevancy of the field for this purpose shall be judged by the Faculty and
approved by the Senate of University of Moratuwa.
1
Document 2: Curriculum and Scheme of Evaluation
Curriculum of Postgraduate Course
Code
Credits1
Course Unit
Evaluation2 (%)
Final Exam
Assignments
Core Modules
EE 5061
State Space Design
2.5
60±20
40±20
EE 5062
Digital Control
2.5
60±20
40±20
EE 5201
Sensors
Systems
2.5
60±20
40±20
EE 5202
Modern Power Electronics and Drives
2.5
60±20
40±20
ME 5124
Automation and Control of Manufacturing
Systems
3.0
60±20
40±20
ME 5144
Mechatronics and Robotics
3.0
60±20
40±20
ME 5201
System Automation
3.0
60±20
40±20
ME 5202
Advanced
Controls
3.0
60±20
40±20
and
Actuators
Engineering
for
Automatic
Mathematics
for
Elective Modules
EE 5071
Microprocessor Based Systems
2.5
60±20
40±20
EE 5073
Computer Networks
2.5
60±20
40±20
EE 5074
Internet Applications
1.5
60±20
40±20
EE 5075
Artificial Intelligence Applications (or ME
5145)
2.5
60±20
40±20
EE 5081
Operations Research
2.5
60±20
40±20
EE 5082
Numerical methods (or ME 5001)
2.5
60±20
40±20
EE 5084
System Identification and Modelling
1.5
60±20
40±20
EE 5203
Robotics Technology
2.5
60±20
40±20
EE 5204
Hardware and Software for Systems Control
2.5
60±20
40±20
EE 5086
Project Management
1.5
60±20
40±20
EE 5087
Human Resource Management (or ME
5146)
1.5
60±20
40±20
ME 5103
Computer Integrated Manufacturing
3.0
60±20
40±20
ME 5121
Industrial Management (or ME 5122)
3.0
60±20
40±20
ME 5122
Quality Management (or ME 5121)
3.0
60±20
40±20
ME 5123
Supply Chain Management
3.0
60±20
40±20
ME 5125
Manufacturing Process: Advanced Concepts
3.0
60±20
40±20
ME 5145
Artificial Intelligence in Manufacturing (or
EE 5075)
3.0
60±20
40±20
2
ME 5146
Human Factors in Engineering (or EE 5087)
3.0
60±20
40±20
ME 5001
Mathematical Techniques (or EE 5082)
3.0
60±20
40±20
ME 5220
Machine Intelligence and Robotics
3.0
60±20
40±20
ME 5290
Research Seminar
3.0
-
100
Project (only for those who plan for PGDip)
10
-
100
EE 5099 or
ME 5299
Code
EE 6099 or
ME 6299
1
2
Evaluation
Module
Dissertation
Exam.
Assignments
Viva
Thesis
1 credit corresponds to 14 hours of lectures or equivalent
The mean value in the evaluation scheme is the default value. It can be changed by the Lecturer/Examiner
concerned, within the specified range, by announcement to the students at the commencement of the course unit.
3
Document 3: Syllabi of Course Units
3.1 EE 5061 – State Space Design (2.5 credits):
Learning Objectives: To provide the necessary exposure to design a control system using
state space design methodology.
Outline Syllabus: Linear Systems theory, Concept of state, Linear differential equations,
State transition matrix for linear time-invariant and time-variant systems, Controllability,
Observability, Duality, Canonical forms, Input/output models, State feedback and modal
control design, State observers and their design, Optimal controller design.
3.2 EE 5062 - Digital Control (2.5 credits):
Learning Objectives: To provide the students with an understanding of the fundamentals of
Digital Control theory and the theoretical and practical principles for design. To provide
guidelines of current trends in the field.
Outline Syllabus: Basics of sampled data systems, Samplers, Data holds and digital
compensators, The z-transform and extended z-transform, Block representation of sampled
data feedback systems, Stability analysis via the root locus and Nyquist plot techniques,
Compensator design for deadbeat response. Practical system designs.
