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Teaching
Modern
electrical engineering education must prepare students to solve a wide
range of engineering problems. Thus in the classroom, I place great emphasis
on showing students how engineering fundamentals are critical to various
broader issues. I employ a variety of teaching strategies, as circumstances
seem to warrant. I try to balance theory and practice and use real-world
examples that students can relate to. To an extent limited by large class
sizes, my goal is to make every effort to actively educate the class as a
whole and to build upon the strength of my students. I consider it
important for my students to leave with, if nothing more, an understanding
of professional and ethical responsibilities and recognition of the need
for life-long learning.
Teaching
awards and prizes
Engineering
Educator Award, Faculty of Engineering, NUS, 2012
Engineering
Educator Award, Faculty of Engineering, NUS, 2011
Annual Teaching
Excellence Award (ATEA) Honour Roll, NUS (awarded in May
2011 for sustained high performance in teaching)
Annual Teaching
Excellence Award, NUS, 2010
Annual Teaching
Excellence Award, NUS, 2009
Annual Teaching
Excellence Award, NUS, 2008
Faculty
Level Teaching awards or prizes
2009 Honours List,
Faculty of Engineering, NUS
2008 Honours List,
Faculty of Engineering, NUS
2007 Honours List, Faculty of Engineering, NUS
2006 Teaching
Commendation Certificate, Faculty of Engineering, NUS
2005 Teaching
Commendation Certificate, Faculty of Engineering, NUS
2003 Best tutor
(B.Eng.), Faculty of Engineering, NUS
2003 Teaching
Commendation Certificate, Faculty of Engineering, NUS
2002 Teaching
Commendation Certificate, Faculty of Engineering, NUS
2001 Teaching
Commendation Certificate, Faculty of Engineering, NUS
Modules
currently teaching:
EE6531
Selected Topics in Smart Grid Technologies
Basic concepts and
structures of micro-grid, smart grid, and vehicular technologies are
discussed in this module. Major topics covered are: power converters for
smart grid, electric and fuel cell vehicles, battery management system,
Intelligent multi-agent control and cyber security of smart grid, system
level issues, and recent development in such emerging technologies.
EE4511
Sustainable Energy Systems
This module provides the students with a good
understanding of analysis and management strategies for promoting the
advancement and use of economically and environmentally sustainable
electrical energy systems. The module will cover distributed generation and
renewable energy sources, and strategies for supply and demand side management for efficient resource
utilisation. Issues related to environmental impact of electrical energy
generation will be discussed. Models of power distribution systems with
embedded generation and microgrids will be
introduced. The module will also cover supply-grid interconnection, and
reliability and power quality issues.
EE4501 Power
System Management & Protection
Robust and
reliable power supply is a backbone of any industrial society. This module
provides necessary analytical tools required to assess the performance of
existing electric power systems under various operating conditions and also
to plan the future expansion of such systems. In addition, it introduces
various protection schemes employed in the industry. It adequately prepares
students seeking employment in the electric energy related industries. The
topics covered are: Modeling of power systems:
bus admittance and bus impedance matrices, network building algorithms;
Load flow studies: problem formulation, computer solution techniques,
applications; Fault analysis: symmetrical components, sequence impedance
networks, symmetrical and unsymmetrical faults; Protection: components,
relay coordination; Protection of distribution systems; Differential, and
earth fault protection systems.
EE6701
Evolutionary Computation
This course explores how principles from theories of evolution
can be used to construct intelligent systems. Established evolutionary
paradigms as well as, significant new developments, including evolutionary
algorithms, evolutionary strategies, evolving neural networks, ant-colony
optimization, artificial immune systems, and swarm intelligence will be
covered. Students will be taught how these approaches identify and exploit
biological processes in nature, allowing a wide range of applications to be
solved in industry and business. Key problem domains such as
multi-objective scheduling, optimization, search, and design will be
examined. Students will gain hands-on experience in applying these
techniques to real-life problems through project work.
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