Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 60-62). / ix, 62 leaves, bound ill. 29 cm

Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 92-96). / 111 leaves, bound ill. (some col.) 29 cm

Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 78-82). / xiii, 82 leaves, bound ill. 29 cm

GPS has been used to monitor engineering structures for a number of reasons. One important reason for monitoring high rise buildings (and other engineering structures) is their safety assessment in events of extreme loading, such as earthquakes and storms. Decisions must be made as soon as possible, whether to allow re-occupation of such buildings, or to assess them for further damage. The time required to reach such decisions is cost-critical, both for the building owner or manager and for the agency doing the assessment. Peak inter-storey drift ratio and detection of permanent damage are some of the damage assessment parameters recommended by assessment agencies. Traditionally, accelerometers have been used to monitor these parameters. Accelerometers measure accelerations which are double-integrated to get displacements. These double integrated displacements are then used for computing the inter-storey drift ratios and locating permanent damage. Displacements obtained by double-integration and inter-storey drift ratios by subtraction of these displacements, are often erroneous and unreliable and direct measurement of displacement is preferred. Direct measurement of displacement is required at a number of points along the height of the building. For example, for computing inter-storey drift ratios, measurements of displacement at both the floor level and roof level are required. Such points on buildings and other engineering structures of vertical profile are termed as mid-height points in this thesis. While GPS has been used for deformation monitoring of engineering structures and to assist in damage assessment during and after extreme loading events, its use has been limited to roof top installations. / This research is an attempt to measure displacements at mid-height locations of engineering structures of vertical profile using GPS. (For complete abstract open document).

Man has a limited ability to accurately and continuously analyse large amounts of data. Observers are typically required to monitor displays over extended periods and to execute overt detection responses to the appearance of low probability critical signals. The signals are usually clearly perceivable when observers are alerted to them, but they can be missed in the operating environment. The challenge is to develop a computer application that will accumulate information on a variable, or several variables, over time and identify when the trend in observations has changed. In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring systems, expert systems and many other computer aided protocols. The expert systems have the potential to improve clinician performance by accurately executing repetitive tasks, to which humans are ill-suited. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. The decision support tools capable of detecting pathological events can enhance the anaesthetist’s performance by providing alternative diagnostic information. The main goal of this research was to develop a clinically useful diagnostic alarm system using two different techniques for monitoring a pathological event during anaesthesia. Several techniques including fuzzy logic, artificial neural networks, control and monitoring techniques were explored. Firstly, an industrial monitoring system called Supervisory Control and Data Acquisition (SCADA) software is used and implemented in the form of a prototype system called SCADA monitoring system (SMS). The output of the system in detecting hypovolaemia was classified into three levels; mild, moderate and severe using SCADA’s InTouch software. In addition, a new GUI display was developed for direct interaction with the anaesthetists. Secondly, a fuzzy logic monitoring system (FLMS) was developed using the fuzzy logic technique. New diagnostic rules and membership functions (MF) were developed using MATLAB. In addition, fuzzy inference system FIS, adaptive neuro fuzzy inference system ANFIS and clustering techniques were explored for developing the FLMS’s diagnostic modules. The raw physiological patient data acquired from an S/5 monitor were converted to a readable format using the DOMonitor application. The data was filtered, preprocessed, and analysed for detecting anaesthesia related events like hypovolaemia. The accuracy of diagnoses generated by SMS and FLMS was validated by comparing their diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s, SMS’s, and FLMS’s diagnoses. In offline analysis both systems were tested with data from 15 patients. The SMS and FLMS achieved an overall agreement level of 87 and 88 percent respectively. It implies substantial level of agreement between SMS or FLMS and the anaesthetists. These diagnostic alarm systems (SMS and FLMS) have shown that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in providing decision support to anaesthetists.

Man has a limited ability to accurately and continuously analyse large amounts of data. Observers are typically required to monitor displays over extended periods and to execute overt detection responses to the appearance of low probability critical signals. The signals are usually clearly perceivable when observers are alerted to them, but they can be missed in the operating environment. The challenge is to develop a computer application that will accumulate information on a variable, or several variables, over time and identify when the trend in observations has changed. In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring systems, expert systems and many other computer aided protocols. The expert systems have the potential to improve clinician performance by accurately executing repetitive tasks, to which humans are ill-suited. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. The decision support tools capable of detecting pathological events can enhance the anaesthetist’s performance by providing alternative diagnostic information. The main goal of this research was to develop a clinically useful diagnostic alarm system using two different techniques for monitoring a pathological event during anaesthesia. Several techniques including fuzzy logic, artificial neural networks, control and monitoring techniques were explored. Firstly, an industrial monitoring system called Supervisory Control and Data Acquisition (SCADA) software is used and implemented in the form of a prototype system called SCADA monitoring system (SMS). The output of the system in detecting hypovolaemia was classified into three levels; mild, moderate and severe using SCADA’s InTouch software. In addition, a new GUI display was developed for direct interaction with the anaesthetists. Secondly, a fuzzy logic monitoring system (FLMS) was developed using the fuzzy logic technique. New diagnostic rules and membership functions (MF) were developed using MATLAB. In addition, fuzzy inference system FIS, adaptive neuro fuzzy inference system ANFIS and clustering techniques were explored for developing the FLMS’s diagnostic modules. The raw physiological patient data acquired from an S/5 monitor were converted to a readable format using the DOMonitor application. The data was filtered, preprocessed, and analysed for detecting anaesthesia related events like hypovolaemia. The accuracy of diagnoses generated by SMS and FLMS was validated by comparing their diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s, SMS’s, and FLMS’s diagnoses. In offline analysis both systems were tested with data from 15 patients. The SMS and FLMS achieved an overall agreement level of 87 and 88 percent respectively. It implies substantial level of agreement between SMS or FLMS and the anaesthetists. These diagnostic alarm systems (SMS and FLMS) have shown that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in providing decision support to anaesthetists.

Thesis (Ph. D.)--University of Adelaide, 1984. / Appendix 7, (3 leaves) in pocket. Includes bibliographical references.

Thesis (M.S.C.E.)--University of Toledo, 2006. / Typescript. "Submitted as partial fulfillment of the requirements for Masters of Science degree in Civil Engineering." Bibliography: leaves 53-55.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2002. / Härtill 6 uppsatser.

Thesis (M.Env.Sc.)--University of Wollongong, 2007. / Typescript. Includes bibliographical references: leaf 83-98.