M.Ing. / Columbus Stainless, a subsidiary of Acerinox, manufactures stainless steel in their plant located in Middelburg, South Africa. During the hot rolling operation the steel is rolled on a 4-high finishing mill where strip movement perpendicular to the rolling direction occurs. This movement is undesirable because it causes inferior product quality and may also lead to downtime if the strip moves past the edge of the rolls. In the past the operator made adjustments to the relative alignment of the rolls in the mill in an attempt to limit the sideways movement of the strip. In order to improve product quality and production throughput, the manual action of adjusting the parallelism of the rolls was replaced with an automatic steering control system. Analysis of the process revealed that several variables have an impact on the way the strip reacts to changes in the alignment of rolls in the mill. An adaptive fuzzy logic control system was designed and implemented in the real time control system of the mill. During commissioning the system did not have an adverse effect on production and all initial project criteria were met, as was stipulated in Section 1.4 of this document. The control system improved the strip movement by an average of 11% on various products rolled. Based on production data, the system potentially prevented two coils from leaving the rolls during the month long evaluation period and saved 40 minutes of production time. If the savings in material losses and the potential gain in production time are added the possible anticipated monetary saving is estimated to be about 24 million Rand a year.

The main contribution in this thesis is the design of a robust AUV docking guidance and navigation approach that can guide and home an AUV towards an acoustic source located on an oriented bottom-mounted underwater docking station, under presence of unknown current disturbances and in the absence of any form of onboard velocity sensor. A Complementary Filter and various forms of Kalman Filters were separately formulated to estimate the current and vehicle positions with strategic vehicle manoeuvres. A current compensator uses the estimated current to maintain the desired vehicle course while under current disturbance. Tagaki-Sugeno-Kang Fuzzy Inference System was designed to realize fuzzy docking guidance manoeuvres. Finally, Monte Carlo runs were performed on a designed AUV docking simulator to evaluate the docking robustness against various docking conditions. Simulation results demonstrated robustness in the designed docking guidance and navigation approach. / by Hoe Eng Teo. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Os motores de indução trifásicos são largamente usados em vários setores da indústria. O dimensionamento da potência adequada de um motor de indução ou assíncrono trifásico, em função do comportamento das cargas acopladas ao eixo, continua em alguns casos impreciso pela falta de conhecimento mais completo do comportamento das cargas. A proposta deste trabalho consiste na utilização de sistemas Fuzzy como uma alternativa aos métodos tradicionais para levantamento do comportamento de carga e, em processos de controle, onde há a necessidade de conhecimento do comportamento do conjugado aplicado ao eixo do motor, enfocando diversos tipos de cargas encontrados em indústrias. Resultados de simulações são apresentados para validar a proposta deste trabalho. / The three phase induction motors are widely used in all industrial sectors. The selection procedure of the motor for a particular application is sometimes inaccurate due to the lack of complete knowledge about the load connected to its shaft. The proposal of this work consists of using Fuzzy system as an alternative tool to the classical methods for extraction of the load behavior and, in control process, where knowledge of the torque behavior applied to the motor shaft are need, focusing several types of loads found in industries. Simulation results are presented to validate the proposal of this work.

by Stephen Chi-tin Yang. / "Sept. 28, 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 158-163). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Yau Po Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-92). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.3 / Chapter 2.1 --- Temporal Theories --- p.3 / Chapter 2.2 --- Related Works --- p.4 / Chapter 2.2.1 --- Consistency and Satisfiability of Timing Specifications --- p.4 / Chapter 2.2.2 --- Symbolic Constraint Satisfaction --- p.5 / Chapter 3 --- Previous Developed Work --- p.7 / Chapter 3.1 --- Previous Problem Domain --- p.7 / Chapter 3.1.1 --- Basics of MC68000 Read Cycle --- p.7 / Chapter 3.2 --- Knowledge-based System Structure --- p.9 / Chapter 3.3 --- Diagnostic Reasoning Mechanisms --- p.10 / Chapter 3.4 --- Time Range Approach --- p.11 / Chapter 3.4.1 --- Time Range Representation --- p.11 / Chapter 3.4.2 --- Constraint Satisfaction of Time Ranges --- p.12 / Chapter 3.4.3 --- Constraint Propagation of Time Ranges --- p.13 / Chapter 3.5 --- Fuzzy Time Point Approach --- p.14 / Chapter 3.5.1 --- Fuzzy Time Point Models --- p.14 / Chapter 3.5.2 --- Definition of Fuzzy Time Points --- p.15 / Chapter 3.5.3 --- Constraint Propagation of Fuzzy Time Points --- p.17 / Chapter 3.5.4 --- Constraint Satisfaction of Fuzzy Time Points --- p.18 / Chapter 4 --- The Proposed Segmented Time Range Approach --- p.20 / Chapter 4.1 --- Introduction --- p.20 / Chapter 4.2 --- The Insufficiency of The Existing Time Range Approach --- p.22 / Chapter 4.3 --- Segmented Time Range Approach --- p.23 / Chapter 4.3.1 --- The Representation --- p.23 / Chapter 4.3.2 --- Constraint Propagation and Satisfaction --- p.25 / Chapter 4.3.3 --- Contributions --- p.25 / Chapter 4.3.4 --- Limitations --- p.29 / Chapter 4.4 --- Conclusion --- p.30 / Chapter 5 --- New Problem Domain and Our New System --- p.31 / Chapter 5.1 --- Introduction --- p.31 / Chapter 5.2 --- Pentium-SRAM Interfacing Problem --- p.31 / Chapter 5.2.1 --- Asynchronous SRAM Solution --- p.32 / Chapter 5.2.2 --- Synchronous SRAM Solution --- p.33 / Chapter 5.3 --- The Knowledge Base --- p.35 / Chapter 5.4 --- Characteristics of Our New System --- p.35 / Chapter 6 --- Burst Read Cycle --- p.37 / Chapter 6.1 --- Introduction --- p.37 / Chapter 6.2 --- Asynchronous SRAM Solution --- p.37 / Chapter 6.2.1 --- Implementation --- p.39 / Chapter 6.2.2 --- Implementation Results --- p.45 / Chapter 6.3 --- Synchronous SRAM Solution --- p.48 / Chapter 6.3.1 --- Implementation --- p.49 / Chapter 6.3.2 --- Implementation Results --- p.56 / Chapter 6.4 --- Conclusion --- p.58 / Chapter 7 --- Burst Write Cycle --- p.60 / Chapter 7.1 --- Introduction --- p.60 / Chapter 7.2 --- Asynchronous SRAM Solution --- p.60 / Chapter 7.2.1 --- Implementation --- p.61 / Chapter 7.2.2 --- Implementation Results --- p.67 / Chapter 7.3 --- Synchronous SRAM Solution --- p.71 / Chapter 7.3.1 --- Implementation --- p.71 / Chapter 7.3.2 --- Implementation Results --- p.79 / Chapter 7.4 --- Conclusion --- p.82 / Chapter 8 --- Conclusion --- p.83 / Chapter 8.1 --- Summary of Achievements --- p.83 / Chapter 8.2 --- Future Development --- p.86 / Appendix Some Characteristics of Our New System --- p.89 / Bibliography --- p.91