3.3 EE 5201 - Sensors and Actuators for Automatic Systems (2.5 credits):
Learning Objectives: To enhance the knowledge and understanding of sensors and
actuators for automating systems.
Outline Syllabus:
Sensors: Digital sensors, analog sensors, and sensor specifications.
Actuators: An introduction to different types of actuators including servomotors, dc motors,
ac motors, grippers, manipulators, and linear actuators.
Data sampling, A/D, D/A, Interfacing and systems development using sensors and
actuators.
3.4 EE 5202 - Modern Power Electronics and Drives (2.5 credits):
Learning Objectives: To enhance the knowledge and understanding of commonly used drive
systems and their power electronics control in automation applications.
Outline Syllabus: Adjustable voltage dc-dc converting systems of different types, switch
mode and resonant mode. Adjustable voltage and adjustable frequency dc-ac converting
systems of different types.
Drive systems of brushless dc, conventional dc, induction steppers, switch reluctance,
linear etc. and their power electronics applications. Design aspects of overall drive systems.
Open loop and closed loop operation of position and speed drives, improvement of
bandwidth.
3.5 ME 5124 - Automation and Control of Manufacturing Systems (3.0 credits):
Learning Objectives: To provide the knowledge and skills required automating and
controlling the functions of the manufacturing process.
Outline Syllabus: Planning and implementation of Automation; Automated assembly;
Automated materials flow and storage systems; Control theory; PID controllers;
Programmable Logic Controllers (PLC’s); Adaptive control in manufacturing; Hierarchical
control concepts; Hardware and software process integration; Instrumentation; Maintenance
and diagnosis; Integration of machine tools.
4
3.6 ME 5144 - Mechatronics and Robotics (3.0 credits):
(Pre-requisite: PME/MSE 205 - Automation and Control of Manufacturing Systems)
Learning Objectives: To introduce students to the engineering and management techniques
of the design and development process of a mechatronics product/system
Outline Syllabus: Introduction of mechatroncis; Revision of basic mechanics, electricity and
electronics, Introduction to programming; Study of components of a mechatronics system
such as stepper motors, A/D converters, Op-amps. solid-state devices; Conceptual design
of a mechatronics system; Implementation of robot systems; Robot programming; Machine
vision.
3.7 ME 5201 - Systems Automation (3.0 credits):
Learning Objectives: Understanding of system automation for a given requirement
Outline Syllabus: Introduction to system automation, hardware and software for general
automation, micro-controller based system design, plant automation for large-scale
factories, and Industrial applications in automation.
3.8 ME 5202 - Advanced Engineering Mathematics for Controls (3.0 credits):
Learning Objectives: To provide the mathematical knowledge and skills required for
developing control systems.
Outline Syllabus: Linear matrix inequalities (LMIs), Stability analysis of dynamic systems,
Adaptive filtering, Prediction and correction methods, Numerical methods for control
algorithms, Vector spaces, Riccati equation, Fractals and chaos, Conformal mapping,
Applications on the above topics
3.9 EE 5071 - Microprocessor Based Systems (2.5 credits):
Learning Objectives: Understand the Principles and Technologies used in microprocessorbased systems
Outline Syllabus: Uniprocessor, coprocessor and multiprocessor systems, RISC and CISC
architectures. Digital Data Manipulation: Error codes, parity, Hamming code. Computer
Organization and Control: Polling, interrupts, DMA, bus control, and priority levels.
Peripheral Devices and data communication standards: RS232, IEEE488, VME. Operating
Systems and Memory Management: Virtual memory, compilers, linkers, interpreters,
network operating systems.
3.10 EE 5073 - Computer Networks (2.5 credits):
Learning Objectives: Understand the Principles and Technologies used in computer
networks
Outline Syllabus: Communication networks; LANs, WANs, MANs, Internet, Intranets,
protocols, layered architecture of networking; ethernet, token ring, token bus, X.25, Use of
modems
3.11 EE 5074 - Internet Applications (1.5 credit):
Learning Objectives: To provide the knowledge and skills for internet based applications.
