Appendix for Degree Report Writing
Transcript of Appendix for Degree Report Writing
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APPENDIXB
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The Report Prepared In Fulfillment Of The Requirements
For Industrial Training MPZ 3016
PREPARED BY:
STUDENT'S NAME
MATRIC NUMBER
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APPENDIXC
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I hereby declare that I have read this report and in my
opinion this report is sufficient in terms of scope and quality for the
requirements of.Industrial Training MPZ 3016
Signature
Name of Supervisor
Date
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APPENDIX A
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INDUSTRIAL TRAINING MPZ 3016
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[ORGANIZATION S NAME]
[CITY AND STATE OF THE ORGANIZATION]
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APPENDIXD
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ACKNOWLEDGEMENT
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...In preparing this thesis, I was in contact with many people, researchers
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~cademicians, and practitioners. They have contributed towards my understanding:
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______ ...and thoughts. In particular, I wish to express my sincere appreciation to my thesis: ......
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~upervisor,Professor Dr.X, for encouragement, guidance, critics, friendship and also:
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his advices and motivation. Without his continued support and interest, this thesis:
~ould not have been the same as presented here.
I am also indebted to Insaniah University College CIUC)and the Librarians at:
ruc
They also deserve special thanks for their assistance in supplying the relevant:
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literatures. :
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: My fellow postgraduate students should also be recognized for their support.:
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My sincere appreciation also extends to all my colleagues and others who have:
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provided assistance at various occasions. Their views and tips are useful indeed.:
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Unfortunately, it is not possible to list all of them in this limited space. I amgrateful'
to all my family members.
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APPENDIXE
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DECLARATION
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DEDICATION
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ACKNOWLEDGEMENTS I V
ABSTRACT V
TABLE OF CONTENTS
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LIST OF TABLES
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LIST OF FIGURES
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LIST OF ABBREVIATIONS
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LIST OF SYMBOLS
XI X
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ORGANISATION OF THE THESIS
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1.1
Definition of a Thesis
1
1.2
Organisation ofthe Thesis
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1.3
Thesis Status Declaration
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1.4
Declaration
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1.4.1 Supervisor's Declaration 4
NOTES NDFOOTNOTES
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4.1 General guideline
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4.2 Consistency
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4
~FEREN ES
35
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~ppendices A - Y . 37 - 62 :
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APPENDIXF
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The role of statistical quality engineering tools and
methodologies
Basic ANN models used for control chart pattern
recognition
General design strategies/structures for CCPR
The overall research plan
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Parameters for simulating individual process variation data 75
Description of performance measures
Targeted recognizer outputs
Design matrix and results for the preliminary feature
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screerung
Regression analysis for the results of preliminary feature
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screerung 111
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ANOV A for the results of preliminary feature screening
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Tentative significant main effects and two-factor
interactions 113
Estimated effects and regression coefficients for the
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recognizer's performance (reduced model)
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~.7 ANOV A for the recognizer's performance (reduced I
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: model) 116 :
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APPENDIXG
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Trends leading to the problem
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Design and development phases of the proposed scheme
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Summary of the research contributions
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Chance and assignable causes of process variation
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(Montgomery, 1996a)
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:2 2 Classification of statistical quality engineering tools 15
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Continuous variability reduction using SPC chart
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(Revelle and Harrington, 1992)
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2.4
Classification of research areas in SPC
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2.5
Advances in process variation monitoring and
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recognition using SPC charting
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Nelson's runs rules (Nelson, 1984) 26
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Typical fully developed patterns on Shewhart control
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chart (Cheng, 1989) 28
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APPENDIXH
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Table 4.3 : Comparison of experimental and computer simulation results
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Experiment
Computer Simulation
Distance Ratio
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mean value)
mean value)
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0.125
0.25 0.137
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0.250
0.46 0.560
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0.375
0.63 0.738
0.500
0.75
0.861
0.625
0.83
0.939
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0.750
0.88
0.981
0.875
0.93
0.997
1.000
1.00
1.000
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APPENDIX I
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APPENDIXJ
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APPENDIXK
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Mmad Zaki Abu Bakar (1989). Pemprosesan Teks Bahasa Melayu Untuk :
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: Pemahaman Komputer. Universiti Teknologi Malaysia: Tesis Doktor Falsafah. :
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funerican Chemical Society ed. (1978). Handbook for Authors of American :
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: Chemical Society Publications. Washington, D. C.: American Chemical Society.
