12 research outputs found
Fuzzy Knowledge Based Reliability Evaluation and Its Application to Power Generating System
PhDThe method of using Fuzzy Sets Theory(FST) and Fuzzy Reasoning(FR) to aid
reliability evaluation in a complex and uncertain environment is studied, with special
reference to electrical power generating system reliability evaluation.
Device(component) reliability prediction contributes significantly to a system's
reliability through their ability to identify source and causes of unreliability. The main
factors which affect reliability are identified in Reliability Prediction Process(RPP).
However, the relation between reliability and each affecting factor is not a necessary and
sufficient one. It is difficult to express this kind of relation precisely in terms of quantitative
mathematics. It is acknowledged that human experts possesses some special characteristics
that enable them to learn and reason in a vague and fuzzy environment based on their
experience. Therefore, reliability prediction can be classified as a human engineer oriented
decision process. A fuzzy knowledge based reliability prediction framework, in which
speciality rather than generality is emphasised, is proposed in the first part of the thesis.
For this purpose, various factors affected device reliability are investigated and the
knowledge trees for predicting three reliability indices, i.e. failure rate, maintenance time
and human error rate are presented. Human experts' empirical and heuristic knowledge are
represented by fuzzy linguistic rules and fuzzy compositional rule of inference is employed
as inference tool.
Two approaches to system reliability evaluation are presented in the second part of
this thesis. In first approach, fuzzy arithmetic are conducted as the foundation for system
reliability evaluation under the fuzzy envimnment The objective is to extend the underlying
fuzzy concept into strict mathematics framework in order to arrive at decision on system
adequacy based on imprecise and qualitative information. To achieve this, various
reliability indices are modelled as Trapezoidal Fuzzy Numbers(TFN) and are proceeded by
extended fuzzy arithmetic operators. In second approach, the knowledge of system
reliability evaluation are modelled in the form of fuzzy combination production rules and
device combination sequence control algorithm. System reliability are evaluated by using
fuzzy inference system. Comparison of two approaches are carried out through case
studies. As an application, power generating system reliability adequacy is studied. Under
the assumption that both unit reliability data and load data are subjectively estimated, these
fuzzy data are modelled as triangular fuzzy numbers, fuzzy capacity outage model and
fuzzy load model are developed by using fuzzy arithmetic operations. Power generating
system adequacy is evaluated by convoluting fuzzy capacity outage model with fuzzy load
model. A fuzzy risk index named "Possibility Of Load Loss" (POLL) is defined based on
the concept of fuzzy containment The proposed new index is tested on IEEE Reliability
Test System (RTS) and satisfactory results are obtained
Finally, the implementation issues of Fuzzy Rule Based Expert System Shell
(FRBESS) are reported. The application of ERBESS to device reliability prediction and
system reliability evaluation is discussed
Constructing 3D faces from natural language interface
This thesis presents a system by which 3D images of human faces can be constructed
using a natural language interface. The driving force behind the project was the need to
create a system whereby a machine could produce artistic images from verbal or
composed descriptions. This research is the first to look at constructing and modifying
facial image artwork using a natural language interface.
Specialised modules have been developed to control geometry of 3D polygonal head
models in a commercial modeller from natural language descriptions. These modules
were produced from research on human physiognomy, 3D modelling techniques and
tools, facial modelling and natural language processing. [Continues.
Aspects of functional programming
This thesis explores the application of functional programming in new areas and its
implementation using new technologies. We show how functional languages can be
used to implement solutions to problems in fuzzy logic using a number of languages:
Haskell, Ginger and Aladin. A compiler for the weakly-typed, lazy language Ginger
is developed using Java byte-code as its target code. This is used as the inspiration
for an implementation of Aladin, a simple functional language which has two novel
features: its primitives are designed to be written in any language, and evaluation
is controlled by declaring the strictness of all functions. Efficient denotational and
operational semantics are given for this machine and an implementation is devel-
oped using these semantics. We then show that by using the advantages of Aladin
(simplicity and strictness control) we can employ partial evaluation to achieve con-
siderable speed-ups in the running times of Aladin programs
A framework for managing global risk factors affecting construction cost performance
Poor cost performance of construction projects has been a major concern for both
contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have
traditionally used financial mark-ups to cover the risk associated with construction
projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement
methods with clients allocating greater risks to contractors.
Evidence shows that there is a gap between existing risk management techniques and
tools, mainly built on normative statistical decision theory, and their practical application
by construction contractors. The main reason behind the lack of use is that risk decision
making within construction organisations is heavily based upon experience, intuition and
judgement and not on mathematical models.
This thesis presents a model for managing global risk factors affecting construction cost
performance of construction projects. The model has been developed using behavioural
decision approach, fuzzy logic technology, and Artificial Intelligence technology. The
methodology adopted to conduct the research involved a thorough literature survey on
risk management, informal and formal discussions with construction practitioners to
assess the extent of the problem, a questionnaire survey to evaluate the importance of
global risk factors and, finally, repertory grid interviews aimed at eliciting relevant
knowledge. There are several approaches to categorising risks permeating construction projects. This
research groups risks into three main categories, namely organisation-specific, global and
Acts of God. It focuses on global risk factors because they are ill-defined, less
understood by contractors and difficult to model, assess and manage although they have
huge impact on cost performance. Generally, contractors, especially in developing
countries, have insufficient experience and knowledge to manage them effectively. The
research identified the following groups of global risk factors as having significant impact
on cost performance: estimator related, project related, fraudulent practices related,
competition related, construction related, economy related and political related factors.
The model was tested for validity through a panel of validators (experts) and crosssectional
cases studies, and the general conclusion was that it could provide valuable
assistance in the management of global risk factors since it is effective, efficient, flexible
and user-friendly. The findings stress the need to depart from traditional approaches and
to explore new directions in order to equip contractors with effective risk management
tools
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies