The Marine Corps Systems Command documented mission requirements that cannot be met by current rebreathers. They need to extend dive times without compromising the stealth and compact design of existing devices. This can be accomplished by reducing the fresh gas flow rate. The current flow rate is adequate to support a diver in heavy work. However, the diver will be utilizing a Diver Propulsion Device (DPD) during a large portion of the mission in question. The assumption, then, is that this portion of the mission will not require "hard work". Thus, a new fresh gas flow rate can be established which is sufficient to sustain a Marine diver using a DPD but is conservative enough to extend the duration of the dive. This experiment was designed for manned testing of the rebreathers in such a way to establish the average oxygen consumption rate for divers using a DPD. Marine divers were fitted with a Divex Shadow Excursion (DSE) rebreather modified with a Draeger C8A PO2 monitor coupled with a Delta P VR3 dive computer. The DSE is a semiclosed-circuit underwater breathing apparatus that provides a constant flow of mixed gas containing oxygen and nitrogen or helium to the diver. The partial pressure of oxygen (PO2) and diver depth were monitored and recorded at ten-second intervals. The Navy Experimental Diving Unit has developed and tested a computational algorithm that uses the PO2 and depth to compute the oxygen consumption rate. Two techniques were employed to estimate the error in this approach: curve fitting and propagation of error. These methods are detailed and the results are presented. They show that the fresh gas flow rate can be safely reduced while the diver is utilizing the DPD, which consequently, will substantially increase the dive time allowed by the device.

The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.

The development of an add-on sensorless position estimator for a 4.8 m Linear Switched Reluctance Machine (LSRM) with minimal modifications to the transducer-based controller is investigated for the first time in this study. LSRMs require position feedback for closed-loop control but present a low cost, high energy efficiency alternative for linear actuation due to their rugged construction and single-sided excitation. Mechanical position transducers mounted on the vehicle are expensive and can impact reliability. The use of a sensorless position estimator removes all electronics from the passive vehicle, resulting in considerable reductions in cost, maintenance, and mechanical complexity. This study examines the use of an add-on processor and data acquisition system for sensorless position estimation. An approach exploiting the active phase windings is used to preserve the normal operation of the transducer-based DSP controller with the goal of limiting reductions in high performance features such as force ripple reduction and velocity control [3]. The estimator system is retrofit to the transducer-based DSP controller by mimicking the output of a mechanical position sensor by emulating a Quadrature encoder. The feasibility and design issues for an add-on or retrofit position estimator are investigated. Although sensorless schemes for rotary Switched Reluctance Machines (SRMs) have been studied in detail, the problem of sensorless implementations for LSRMs has not been addressed. Experimental validation of the proposed sensorless estimation scheme is attempted, but closed-loop operation is not achieved successfully due to air gap fluctuations. In depth analysis of the sources and propagation of error is presented. / Master of Science