Outline Syllabus: Overview of the Internet, Browser, Client and Server. Introductory-level
overview of the technologies involved in building a web application. (No previous
experience with web design or HTML is assumed.) Creating a Web-page - Basic Document
Structure. Hand coded HTML: tags and tag attributes, Lists, Images, Hyperlinks, Tables,
Forms, Links and Anchors. Document Header, Meta tags. Frames. Class information, Style
sheets. Authoring Tools. HTML Validation. Exercise: Creating an interactive web-page for
ones-self.
5
3.12 EE 5075 - Artificial Intelligence Applications [or ME 5145] (2.5 credits):
Learning Objectives: Understanding of artificial intelligence (AI) and apply AI techniques in
real world problems.
Outline Syllabus: Background, Natural and Artificial Intelligence, Turing Test, Applications in
AI, Future of AI.
AI Agent, PEAS (Performance-Environment-Actuators-Sensors), Hardware and Software
agents, Simple reflex agent, Learning Agent for complex systems.
Search Algorithms, Depth First Search (DFS), Hill Climbing, Constraints, Quasigroup
Completion Problems (QCP), Genetic Algorithms (GA): Population, Crossover, Mutation
and other parameters.
Introduction to FL, Applications, Why Use Fuzzy Logic, Fuzzy Sets, Mamdani-type and
Sugeno-type fuzzy systems, Development of an AI Agent using Fuzzy reasoning: fuzzy
operators and Implication Methods, Takagi- Sugeno Fuzzy Model.
Neural network architectures and learning rules, Back propagation, Application of neural
networks
Knowledge based systems and expert systems
Artificial Intelligence applications
3.13 EE 5081 - Operations Research (2.5 credits):
Learning Objectives: To enable graduates to play an effective role in providing decision
support to managers
Outline Syllabus: Linear and dynamic programming, Sensitivity analysis, Network analysis,
Integer programming.
3.14 EE 5082 - Numerical Methods [or ME 5001] (2.5 credits):
Learning Objectives: To enable graduates to apply numerical methods to solve real world
problems.
Outline Syllabus: Fast Fourier Transforms (FFT), Numerical methods for solving elliptic
equations, Finite difference methods, Finite element methods and variational methods,
Modal matrix analysis,
3.15 EE 5084 - System Identification and Modeling (1.5 credit):
Learning Objectives: To provide the knowledge for analyzing systems for control and
automation purposes.
Outline Syllabus: Deterministic modeling, Data analysis and sampling, Windowing,
Parametric and non-parametric spectral analysis, Off-line system identification, On-line
system identification, AR, ARX, ARMA, ARMAX modeling, Model order determination,
Model validation, Prony signal and transfer function identification techniques.
3.16 EE 5203 - Robotics Technology (2.5 credits):
Learning Objectives: To introduce students to the fundamentals on robotics and provide
them with essential knowledge about theoretical & practical background on robotics, so that,
in the future, the students will be able to readily apply their knowledge in industry or
research or further enhance it by self study.
Outline Syllabus: Spatial Descriptions and Transformations, Manipulator Kinematics and
Inverse Kinematics, Force/velocity propagation along manipulator links, Manipulator
dynamics, Trajectory Planning
Mechanical design of robot manipulators, Robot manipulator control, Robot system
integration and programming, Robot sensors and actuators, Robot vision, Sensor fusion.
6
3.17 EE 5204 - Hardware and Software for Systems Control (2.5 credits):
Learning Objectives: To familiarize with standard software and hardware components related
to systems control and automation.
Outline Syllabus: Examples of computer controlled systems, Basic sampling theory, D/A and
A/D conversion, Hardware components of data acquisition, Device interfacing, control
registers, instruction set and assembler programming for DAQs, Concurrent programming
for on-line control, real-time executives and applications, Distributed systems.
3.18 EE 5086 - Project Management (1.5 credit):
Learning Objectives: To provide the necessary exposure to the application of the project
management knowledge, skills, tools and techniques to meet or exceed stakeholder needs
and expectations.