Billings S. A. (1980). Identification of Nonlinear Systems: A survey. Proc. Instn
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Electr. Engrs Part D. 127(6): 272-284.
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Engineers Joint Council (1969). Thesaurus of Engineering and Scientific Terms.
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New York: Engineers Joint Council.
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Martin, A. M. ed. (1991).
Peat as an Agent in Biological Degradation of Waste.
London: Elsevier. 314-362.
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Puget, J. F. and Albert, P. (1994a). SOLVER: Constraints - Objects Descriptions.
Technical Report. ILOG S. A.
puget, J. F. and Albert, P. (1994b). A C Implementation
CLP. Technical
Report. ILOG S. A.
~heta, A. F. and De Jong, K.(1996). Parameter Estimation of Nonlinear Systems in
Noisy Environments Using Genetic Algorithms. Proceedings of the 1966 IEEE
International Symposium on Intelligent Control.
September 15-18. Dearborn,
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Michigan: IEEE, 360-365.
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Sukiman Sannani (1987). Pencemaran Radioaktif DIm. Ahmad Badri Mohamad.
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: Perspektif Persekitaran. Petaling Jaya: Fajar Bakti. 71-87.
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:rheusen, G. J. and Fabrycky, W.
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(1984). Engineering Economy. 6th edition.
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: Englewood Cliffs, N. J.: Prentice-Hall.
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Weres, S. M. (1990). Structure Selection of Stochastic Dynamic Systems. New York:
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Gordon and Breach Science Publishers.
*Note: Arranged alphabetically according to author's name.
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APPENDIXL
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: INDUSTRIAL TRAINING REPORT
SESSION 2012
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NURUL IDDAYAH AHMAD
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BBA
INSANIAH UNIVERSITY COLLEGE
MAY 2012
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APPENDIXM
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ABSTRACT
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: ... ..The purpose of this study is to investigate the application of genetic algorithm
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lGA) in modelling linear and non-linear dynamic systems and develop an alternative:
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_____ __. model structure selection algorithm based on GA. Orthogonal least square (OLS), a: ......_
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gradient descent method was used as the benchmark for the proposed algorithm. A:
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model structure selection based on modified genetic algorithm (MGA) has been:
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proposed in this study to reduce problems of premature convergence in simple GA:
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tSGA). The effect of different combinations of MGA operators on the performance of:
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the developed model was studied and the effectiveness and shortcomings of MGA:
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were highlighted. Results were compared between SGA, MGA and benchmark OLS:
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method. twas discovered that with similar number of dynamic terms, in most cases,:
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MGA performs better than SGA
in
terms of exploring potential solution and:
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butperformed the OLS algorithm in terms of selected number of terms and predictive:
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accuracy. In addition, the use of local search with MGA for fine-tuning the algorithm:
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was also proposed and investigated, named as memetic algorithm (MA). Simulation:
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results demonstrated that in most cases, MA is able to produce an adequate and:
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parsimonious model that can satisfy the model validation tests with significanf
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advantages over OLS, SGA and MGA methods. Furthermore, the case studies on:
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~dentification of multi variable .systems based on real experimental data from two:
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systems namely a turbo alternator and a continuous stirred tank reactor showed that:
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the proposed algorithm could be used as an alternative to adequately identify adequate:
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tmd parsimonious models for those systems.
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APPENDIXN
Sample of approved abbreviations
ABBREVIATIONS
ABBREVIATIONS TO BE USED
No. DEGREE
TO BE USED IN
IN THE TITLE PAGE
THE SPINE
Bachelor of Business Administration
BBA
B.B.A. (Hons)
(Honours)
Bachelor of Business Administration
B.B.A. (Hons) (Islamic Banking &
2 (Honours) (Islamic Banking and
BIBF
Finance)Finance)
Bachelor of Business Administration
B.B.A. (Hons) (Islamic Financial
3
(Honours) (Islamic Financial
BIFP
Planning
Planning)
Bachelor of Business Administration
B.B.A. (Hons) (Takaful Risk
4
(Honours) (Takaful Risk BIRT
Management)
Management)
5
Bachelor of Accounting (Honours) BACC B.Acc (Hons)
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APPENDIX 0
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