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MECH S395

More information: Course Guide
MECHATRONICS

MECH S395

More information: Course Guide

MECHATRONICS

Course Start Date
N/A
Course Level
Higher
Length in Terms
2 terms
Credits
10
Language
English
Fees ($) (including lab fees)
N/A
Future Terms
Aut 22
Quota and Schedule
Course Start Date
Course LevelLength in TermsCredits
Language
Fees ($) (including lab fees)
Future Terms
N/A
Higher2 terms10
English
N/A
Aut 22

Course Coordinator: Dr Kevin Hung, BSc (Queen’s University); MPhil, PhD (CUHK); SMIEEE; MIET; MCIE

Course Developer: The Open University, UK, Course Team

MECH S395 is an optional course within the BSc and BSc (Hons) programmes in Applied Computing and Electronics. 2022 Autumn is the final presentation of MECH S395.

The course concentrates on the architecture, desired behaviour and performance of intelligent machines rather than on technological details. The emphasis is on principles and methods applicable to configuring mechatronic systems and sub-systems.

Aims
This course aims to:

  • Introduce an integrated interdisciplinary knowledge of mechatronics;
  • Develop skills needed to participate in the specification and conceptual design of intelligent machines.

Contents
The course covers the following topics:

  • The concept of an ‘intelligent machine’; economic and other reasons for developing intelligent machines; aspects of intelligent behaviour; a framework based on concepts of perception, cognition and execution within which intelligent machines can be anlaysed and designed
  • Perception as a machine’s capability to collect and use information about its environment and its own behaviour
  • Cognition as a machine’s capability to interpret models constructed by its perception subsystem with a view to planning its behaviour. Reasoning in conditions of certainty and uncertainty
  • Execution as capability of acting on instructions; the concept of a deterministic control system; fundamentals of feedback, stability and hierarchical control
  • The use of artificial intelligence (AI) in the design of machines in order to add value to their performance
  • Pattern-recognition systems
  • Reasoning
  • Acquiring explicit knowledge
  • Knowledge-based systems and neural networks
  • Intelligent planning; intelligent control
  • Algorithmic and AI approaches to computer vision and self-diagnosing

Learning support
There will be tutorials, surgeries, and several day-long laboratory sessions.

Assessment
There will be four tutor-marked assignments and a final examination. Students are required to submit assignments via the Online Learning Environment (OLE).

Online requirement
This course is supported by the Online Learning Environment (OLE). You can find the latest course information from the OLE. Through the OLE, you can communicate electronically with your tutor and the Course Coordinator as well as other students. To access the OLE, students will need to have access to the Internet. The use of the OLE is mandatory for the study of this course. The tutorials and surgeries will be video-recorded and available for playback from the OLE.

Equipment
Students will need access to a personal computer and to the Internet. The recommended configuration is:

  • Pentium 4 CPU
  • Microsoft Windows XP or above
  • Display card and monitor
  • 1 GB RAM
  • 10 GB free hard disk space
  • DVD-ROM Drive (8X or faster)
  • Mouse, keyboard, printer
  • LAN card for Internet access
  • Internet Explorer 8 or above

Software
Student will need access to Microsoft Windows XP or above.

Set book(s)
There are no set books for this course.

Students with disabilities or special educational needs
Students with impaired hearing or restricted manual movement may find this course difficult. Course and supplementary materials are not available on tape. You should seek advice from the Course Coordinator before enrolling on the course.