Outline Syllabus: Definition of Project Management and relationship to other management
disciplines. Project Appraisal: Financial, technical, environmental etc. Project Management
Context: Project phases and project life cycle, project strategy development, project stake
holders. Organizational and socio-economic influence, key management skills, project
initiation and modelling, project management process, project procurement management
and project scheduling. Project cost estimation and control, project quality management,
project risk management, project assessment and stakeholder marketing and case studies.
3.19 EE 5087 - Human Resource Management (1.5 credit):
Learning Objectives: To appreciate the role of Human Resource Management in an
organization and it’s various facets and to evaluate HRM practices in an organization.
Outline Syllabus: Human resources planning, Job Analysis and Job design, Recruitment &
Selection, Training & Development, Managing Performance, Reward Management, Human
Resource Information Systems, Strategic Human resource management, Managing Labour
Relations.
3.20 ME 5103 - Computer Integrated Manufacturing (3.0 credits):
Learning Objectives: To acquaint the student with the integration of the elements of CAD
and CAM and the integration of manufacturing functions.
Outline Syllabus: Integrated approach in manufacturing systems; Concurrent engineering;
Production process design; Group technology; Computer aided process planning;
Production management aspects of CIM; Implementation of CIM; Flexible manufacturing
systems (FMS); Factory of future; Communication networks, DBMS, Interfacing, Data
logging and acquisition.
3.21 ME 5121 - Industrial Management
[or ME 5122] (3.0 credits):
Learning Objectives: To provide the general management background needed by the
manufacturing executive for effectively participating in the total business environment of a
firm.
Outline Syllabus: Human factors in industry: Organization structure, Cultures and
management styles, leadership and characteristics of individuals, group behavior,
Manpower requirements and skill needs- selection recruitment and training, Motivation,
Rewards strategies- reward and job evaluation, Implementing changes in organization;
Financial decision making: Financial analysis, Profitability analysis, and Investment
appraisal;
Marketing: Concepts and importance of marketing, Marketing system, Market types,
Marketing research and analysis, Managing marketing mix, Four P’s (Product, Price, Place,
Promotion)
7
3.22 ME 5122 - Quality Management [or ME 5121] (3.0 credits):
Learning Objectives: To present quality as a strategic tool for competitiveness and to provide
knowledge of the ways and means to achieve quality in manufacturing.
Outline Syllabus: Quality management systems: Quality management philosophies, Total
Quality Management, Quality awards (Malcom Baldrige award, National Quality award),
Quality certification (ISO 9000, SLS) HRM for quality, Vendor quality; Statistical quality
control: Statistical methods in QC, Statistical process control, Acceptance sampling,
Taguchi method, Product reliability; Design for quality, Quality function deployment.
3.23 ME 5123 - Supply Chain Management (3.0 credits):
Learning Objectives: To support the globalisation trends in manufacturing systems by
developing the management know-how required by the students aspiring to work in
international business.
Outline Syllabus: The concept and structure of Global Supply Chain; International trade;
Supply organisation; The procurement process; Distribution management; Transportation
systems; Enterprise resource planning; Management and organisation of information
systems; Strategic considerations.
3.24 ME 5125 - Manufacturing Processes: Advanced Concepts (3.0 credits):
Learning Objectives: To provide an overview (and a review) of the manufacturing methods
for metallic and non-metallic materials covering their critical aspects and with reference to
local manufacturing industry.
Outline Syllabus: An overview of local manufacturing industry; Mechanical behaviour and
manufacturing properties of materials; Casting Technology: Metals and melting practice,
Design aspects of product/mould, product quality; Forming Technology: General
characteristics, bulk forming vs sheet-metal working, formability, analysis of process with
respect to machinery and tooling, forming loads and energy requirements, product quality
concerns; Joining Processes (for metals): Process classification, fusion weld quality and
weld metallurgy, testing and inspection; Forming and shaping of fibre reinforced materials;
Processing of powder materials and ceramics: Sintering technology; Non-conventional
Machining Processes: CM, ECM, EDM, laser/electron beam machining, water/abrasive-jet
machining, etc.; Surface treatment and coating: methods and technology.
