Extensive brackishwater aquaculture, which is a dominant land-based aquaculture system in Indonesia, has experienced variable success in most farming locations in the country due to poor understanding of spatial assessment of environmental factors and rudimentary site selection criteria. Despite tremendous potential, the application of GIS and remote sensing in spatial assessment has tended to focus on Boolean (Crisp) logic that is often unable to effectively handle the complexity and spatial variability of key environmental factors for the development of aquaculture. This study explored the possibility of integrating fuzzy logic techniques into GIS and remote sensing technology to generate more robust mapping protocols in aquaculture, compensating for the disadvantages of the Crisp method. Two models were developed in two different provinces in Indonesia to spatially assess soil and hydrological constraints on extensive brackishwater aquaculture. The soil assessment focussed on acid sulfate soils (ASS) and sandy-textured sediments in Aceh, and the hydrological study focused on investigating important wave parameters that influence the suitability of coastal areas for siting extensive pond units in South Sulawesi. The study showed that fuzzy-based classification methods, integrated into the image analysis, was highly effective in identifying existing and potential pond areas for extensive brackishwater aquaculture compared to the best result of the commonly used Crisp method. By addition of one or more key environmental variables of ASS into the fuzzy-classified existing and potential ponds areas, a very robust predictive tool to identify potential ponds areas affected by ASS in Kembang Tanjung, Aceh was developed. A more detailed assessment of ASS developed in this study also successfully highlighted the severity of sandy-soils and identified them as another key soil variable that has and will severely impact on pond productivity. The second model developed by the study enables fuzzy logic to be integrated into GIS to predict the possible areas impacted by moderate to high energy wave conditions and possible ways of minimising their direct and indirect impacts. The models developed in this study were shown to work well in both study sites and can be applied elsewhere. The mapping outputs are easy to interpret even by stakeholders with no prior training in map reading. Overall, the models have the potential to reduce planning errors and to improve decision making in aquaculture provided that quality data sources are used.

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Also available online.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Recent technical progress has made new forms of controller implementations on computers possible. Especially the technique of Fuzzy Logic Control has found a growing number of applications. Also the development of fast A/D converters has made the acquisation of data with vision based systems possible. In this project we combine the two techniques of Fuzzy Logic Control and Vision Feedback to control an inverted pendulum and to determine their usefulness and limitations. The experiment was conducted and provided us with the data necessary to judge the performance of the new control strategy. The gathered data support the hypothesis that it is possible to control the inverted pendulum with Fuzzy Logic Control using Vision Feedback, though not without limitations. / Graduation date: 1995

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 212-221).

In this thesis, we develop a method using fuzzy logic to do cooperative localization. In a group of robots, at a given instant, each robot gives crisp pose estimates for all the other robots. These crisp pose values are converted to fuzzy membership functions based on various physical factors like acceleration of the robot and distance of separation of the two robots. For a given robot, all these fuzzy estimates are taken and fused together using fuzzy fusion techniques to calculate a possibility distribution function of the pose values. Finally, these possibility distributions are defuzzified using fuzzy techniques to find a crisp pose value for each robot. A MATLAB code is written to simulate this fuzzy logic algorithm. A Kalman filter approach is also implemented and then the results are compared qualitatively and quantitatively.

This thesis addresses the issue of ground collision in poor weather conditions. As bad weather is an adverse factor when airplanes are taxiing, an obstacle detection system based on thermal vision is proposed to enhance the awareness of pilots during taxiing in poor weather conditions. Two infrared cameras are employed to detect the objects and estimate the distance of the obstacle. The distance is computed by stereo vision technology. A warning will be given if the distance is less than the safe distance predefined. To make the system independent, the proposed system is an on-board system which does not rely on airports or other airplanes. The type of obstacle is classified by the temperature of the object. Fuzzy logic is employed in the classification. Obstacles are classified into three main categories: aircraft, vehicle and people. Membership functions are built based on the temperature distribution of obstacles measured at the airport. In order to improve the accuracy of classification, a concept of using position information is proposed. Different types of obstacle are predefined according to different area at the airport. In the classification, obstacles are classified according to the types limited in that area. Due to the limitation of the thermal infrared camera borrowed, images were captured first and then processed offline. Experiments were carried out to evaluate the detecting distance error and the performance of system in poor weather conditions. The classification of obstacle is simulated with real thermal images and pseudo position information at the airport. The results suggest that the stereo vision system developed in this research was able to detect the obstacle and estimate the distance. The classification method classified the obstacles to a certain extent. Therefore, the proposed system can improve safety of aircraft and enhance situational awareness of pilots. The programming language of the system is Python 2.7. Computer graphic library OpenCV 2.3 is used in processing images. MATLAB is used in the simulation of obstacle classification.

Adequate control of the position of the plasma column within the STOR-M tokamak is a chief requirement in order for experimental quality discharges to be obtained. Optimal control over tokamak discharge parameters, including the plasma position, is very difficult to achieve. This is due in large part to the difficulty in modelling the tokamak discharge parameters, as they are highly nonlinear and time varying in nature. The difficulty of modelling the tokamak discharge parameters suggests that a control system, such as a fuzzy logic based controller, which does not require a system model may be well suited to the control of fusion plasma. In order to improve the quality of control over the plasma position within the STOR-M tokamak, the existing analog PID controller was modified. These modifications facilitate the application of a digital controller by a personal computer via the Advantech PCL-711B data acquisition card. The performance of the modified plasma position controller and an Arbitrary Signal Generator developed by the author was evaluated. This modified plasma position controller was applied successfully to the STOR-M tokamak during both normal mode and A.C. mode operation. In both cases, the modified controller provided adequate control over the position of the plasma column within the discharge chamber. Furthermore, the modified controller was more convenient to optimize than the original, existing analog PID controller. By taking advantage of the modifications that were made to the plasma position controller, a fuzzy logic controller was developed by the author. The fuzzy logic based plasma position controller was also successfully applied to the STOR-M tokamak during both normal mode and A.C. operation. The fuzzy controller was demonstrated to reliably provide a higher degree of control over the position of the plasma column within the STOR-M tokamak than the modified PID controller.

Adequate control of the position of the plasma column within the STOR-M tokamak is a chief requirement in order for experimental quality discharges to be obtained. Optimal control over tokamak discharge parameters, including the plasma position, is very difficult to achieve. This is due in large part to the difficulty in modelling the tokamak discharge parameters, as they are highly nonlinear and time varying in nature. The difficulty of modelling the tokamak discharge parameters suggests that a control system, such as a fuzzy logic based controller, which does not require a system model may be well suited to the control of fusion plasma. In order to improve the quality of control over the plasma position within the STOR-M tokamak, the existing analog PID controller was modified. These modifications facilitate the application of a digital controller by a personal computer via the Advantech PCL-711B data acquisition card. The performance of the modified plasma position controller and an Arbitrary Signal Generator developed by the author was evaluated. This modified plasma position controller was applied successfully to the STOR-M tokamak during both normal mode and A.C. mode operation. In both cases, the modified controller provided adequate control over the position of the plasma column within the discharge chamber. Furthermore, the modified controller was more convenient to optimize than the original, existing analog PID controller. By taking advantage of the modifications that were made to the plasma position controller, a fuzzy logic controller was developed by the author. The fuzzy logic based plasma position controller was also successfully applied to the STOR-M tokamak during both normal mode and A.C. operation. The fuzzy controller was demonstrated to reliably provide a higher degree of control over the position of the plasma column within the STOR-M tokamak than the modified PID controller.