1. MyTRIZ Competition 2012
Proposal Team:
CS
Aisyah Ismail
Azleena Mohd Kassim
Mohd Adib Omar (Group Leader)
Tan Choo Jun
Wan Mohd Nazmee Wan Zainon

2. Contents
• Part 1
• Question (a)
• Impact: The country will have sustainable skilled
and qualified ICT workers needed for the future.
• Part 2
• Impact: The wood materials are transported
without damaging the pipe.
• Part 3
• Impact: Enhancing the FAM Learning Mechanism
using Evolutionary-based TRIZ S-Curve Model

3. Part 1: Problem Analysis
• Shortage of skilled ICT graduates
• Why? Because current Institute of Higher Learning
(IPT/IPTA) is unable to produce sufficient "skilled"
graduates
• Why? Because good students tend to choose other
fields, e.g. Medicine, Dentistry & Pharmacy etc.
• Why? Students believe that career in ICT does not
provide good pay and job satisfaction
• Why? Misperceptions that ICT field does not provide
better pay, low jobs satisfaction and lacks of jobs
opportunities.

4. TRIZ Process, TRIZ Tools & Potential Solutions
Insufficient Awareness and Wrong Perception on ICT Career Computing Major is not as attractive as Medicine, Minimal entry requirement Many unemployed due to skill
Problem
Engineering, Accounting and Pharmacy among best students mismatch
Insufficient ICT Teachers No cap on the student limit per intake
Pre-University Industry
Primary and Secondary input input University input
Stages of Educational Matriculation Government
Education
Process Diploma Further Study
Form Six (STPM) Entrepreneurship
TRIZ Process and Tools Physical Contradiction by Separation in Time Physical Contradiction by Separation in Time Physical Contradiction by Separation in Space and
Physical Contradiction by Separation in Time
Physical Contradiction by Separation in System Level - Supersystem
Inventive Principles 9. Preliminary anti-action 10. Preliminary action 13. ’The other way round’ 19. Periodic Action
20. Continuity of Useful Action
23. Feedback
Proposed Solutions 9. Cultivate Problem Solving Culture 10. Cultivate Algorithmic Thinking 13. Stringent entry requirements 19. Periodic Retraining the
unemployed for certification
9. National Computing Competition 10. Standardized and up-to-date ICT Curriculum 13. Limit intake to ensure quality
9. Awareness Program: ICT camp 20. Industrial Training & Internships
9. Increase ICT Teachers
23. Formation of ICT Professional body to monitor the best practices, curriculum and prestige
Committed students
Expected Outcome inculcate the right perception to students about Careers in Students who are interested in ICT are prepared for Employable graduates by the
Quality ICT Program
ICT university education industry

5. Part 2: Problem Statement
• The 1-meter pipe leaks due to the
rubbing and knocking of wood
materials transported inside the
pipe using water as a medium of
transport.
example of wood material
Pipe (with water) to transport wood material

6. TRIZ process used
Function Analysis:
Pipe contains water and wood based materials
Water holds the wood based materials inside the pipe
Water flows with wood based materials through the pipe
Identify the type of contradictions:
Administrative contradictions: We want to allow the wood based
materials to traverse through the pipe without damaging the pipe.
Engineering contradictions: wood based material surface contact
damages the pipe
Physical Contradiction Solution Strategies: Separation in Space
Inventive Principle:
30. Flexible Shells / Thin Films

7. TRIZ tools used
• Contradiction Matrix cannot be used
since we are dealing single parameter,
in this case, the contact surface of
wood based materials.
• Instead, Physical Contradiction
Solution Strategies: Separation in Space
is used. It leads to the Inventive
Principle: 30. Flexible Shells / Thin Films

8. Potential Solutions
• Cover the wood based materials
with bubble wrap and then
transport them through the pipe.

9. Part 3:
Enhancing the FAM Learning
Mechanism using
Evolutionary-based TRIZ S-
Curve Model

10. The Scope of
TRIZ Hybridization Model
1. A combination with a Pareto-based algorithm
(MmGA) to undertaking the efficiency of learning
process in FAM
2. An optimization of the number of nodes in FAM
network without prior configuration towards Pareto
Front solutions
3. An implementation of proposed model using USM
Extract with Mobile Desktop Grid (MDG) in the form
of web application and API level

11. Do you see the their differential?
Unsupervised
ART-1 ART-2 Learning
Model
Fuzzy Set ARTMAP
Supervised
Learning
FAM Model

12. The Fuzzy ARTMAP (FAM)
FAM (same as ART and ARTMAP) consists of
Training Phase and Prediction Phase
Note:
They also called as Training Model and Prediction Model

13. The FAM Architecture
Adapted from: G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen,
“Fuzzy artmap: A neural network architecture for incremental su-pervised learning of
analog multidimensional maps,” IEEE Transactions on Neural Networks, vol. 3, pp. 698–713, Sept. 1992.
 Nodes are created
when sensation and
expectation does
not exceed the
vigilance value
 Training weights
(knowledge) are
kept for Prediction
Phase activities

14. The Optimization Dilemma
and FAM
• The accumulated knowledge of FAM in Optimization
Dilemma

15. The TRIZ-based
Evolutionary S-Curve
• The metrics of TRIZ’s Evolutionary S-
Curve

16. The FAM and TRIZ-based Evolutionary S-
Curve
• Metrics of determining the FAM's knowledge laying
alone the TRIZ-based Evolutionary S-Curve
1. Performance
2. Number of Inventions
3. Level of Inventions
4. Profitability
5. Cost Reduction Related Inventions

17. The USM Extract

18. The form of USM Extract Implementation
with MDG
• The flow of The flow of
• Web Application API Implementation
Instance

19. The Case Study of Our Model
Prediction Phase result for Diabetes patient

20. Thank you