3.25 ME 5145 - Artificial Intelligence in Manufacturing [or EE 5075] (3.0 credits):
Learning Objectives: To gain an understanding of expert systems and AI techniques and
how it can be applied in control, automation, manufacturing, design and operational
management.
Outline Syllabus: Knowledge based systems, Expert systems, Fuzzy systems and fuzzy
control, Artificial Neural Networks, Genetic Algorithms, Selection of AI techniques, AI based
applications in control, automation, manufacturing, design and operations management.
3.26 ME 5146 - Human Factors in Engineering [or EE 5087] (3.0 credits):
Learning Objectives: To gain an understanding of the impact of human capabilities and
limitations on the design and development of products and equipment. To appreciate the
contemporary approaches to the design of safe and fatigue free work environments.
Outline Syllabus: The scope of human factors engineering and its relationship with product
design and manufacture; Systems Ergonomics; Human characteristics; Principles of
anthropometry;
Application of ergonomics to product design; Biomechanics and safety engineering;
Introduction to Ergonomics CAD in product design; Practical case studies taken from the
fields of consumer product design, work place and work design.
8
3.27 ME 5001 - Mathematical Techniques [or EE 5082] (3.0 credits):
Learning Objectives: To enable graduates to apply mathematical techniques to solve real
world problems
Outline Syllabus: Partial differential equations – classification, modelling, solutions using
Fourier series, Fourier transform; Numerical methods – finite difference methods, finite
element methods, solutions of ordinary and partial differential equations, applications using
computer software; Optimisation – Non-linear optimisation involving multivariate function,
dynamic programming; Methods of applied statistics – sampling, hypothesis tests, basic
ideas of linear models, time series modeling.
3.28 ME 5220 - Machine Intelligence and Robotics (3.0 credits):
Learning Objectives: To enable graduates to apply intelligent techniques to develop
intelligent machines.
Outline Syllabus: Introduction to Intelligent Systems, Hardware and software agents,
Kinematics and dynamics for controlling robots, Modeling and Control of Dynamical
Systems, Mobile robots and their autonomous navigation, Machine Learning, Intelligent
Systems Control Techniques like Behavior Based Controls, Neuro-Fuzzy controllers.
3.29 ME 5290 - Research Seminar (3.0 credits):
Learning Objectives: To develop the ability of students to do unsupervised work at graduate
level, especially on research paper reading and understanding related to automation.
Outline Syllabus: Critical reading of technical literature and summarizing contents. Verbal
communications and writing skill development. Adapting the speech and the written material
for the intended audience. Making Seminar type presentations as a tool for interpersonal
communication, projects presentation, public speaking, and report writing.
3.30 EE 5099 - Project (Only for those who plan for PGDip) (10 credits):
Learning Objectives: To allow students to apply skills gained in the course to a related
project. To gain the ability to learn and apply new ideas as needed to meet project goals.
Outline Syllabus: The student is expected to work individually to develop a given project at a
greater depth than in EE5199 and may be allowed to be done in lieu of courses in special
cases. They shall be carried out for a period of not less than three months, on a part time
basis (or equivalent period full time) under the supervision of a senior staff member. All
students must make a written and verbal presentation at the completion of the module.
3.31 EE 6099 - Dissertation (25 credits):
Learning Objectives: To allow students to apply skills gained in the course to a
multidisciplinary project. To develop specific skills in project definition, planning, and
scheduling, effective written and oral communication of technical ideas. To incorporate
realistic constraints and engineering standards.
Outline Syllabus: The student is expected to work individually on a research dissertation on
a topic assigned or agreed by the Department. It is to be carried out for a period of not less
than one academic year, on a part time basis (or equivalent period full time) under the
supervision of a senior staff member and/or industrial supervisors. The student is expected
to develop a complete plan from feasibility study, cost analysis, through electrical design
and documentation to the building of a prototype or developing of a model as applicable. All
students must make a formal written and verbal presentation to a panel.
Document 4: Performance Criteria
9
4a: For Postgraduate Diploma
4a.1
Title of the Award: Postgraduate Diploma in Industrial Automation
4a.2
Participation in Academic Program:
1. The candidate is required to have attended at least 80% in lectures, tutorial
classes, seminars and other components.
