Ph. D.

A "causal" expert system based on hypothetical reasoning and its application to a Mark 45 turret gun's lower hoist are described. HOIST is a system that performs fault diagnosis without the use of a domain expert or "shallow rules". Rather its "knowledge" is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the lesser acquainted diagnostician differs considerably from the normal rule-based expert system techniques: it reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, a functional model must reason about forces, fluid pressures and mechanical linkages, that is, qualitative physics. HOIST technology can be directly applied to any exactly specified device for modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in HOIST, has general utility in qualitative physics and reason maintenance. / Master of Science

by Leung Wing-shan, Queenie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 79-82). / Abstracts in English and Chinese. / Abstract --- p.iii / Acknowledgements --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Current Problem --- p.4 / Chapter 1.2 --- Research Proposal --- p.6 / Chapter 1.3 --- Research Target --- p.8 / Chapter 1.4 --- Thesis Overview --- p.9 / Chapter 2 --- Fuzzy Logic and Fuzzy Petri Net --- p.11 / Chapter 2.1 --- Background --- p.13 / Chapter 2.2 --- Fuzzy sets --- p.15 / Chapter 2.3 --- Operations on fuzzy sets --- p.18 / Chapter 2.4 --- Fuzzy logic --- p.20 / Chapter 2.5 --- Weighted Fuzzy Petri Net --- p.23 / Chapter 2.6 --- Fuzzy reasoning --- p.25 / Chapter 2.7 --- More about fuzzy logic --- p.29 / Chapter 2.8 --- Chapter Summary --- p.31 / Chapter 3 --- Dynamic Certainty Factor --- p.32 / Chapter 3.1 --- Definition --- p.32 / Chapter 3.1.1 --- Background --- p.33 / Chapter 3.1.2 --- Examples --- p.37 / Chapter 3.2 --- Advantages --- p.43 / Chapter 3.2.1 --- Best reasoning --- p.43 / Chapter 3.2.2 --- Independency --- p.46 / Chapter 3.2.3 --- Interaction effect --- p.49 / Chapter 3.3 --- Chapter Summary --- p.51 / Chapter 4 --- Experiment --- p.53 / Chapter 4.1 --- Transformation Definition --- p.54 / Chapter 4.2 --- Case Study --- p.61 / Chapter 4.2.1 --- Example 1 --- p.61 / Chapter 4.2.2 --- Example 2 --- p.63 / Chapter 4.3 --- Analysis --- p.65 / Chapter 4.3.1 --- Comparisons --- p.65 / Chapter 4.3.2 --- Discussion --- p.67 / Chapter 4.4 --- Chapter Summary --- p.68 / Chapter 5 --- Conclusion --- p.69 / Chapter 5.1 --- Final Summary --- p.69 / Chapter 5.2 --- Deficiency and Improvement --- p.71 / Chapter 5.3 --- Future Research Aspect --- p.73 / Appendix --- p.75 / Chapter A --- Data Details --- p.76 / Bibliography --- p.79

Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2008. / Includes bibliographical references.

MCom (Business Information Systems) / Deparment of Business Information Systems / The extent of success of a given project can be increased by using an appropriate Project Management Methodology (PMM) that takes into account the specific characteristics of the project (such as complexity, size, budget, nature of risk, etc.). PMMs have evolved over the years to become more diverse, complex, with evolving and dynamic ICT platforms. Such PMMs have traditionally been used as frameworks to guide the project management process for decision makers (such as Project Managers, Project Owners and Project Teams). The choice of selecting an appropriate project methodology is daunting; apart from other considerations related to project characteristics such as budget, scope, schedule, performance and resource constraints. One of the vital stages of a successful software development project is selecting a good software development methodology that best suits that project. The aim of this research is to investigate the critical factors to be considered by project managers in the selection of the software development methodology for the project. These critical factors are then used as a foundation for an architecture for an “unbounded rule-based expert system. A survey was conducted amongst project managers to determine the critical factors necessary for the selection of a software development methodology. From the findings of the study, it was established the critical factors revolved around three constructs of Project Excellence Enablers, Excellent Project Management Practices, and Business Value Proposition factors. The findings from this study therefore provided a rationale and a basis for the evolution of an “Unbounded Rule-Based Expert Systems Architecture” as a basis for the selection of the right software development methodology / NRF

Computer systems for the interpretation of diagnostic ECGs are widely used, but currently provide no explanatory or teaching functions of value to the less experienced practitioner. The relevant literature is reviewed, and specifications are provided for an ECG analysis system which will function as a learning aid for undergraduate and postgraduate medical students. Key aspects of the specifications are implemented on an IBM-PC. Recommendations for further development are provided.

No description available.

This master thesis deals with the topic of expert systems in the military. In the first part, expert systems are described in general focusing on the theoretical aspects. The creation, types, structure, architecture, advantages, disadvantages and history of expert systems and knowledge acquisition are described. The next part of the thesis deals with the specific military and security (defence) applications of expert systems. There are descriptions of applications from Ground forces (ADRIES, ESROC, KBGIS, PRIDE, TED systems), Air forces (AIRID, LES, TATR), NAVY (systems BATTLE, SIAMES systems), Joint forces (AI-EOD, CMES, GTEX, MBEES, MCTA, SEAT SEC systems) and security areas (EPS-APES, DOC - EXPLOIT, FRIEND - FOE / FIRE DECISION systems). A more detailed description of ADRIES, BATTLE, KBGIS a FRIEND - FOE DETECTION / FIRE DECISION expert systems is included in the final part of the thesis.

This dissertation describes a research study conducted to determine the benefits and use of ontology technologies to support a simulation environment that evaluates countermeasures employed to protect military aircraft. Within the military, aircraft represent a significant investment and these valuable assets need to be protected against various threats, such as man-portable air-defence systems. To counter attacks from these threats, countermeasures are deployed, developed and evaluated by utilising modelling and simulation techniques. The system described in this research simulates real world scenarios of aircraft, missiles and countermeasures in order to assist in the evaluation of infra-red countermeasures against missiles in specified scenarios. Traditional ontology has its origin in philosophy, describing what exists and how objects relate to each other. The use of formal ontologies in Computer Science have brought new possibilities for modelling and representation of information and knowledge in several domains. These advantages also apply to military information systems where ontologies support the complex nature of military information. After considering ontologies and their advantages against the requirements for enhancements of the simulation system, an ontology was constructed by following a formal development methodology. Design research, combined with the adaptive methodology of development, was conducted in a unique way, therefore contributing to establish design research as a formal research methodology. The ontology was constructed to capture the knowledge of the simulation system environment and the use of it supports the functions of the simulation system in the domain. The research study contributes to better communication among people involved in the simulation studies, accomplished by a shared vocabulary and a knowledge base for the domain. These contributions affirmed that ontologies can be successfully use to support military simulation systems / Computing / M. Tech. (Information Technology)