Development of medical expert systems with fuzzy concepts in a PC environment.

by So Yuen Tai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.Bibliography: leaves [144]-[146].ACKNOWLEDGEMENTSTABLE OF CONTENTS --- p.T.1ABSTRACTChapter 1. --- INTRODUCTION --- p.1.1Chapter 1.1 --- Inexact Knowledge in Medical Expert Systems --- p.1.1Chapter 1.2 --- Fuzzy Expert System Shells --- p.1.2Chapter 1.2.1 --- SPII-2 --- p.1.3Chapter 1.2.2 --- Fuzzy Expert System Shell for Decision Support System --- p.1.4Chapter 1.3 --- Medical Expert Systems --- p.1.6Chapter 1.3.1 --- EXPERT --- p.1.6Chapter 1.3.2 --- DIABETO --- p.1.8Chapter 1.4 --- Impact from Micro-computer --- p.1.10Chapter 1.5 --- Approach --- p.1.11Chapter 2. --- SYSTEM Z-ll --- p.2.1Chapter 2.1 --- General Description --- p.2.1Chapter 2.2 --- Main Features --- p.2.2Chapter 2.2.1 --- Fuzzy Concepts --- p.2.2Chapter 2.2.2 --- Fuzzy Certainty --- p.2.3Chapter 2.2.3 --- Fuzzy Comparison --- p.2.5Chapter 2.2.4 --- Rule Evaluation --- p.2.7Chapter 2.2.5 --- Certainty Factor Propagation --- p.2.9Chapter 2.2.6 --- Linguistic Approximation --- p.2.10Chapter 2.3 --- Limitations and Possible Improvements --- p.2.11Chapter 3. --- A FUZZY EXPERT SYSTEM SHELL (Z-lll) IN PC ENVIRONMENT --- p.3.1Chapter 3.1 --- General Description --- p.3.1Chapter 3.2 --- Programming Environment --- p.3.1Chapter 3.3 --- Main Features and Structure --- p.3.3Chapter 3.3.1 --- Knowledge Acquisition Module --- p.3.5Chapter 3.3.1.1 --- Object Management Module --- p.3.5Chapter 3.3.1.2 --- Rule Management Module --- p.3.6Chapter 3.3.1.3 --- Fuzzy Term Management Module --- p.3.7Chapter 3.3.2 --- Consultation Module --- p.3.8Chapter 3.3.2.1 --- Fuzzy Inference Engine --- p.3.8Chapter 3.3.2.2 --- Review Management Module --- p.3.11Chapter 3.3.2.3 --- Linguistic Approximation Module --- p.3.11Chapter 3.3.3 --- System Properties Management Module --- p.3.13Chapter 3.4 --- Additional Features --- p.3 14Chapter 3.4.1 --- Weights --- p.3.15Chapter 3.4.1.1 --- Fuzzy Weight --- p.3.16Chapter 3.4.1.2 --- Fuzzy Weight Evaluation --- p.3.17Chapter 3.4.1.3 --- Results of Adding Fuzzy Weights --- p.3.21Chapter 3.4.2 --- Fuzzy Matching --- p.3.24Chapter 3.4.2.1 --- Similarity --- p.3.25Chapter 3.4.2.2 --- Evaluation of Similarity measure --- p.3.26Chapter 3.4.3 --- Use of System Threshold --- p.3.30Chapter 3.4.4 --- Use of Threshold Expression --- p.3.33Chapter 3.4.5 --- Playback File --- p.3.35Chapter 3.4.6 --- Database retrieval --- p.3.37Chapter 3.4.7 --- Numeric Variable Objects --- p.3.39Chapter 3.5 --- Implementation Highlights --- p.3.41Chapter 3.5.1 --- Knowledge Base --- p.4.42Chapter 3.5.1.1 --- Fuzzy Type --- p.4.42Chapter 3.5.1.2 --- Objects --- p.3.45Chapter 3.5.1.3 --- Rules --- p.3.49Chapter 3.5.2 --- System Properties --- p.3.53Chapter 3.5.2.1 --- System Menu --- p.3.53Chapter 3.5.2.2 --- Option Menu --- p.3.54Chapter 3.5.3 --- Consultation System --- p.3.55Chapter 3.5.3.1 --- Inference Engine --- p.3.56Chapter 3.5.3.2 --- Review Management --- p.3.60Chapter 3.6 --- Comparison on Z-lll and Z-ll --- p.3.61Chapter 3.6.1 --- Response Time --- p.3.62Chapter 3.6.2 --- Accessibility --- p.3.62Chapter 3.6.3 --- Accommodation of Large Knowledge Base --- p.3.62Chapter 3.6.4 --- User-Friendliness --- p.3.63Chapter 3.7 --- General Comments on Z-lll --- p.3.64Chapter 3.7.1 --- Adaptability --- p.3.64Chapter 3.7.2 --- Adequacy --- p.3.64Chapter 3.7.3 --- Applicability --- p.3.65Chapter 3.7.4 --- Availability --- p.3.65Chapter 4. --- KNOWLEDGE ENGINEERING --- p.4.1Chapter 4.1 --- Techniques used in Knowledge Acquisition --- p.4.1Chapter 4.2 --- Interviewing the Expert --- p.4.2Chapter 4.3 --- Knowledge Representation --- p.4.4Chapter 4.4 --- Development Approach --- p.4.6Chapter 4.5 --- Knowledge Refinement --- p.4.7Chapter 4.6 --- Consistency Check and Completeness Check --- p.4.12Chapter 4.6.1 --- The Consistency and Completeness in a nonfuzzy rule set --- p.4.13Chapter 4.6.1.1 --- Inconsistency in nonfuzzy rule-based system --- p.4.13Chapter 4.6.1.2 --- Incompleteness in nonfuzzy rule-based system --- p.4.18Chapter 4.6.2 --- Consistency Checks in Fuzzy Environment --- p.4.20Chapter 4.6.2.1 --- Affinity --- p.4.21Chapter 4.6.2.2 --- Detection of Inconsistency and Incompleteness in Fuzzy Environment --- p.4.24Chapter 4.6.3 --- Algorithm for Checking Consistency --- p.4.25Chapter 5. --- FUZZY MEDICAL EXPERT SYSTEMS --- p.5.1Chapter 5.1 --- ABVAB --- p.5.1Chapter 5.1.1 --- General Description --- p.5.1Chapter 5.1.2 --- Development of ABVAB --- p.5.2Chapter 5.1.3 --- Computerisation of Database --- p.5.4Chapter 5.1.4 --- Results of ABVAB --- p.5.7Chapter 5.1.5 --- From Minicomputer to PC --- p.5.15Chapter 5.2 --- INDUCE36 --- p.5.17Chapter 5.2.1 --- General Description --- p.5.17Chapter 5.2.2 --- Verification of INDUCE36 --- p.5.18Chapter 5.2.3 --- Results --- p.5.19Chapter 5.3 --- ESROM --- p.5.21Chapter 5.3.1 --- General Description --- p.5.21Chapter 5.3.2 --- Multi-layer Medical Expert System --- p.5.22Chapter 5.3.3 --- Results --- p.5.25Chapter 6. --- CONCLUSION --- p.6.1REFERENCES --- p.R.1APPENDIX I --- p.A.1APPENDIX II --- p.A.2APPENDIX III --- p.A.3APPENDIX IV --- p.A.1

Many-Objective Genetic Type-2 Fuzzy Logic Based Workforce Optimisation Strategies for Large Scale Organisational Design

Workforce optimisation aims to maximise the productivity of a workforce and is a crucial practice for large organisations. The more effective these workforce optimisation strategies are, the better placed the organisation is to meet their objectives. Usually, the focus of workforce optimisation is scheduling, routing and planning. These strategies are particularly relevant to organisations with large mobile workforces, such as utility companies. There has been much research focused on these areas. One aspect of workforce optimisation that gets overlooked is organisational design. Organisational design aims to maximise the potential utilisation of all resources while minimising costs. If done correctly, other systems (scheduling, routing and planning) will be more effective. This thesis looks at organisational design, from geographical structures and team structures to skilling and resource management. A many-objective optimisation system to tackle large-scale optimisation problems will be presented. The system will employ interval type-2 fuzzy logic to handle the uncertainties with the real-world data, such as travel times and task completion times. The proposed system was developed with data from British Telecom (BT) and was deployed within the organisation. The techniques presented at the end of this thesis led to a very significant improvement over the standard NSGA-II algorithm by 31.07% with a P-Value of 1.86-10. The system has delivered an increase in productivity in BT of 0.5%, saving an estimated £1million a year, cut fuel consumption by 2.9%, resulting in an additional saving of over £200k a year. Due to less fuel consumption Carbon Dioxide (CO2) emissions have been reduced by 2,500 metric tonnes. Furthermore, a report by the United Kingdom’s (UK’s) Department of Transport found that for every billion vehicle miles travelled, there were 15,409 serious injuries or deaths. The system saved an estimated 7.7 million miles, equating to preventing more than 115 serious casualties and fatalities

Power Distribution System Event Classification Using Fuzzy Logic

This dissertation describes an on-line, non-intrusive, classification system for identifying and reporting normal and abnormal power system events occurring on a distribution feeder based on their underlying cause, using signals acquired at the distribution substation. The event classification system extracts features from acquired signals using signal processing and shape analysis techniques. It then analyzes features and classifies events based on their cause using a fuzzy logic expert system based classifier. The classification system also extracts and reports parameters to assist utilities in locating faulty components. A detailed illustration of the classifier design process is presented. Power distribution system event classification problem is shown to be a large scale classification problem. The reasoning behind the choice of a fuzzy logic based hierarchical expert system classifier to solve this problem is explained in detail. The fuzzy logic based expert system classifier uses generic features, shape based features and event specific features extracted from acquired signals. The design of feature extractors for each of these feature categories is explained. A new, fuzzy logic based, modified Dynamic Time Warping (DTW) algorithm was developed for extracting shape based features. Design of event specific feature extractors for capacitor problems, arcing and overcurrent events are discussed in detail. The fuzzy logic based hierarchical expert system classifier required a new fuzzy inference engine that could efficiently handle a large number of rules and rule chaining. A new fuzzy inference engine was designed for this purpose and the design process is explained in detail. To avoid information overload, an intelligent reporting framework that processes raw classification information generated by the fuzzy classifier and reports events of interest in a timely and user friendly manner was developed. Finally, performance studies were carried out to validate the performance of the designed fuzzy logic based expert system classifier and the intelligent reporting system. The data needed to design and validate the classification system were obtained through the Distribution Fault Anticipation (DFA) data collection plat- form developed by Power System Automation Laboratory (PSAL) at Texas A&M University, sponsored by the Electric Power Research Institute (EPRI) and multiple partner utilities