2. Undertake an individual project, as assigned by the Department, on a specific
subject area.
3. No postponement of the course is allowed without the prior approval of the Senate.
4a.3
Pass in the Postgraduate Examination:
1. A candidate is deemed to have passed the Postgraduate Examination if the
candidate has:
1.
successfully completed the required course units, including compulsories,
totalling a minimum of 40 credits
AND
2.
successfully completed the prescribed seminars
AND
3.
successfully completed all the prescribed assignments, laboratory work, AND
4.
successfully completed the prescribed project.
Note: In order to be considered successful and earn credit for the course unit, the
candidate must earn grade C or above. Where a course unit consists of more than
one component (written examination, seminars, laboratory work, assignments
etc) the pass mark for each component is 40%.
2. If the candidate is unsuccessful in any of the parts 1.(a) through 1.(d), he/she
may be re-examined. Normally only one re-examination will be allowed and this
shall be at the next holding of the examinations or assessments. No
postponement shall be allowed without approval from the Senate.
3. Classes will not be awarded.
4a.4
4a.5
Credit Rating: A credit is defined as one hour of Lectures per week for the
duration of one Semester which will usually be of 14 weeks duration. A Credit will
also be equivalent to about 2 hours of assignments per week for one semester.
Grading of Marks: Performance of the candidate in each course unit shall be
graded based on the following benchmarks:
Grade
Benchmark
Grade Point
Description
A+
>= 85%
4.2
A
75% - 84%
4.0
A-
70% - 74%
3.7
B+
65% - 69%
3.3
B
60% - 64%
3.0
B-
55% - 59%
2.7
C+
50% - 54%
2.3
Pass
C
50% - 54%
2.0
Pass (Repeat Candidate)
Excellent
Good
I
0
Incomplete
F
0
Fail
N
0
Academic Concession
A candidate who has not earned a grade of C+ or above in a particular course unit at the first
attempt, but has obtained minimum marks for at least one component, receives the grade I
10
otherwise he receives the grade F. By repeating the incomplete component for those obtaining
the grade I, or all the components for those obtaining the grade F, the candidate can upgrade
grade C only and this will be used for calculating the grade point average (GPA). The grade N
signifies the academic concession granted with the approval of the Senate.
4a.6
Calculation of Grade Point Average: The overall grade point average (GPA) of
the postgraduate examination will be calculated according to the following formula.
Overall GPA =
∑ [GradePoints × Credits]
∑ Credits
Note: All credits offered by the student, irrespective of whether completed or not will be considered
in the evaluation of the Overall GPA.
4a.7
Release of Result of Written Examination: Performance of a candidate at the
written examination shall be released after the Board of Examiners meeting,
subject to confirmation of the Senate, unless the Board of Examiners recommends
withholding of the results for specific reasons.
4a.8 Criteria for the Award of the Postgraduate Diploma:
1. Passed the Postgraduate Examination as specified in clause 4a.3
AND
2. Not desirous of proceeding to the Master's dissertation, either before
commencement or thereafter, as indicated in writing to the head of department
OR Not able to undertake/complete the Master's dissertation under the prescribed
conditions.
4a.9
Date of Award: The effective date of the Postgraduate Diploma shall be the first
day of the following month after the successful completion of all of the following
components of the postgraduate examination:
1. written examinations
2. seminars
3. assignments and laboratory work and projects
4b: Performance Criteria for Master of Science Degree
11
4b.1
Title of the Award: Master of Science - Specialization Industrial Automation
4b.2
Participation in Academic Program:
1. Passed the postgraduate examination as specified in clause 4a.3 but has not been
awarded the Postgraduate Diploma
2. Has obtained an overall GPA of at east 3.0 at the postgraduate examination.
3. Undertake an individual research dissertation, as assigned by the Department, on
a specific subject area, for a period of not less than one academic year duration
on a part time basis or equivalent.
4. The postponement of the dissertation will only be allowed with prior approval from
the Senate.
