The condition of the fetus during labour is inferred from the continuous plot of fetal heart rate and uterine contractions (cardiotocogram, CTG). This can be _ difficult to interpret which results in both unnecessary intervention and a failure to intervene when necessary causing potentially preventable neurological damage and mortality. Conventional computing approaches have not been successful in addressing these problems. This is perhaps because the correct interpretation of fetal condition requires physiological knowledge, considerable practical experience and knowledge of the specific patient. The work described in this thesis is concerned with the investigation of artificial intelligence techniques to assist in the interpretation of fetal condition and advise on labour management. A fundamental investigation examined the performance of five types of scalp electrodes for obtaining the fetal electrocardiogram (ECG), from which heart rate is derived, and examined the factors which hamper fetal ECG data acquisition. New methods were developed to classify the important features from the CTG and included an investigation using neural networks. Other CTG features were classified using novel numerical algorithms developed closely with experts. An expert system, guided by a database of rules obtained from experts, was used to process and interpret changes in the CTG features by taking account of patient specific information. This hybrid approach was adopted to improve performance and reliability. After two internal evaluations had found the system obtained a performance comparable with local experts, an extensive external validation was undertaken. This study involved 17 experts from 16 leading centres within the UK. Each expert and the system reviewed 50 cases twice, at least one month apart which contained those considered most difficult to interpret selected from a database of 2400 high risk labours. A novel method was developed to present all the relevant clinical information in a way which approximated the clinical situation. The reviewers scored each 15 minutes of recording according to the concern they had for the fetus and the management they considered appropriate. In this respect, this is the first reported study to examine the performance of expert obstetricians in the management of labour. A new method was derived to measure the agreement between the scores obtained and is applicable to other areas where it is required to measure the similarity between time related sequences. This study found that the experts agreed well and were consistent in their management of the cases. The system was indistinguishable from the experts, except it was more consistent, even when used by an engineer with little knowledge of labour management. This study has shown that expertise in fetal monitoring is achievable in which case the current evidence suggests that this is not being adequately transferred to clinicians. The challenge remains to formulate a method to effectively transfer knowledge to the labour ward and thereby address the real and practical problems which face fetal monitoring today. This study demonstrates that intelligent systems could provide the vehicle to achieve this. I dedicate this work to the memory of my father, Bradley Kenneth Keith with a hope that he always believed it possible. I know he would have had some interesting comments to make and I sadly miss the opportunity of discussing them with him. I also dedicate this work to my mother for always being there, and to my wife Michelle for her unwavering support, patience and most of all her encouragement throughout this work.

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This research has identified the performance measures congruent with each model during a business life cycle. Questionnaire surveys have also been carried out to complement and validate the theoretical models. The results of the survey generally confirm the expected measures derived from the theoretical models. The learning process for these newer industrial business organisations can be greatly reduced if the expertise and experience of the established manufacturing business organisations is made readily available. This is the motivation for this research and the methodology which has been developed. The research also proposes use of a knowledge based expert decision support system to encapsulate the methodology, and the wealth of expert knowledge in the domain of performance measures. A prototype knowledge based expert decision support system has been developed to test the concept. It is hoped that this research has achieved its aim to provide a new contribution in the manufacturing business organisation strategy domain and to the improvement of managerial productivity and effectiveness through better use of performance measures.

Autorotation maneuvers are required to perform a safe landing of a helicopter in cases of engine loss in a single engine vehicle and transmission or tail rotor malfunction. The rise of autonomous helicopter technology, and the pilot skill required to manually perform an autorotation, motivate the need for new autonomous autorotation control laws. Previous approaches to automatic control for this maneuver have relied on control law optimization based on a high-fidelity model of the helicopter, or have attempted to match recorded trajectories flown by an expert human pilot. In this paper, a new expert control system is proposed. The term “expert control system” is used because the system is intended to mimic the actions that a human pilot might take, does not require any iterative learning, model prediction, or optimization at runtime, and is based on an inference system that involves fuzzy logic, PID, and other conventional control techniques. The multi-stage control law drives the helicopter to a near-optimal steady-state descent and uses an estimate of the time to impact to safely flare and land the helicopter in the vast majority of flight conditions. The control law is validated using a full 6-degree-of-freedom simulation of both a full-size attack helicopter and a small hobby-class helicopter. The pro- posed control design is highly flexible and may be used to perform fully autonomous autorotation or to provide guidance to pilots during manual autorotation maneuvers.

No description available.

Doctor-patient partnership has been advocated for achieving effective diabetes management. Such a partnership requires empowering patients with proper knowledge and skills so they are capable of participating in decision-making, effective communication with health professionals and successful diabetes self-management. Although it is well known that patient education should be tailored and prioritised, little research on Computer-based Patient Education Systems (CPESs) has aimed at customising information that is flexible enough to adapt to firstly, the dynamic nature of patients' ongoing information needs and secondly, changes in their personal health and social circumstances. Moreover, except for indirect support for doctor-patient communication through tailored information, current CPESs do not aid patients in formulating their questions. / This thesis targets limitations of current CPESs and explores approaches for using information technology (IT) to support the doctor-patient partnership. Two approaches used to achieve the research goal are 1) providing essential information to individual patients - information that is not only relevant, but also prioritised; and 2) providing direct support for patients to generate personalised agendas prior to scheduled health visits. The innovative technologies that have been developed for implementing these two approaches include a comprehensive Diabetes Information Profile (DIP) for each patient, information tailoring and prioritisation algorithms (information algorithms), quiz tailoring and prioritisation algorithms (quiz algorithms), and agenda personalisation algorithms (which serve to populate an agenda question pool). The DIP includes data elements on a patient's lifestyle, diet profile, psychosocial profile, risk factors of diabetic complications, behaviour change profile, self-management profile, and clinical status. The information algorithms take into account these DIP elements, as well as patients' diabetes knowledge level (based on educational exposure) and individual information preferences. Collectively, the implementation of these approaches, using an extensible architecture based on Extensible Mark-up Language (XML) and Java technologies, is called “Violet Technology” (VT). A VT-based web portal has been developed and evaluated. / A two-phase evaluation was conducted through the Diabetes Centre of a metropolitan hospital. The first study evaluates the validity of the information algorithms through patient and healthcare provider assessment of prioritised information topics. The participants of the first study include 11 patients with diabetes, one General Practician (GP), one endocrinologist, two diabetes nurse educators and one dietician. The second trial evaluates the VT-based portal overall - including information, quiz and agenda personalisation algorithms - through a field trial of the portal with random selection of patients to treatment and control groups. In total, 27 patients, one GP, one endocrinologist, two nurse educators, and one dietician were involved in the second trial. The evaluations provide qualitative support for the relevance of information prioritisation by VT, and show acceptable consumer usability, as well as healthcare provider support, for the portal. The evaluations also revealed further incremental refinements to the information algorithms. / This thesis contributes a specific framework for the use of IT to support the doctor-patient partnership through prioritised information and integrated agenda formulation services. While a larger scale of evaluation is needed to establish patient health benefits, the results of the two initial studies are encouraging. This framework could be adapted for other chronic diseases, such as depression or asthma. It could also be used for other purposes, such as an intelligent information searching facility. A future VT framework should provide more explicit representation of patients' emotional supports and have further mechanisms for promoting patient behaviour changes. / Thesis (PhDInformationTechnology)--University of South Australia, 2005.

Thesis (MEng in Electronic Engineering (Research)) -- University of South Australia, 1995

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Thesis (Ph. D.)--University of Washington, 1988. / Vita. Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xiii, 151 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references (p. 104-107).