Thesis (Ph. D.)--Georgia State University, 2005. / 1 electronic text (119 p. : ill.) : digital, PDF file. Title from title screen. Rajshekhar Sunderraman, committee chair; Yan-Qing Zhang, Anu Bourgeois, Lifeng Ding, committee members. Description based on contents viewed Apr. 3, 2007. Includes bibliographical references (p. 112-119).

Thesis (M.S.)--Baylor University, 2005.

Thesis (Ph. D.)--University of Rhode Island, 2005. / Typescript. Includes bibliographical references (v. 2: leaves 582-593).

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Traditionally, the fuzzy rules for a fuzzy controller are provided by experts. They cannot be trained from a set of input-output training examples because the correct response of the plant being controlled is delayed and cannot be obtained immediately. In this paper, we propose a novel approach to construct fuzzy rules for a fuzzy controller based on reinforcement learning. Our task is to learn from the delayed reward to choose sequences of actions that result in the best control. A neural network with delays is used to model the evaluation function Q. Fuzzy rules are constructed and added as the learning proceeds. Both the weights of the Q-learning network and the parameters of the fuzzy rules are tuned by gradient descent. Experimental results have shown that the fuzzy rules obtained perform effectively for control.

It is preferable that control and bearing condition monitoring are integrated, as the condition of the system should influence control actions. As wind turbines mainly work in remote areas, it becomes necessary to develop a wireless platform for the control system. A fuzzy system with self-tuning mechanism was developed. The input speed error and speed change were selected to control the shaft speed, while the kurtosis and peak-to-peak values were used as another set of inputs to monitor the bearing conditions. To enhance effectiveness, wait-and-see (WAS) logic was used as the pre-processing step for the raw vibration signal. The system was implemented on the LabVIEW platform. Experiments have shown that the system can effectively adjust motor rotating speed in response to bearing conditions. For future studies, more advanced fault detection methods can be integrated with proper tuning mechanisms to enrich the performance and function of the controller.

Cryptographic credentials allow user authorizations to be granted and verified. and have such applications as e-Passports, e-Commerce, and electronic cash. This thesis proposes a privacy protecting approach of binding biometrically derived keys to cryptographic credentials to prevent unauthorized lending. Our approach builds on the 2011 work of Adams, offering additional benefits of privacy protection of biometric information, generality on biometric modalities, and performance. Our protocol integrates into Brands’ Digital Credential scheme, and the Anonymous Credentials scheme of Camenisch and Lysyanskaya. We describe a detailed integration with the Digital Credential Scheme and sketch the integration into the Anonymous Credentials scheme. Security proofs for non-transferability, correctness of ownership, and unlinkability are provided for the protocol’s instantiation into Digital Credentials. Our approach uses specialized biometric devices in both the issue and show protocols. These devices are configured with our proposed primitive, the fuzzy ex-tractor indistinguishability adaptor which uses a traditional fuzzy extractor to create and regenerate cryptographic keys from biometric data and IND-CCA2 secure en-cryption protect the generated public data against multiplicity attacks. Pedersen commitments are used to hold the key at issue and show time, and A zero-knowledge proof of knowledge is used to ensure correspondence of key created at issue-time and regenerated at show-time. The above is done in a manner which preserves biometric privacy, as and delivers non-transferability of digital credentials. The biometric itself is not stored or divulged to any of the parties involved in the protocol. Privacy protection in multiple enrollments scenarios is achieved by the fuzzy extractor indistinguishability adapter. The zero knowledge proof of knowledge is used in the showing protocol to prove knowledge of values without divulging them.

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

With the rapid growth of the digital imaging, image processing techniques are widely involved in many industrial and medical applications. Image thresholding plays an essential role in image processing and computer vision applications. It has a vast domain of usage. Areas such document image analysis, scene or map processing, satellite imaging and material inspection in quality control tasks are examples of applications that employ image thresholding or segmentation to extract useful information from images. Medical image processing is another area that has extensively used image thresholding to help the experts to better interpret digital images for a more accurate diagnosis or to plan treatment procedures. Opposition-based computing, on the other hand, is a recently introduced model that can be employed to improve the performance of existing techniques. In this thesis, the idea of oppositional thresholding is explored to introduce new and better thresholding techniques. A recent method, called Opposite Fuzzy Thresholding (OFT), has involved fuzzy sets with opposition idea, and based on some preliminary experiments seems to be reasonably successful in thresholding some medical images. In this thesis, a Weighted Opposite Fuzzy Thresholding method (WOFT) will be presented that produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using both synthetic and real world images. Experimental evaluations were conducted on two sets of synthetic and medical images to validate the robustness of the proposed method in improving the accuracy of the thresholding process when fuzzy and oppositional ideas are combined.