4b.3
Pass in the Dissertation:
1. The candidate will be graded based on the evaluation of the final seminar and oral
examination by a panel of examiners.
2. The grading of the dissertation is directly on a letter Grade. The benchmark
performance given in clause 4a.5 may be used for guidance.
3. A candidate is deemed to have passed the dissertation, if the candidate earns the
Grade C+ or above at the first attempt.
4. If the candidate is unsuccessful in dissertation, he/she may be re-examined and
given the pass grade C if successful. Normally only one re-examination will be
allowed, usually after a minimum of three months but not exceeding 12 months
after the initial examination/assessment.
4b.4
Criteria for the Award of the M.Sc. Degree:
1. Passed the Postgraduate Examination as specified in clause 4a.3
AND
2. Successfully completed any additional prescribed seminars and assignments
AND
3. Successfully completed the research dissertation assigned to the candidate.
4b.5
Date of Award:
The effective date of the MSc degree shall be the first day of the following month
after the successful completion and evaluation of all of the following components:
1. Postgraduate Examination as specified in clause 4a.3
2. Research dissertation
3. Submission of final bound copies of dissertation (after corrections if any).
Document 6: Resource Persons
12
CODE
SUBJECTS (CREDITS )
Resource Persons
EE5061
State Space Design
Dr. Trishantha Nanayakkara
EE5062
Digital Control
Dr. Sisil Kumarawadu
EE5201
Sensors and Actuators for Automatic
Systems
Dr. Nalin Wicramarachchi
EE5202
Modern Power Electronics and Drives
Dr. JP Karunadasa
PME/MSE205
Automation and Control of
Manufacturing Systems
Dr. Palitha Dasanayake
Dr. Thrishantha Nanayakkara
PME/MSE304
Mechatronics and Robotics
Dr. Palitha Dasanayake
Dr. Rohan Munasinghe
PME/IA101
System Automation
Dr. Chulantha Kulasekara
Dr. Lanka Udawatta
PME/IA102
Advanced Engineering Mathematics
for Controls
Dr. Thrishantha Nanayakkara
Dr. Rohan Munasinghe
Dr. Sisil Kumarawadu
Mr. Shantha Fernando
E E5071
Microprocessor Based Systems
Dr. Chathura De Silva
EE5073
Computer Networks
Mr. Mohommad Firdhous
EE5074
Internet Applications
Prof. JR Lucas
EE5075
Artificial Intelligence Applications
Dr. Lanka Udawatta
EE5081
Operations Research
Dr. M. Indralingam.
EE5082
Numerical Methods (or PME/ET101)
Dr. G.T.F. de Silva
EE5084
System Identification and Modelling
Dr. Nishantha Nanayakkara
13
EE5203
Robotics Technology
Dr. Rohan Munasinghe
EE5204
Hardware and Software for Systems
Control
Dr. Thrishantha Nanayakkara
PME/MSE305
Artificial Intelligence in Manufacturing
Dr. Palitha Dasanayake
Dr. Lanka Udawatta
PME/MSE103
Computer Integrated Manufacturing
PME/MSE203
Quality Management
Mr. ACM Naeem*
Dr. Udaya Kahangamage
Mr. HKG Punchihewa
PME/MSE202
Industrial Management
Dr. Chandana Perera
PME/MSE306
Human Factors in Engineering
Mr. HKG Punchihewa
PME/MSE204
Supply Chain Management
Mr. ACM Naeem*
PME/MSE206
Manufacturing Processes: Advanced
Concepts
Dr. MARV Fernando
Dr. Rohan Tittagala
Dr. Thrishantha Nanayakkara
PME/IA103
Machine Intelligence and Robotics
Dr. Lanka Udawatta
Dr. Thrishantha Nanayakkara
PME/IA104
Research Seminar
EE5099
Project ( Only for those who plan for
PGDip)
Dr. Lanka Udawatta
All staff members from EE &
ME
*Mr. ACM Naeem
BSc. Eng. (Hons), M.Eng. (AIT)
Manage, Projects, Kingslake Engineering Systems, Colombo 2
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