No description available.

[pt] Desde sua formulação, no início da década de setenta, o conceito de grupo estratégico é objeto de pesquisas teóricas e empíricas que buscam confirmar sua existência, sua contribuição à avaliação da performance e à formação das estratégias das empresas. Este trabalho soma-se a estas pesquisas, utilizando os conceitos da Visão Resource- Based e a aplicação de ferramentas de inteligência computacional, neste caso as redes neurais e os sistemas de inferência fuzzy, com o objetivo de contribuir para a discussão deste tema na superação de suas limitações e dos novos desafios que o aumento da complexidade das arenas competitivas trouxeram para as pesquisas do gerenciamento estratégico. A Visão Resource-Based fornece a base teórica para o desenvolvimento dos construtos: grau de inimitabilidade e grau de imobilidade, resultantes da exploração estratégica dos recursos da empresa. Estes construtos são propostos como dimensões de avaliação da semelhança estratégica entre as empresas de uma arena competitiva. A inteligência computacional fornece os meios de extração de informações subjetivas, e presentes em ambientes complexos, através da simulação do aprendizado, percepção, evolução e adaptação do raciocínio humano. O resultado é a proposição de um modelo de avaliação da existência de grupos estratégicos, utilizando os construtos Grau de Inimitabilidade e Grau de Imobilidade, e Sistemas Neuro-fuzzy. Este modelo é aplicado ao setor de supermercados como teste de validação do mesmo. / [en] Since its has introduced, in the beginning of the decade of seventy, the concept of strategic groups is object of theoretical and empirical research that aims to confirm its existence, its contribution to performance evaluation and the formulation of the strategies of the firms. This text join these research, using the Resource-Based Views framework and soft computing, in this case neural networks and fuzzy inference systems, with aims at contributing for the discussion of this subject to overcome its limitations and the new challenges, resulting increasingly complexity and competitive environment, for the strategic management research. The Resource-Based View framework supplies the theoretical underpinnings to use the inimitability degree and immobility degree, resultants of the strategical exploration of the resources of the firms, as constructors to evaluate firm strategic similarity in a competitive environment. Soft computing is a tool to extract subjective data from complexity environments, simulating the ability for learning, perception, evolution and adaptation of human reasoning. The result of this research is the proposal of a model to identify strategic groups, applying the constructors Inimitability Degree and Immobility Degree, and Neuro-fuzzy Inference Systems. To validate the model, a test is performed to the supermarkets industry.

Yue Ho-Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 129-137). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Definition --- p.7 / Chapter 1.2 --- Contributions --- p.8 / Chapter 1.3 --- Thesis Organization --- p.10 / Chapter 2 --- Literature Review --- p.11 / Chapter 2.1 --- "Content-based Retrieval, Background and Indexing Problem" --- p.11 / Chapter 2.1.1 --- Feature Extraction --- p.12 / Chapter 2.1.2 --- Nearest-neighbor Search --- p.13 / Chapter 2.1.3 --- Content-based Indexing Methods --- p.15 / Chapter 2.2 --- Indexing Problems --- p.25 / Chapter 2.3 --- Data Clustering Methods for Indexing --- p.26 / Chapter 2.3.1 --- Probabilistic Clustering --- p.27 / Chapter 2.3.2 --- Possibilistic Clustering --- p.34 / Chapter 3 --- Fuzzy Clustering Algorithms --- p.37 / Chapter 3.1 --- Fuzzy Competitive Clustering --- p.38 / Chapter 3.2 --- Sequential Fuzzy Competitive Clustering --- p.40 / Chapter 3.3 --- Experiments --- p.43 / Chapter 3.3.1 --- Experiment 1: Data set with different number of samples --- p.44 / Chapter 3.3.2 --- Experiment 2: Data set on different dimensionality --- p.46 / Chapter 3.3.3 --- Experiment 3: Data set with different number of natural clusters inside --- p.55 / Chapter 3.3.4 --- Experiment 4: Data set with different noise level --- p.56 / Chapter 3.3.5 --- Experiment 5: Clusters with different geometry size --- p.60 / Chapter 3.3.6 --- Experiment 6: Clusters with different number of data instances --- p.67 / Chapter 3.3.7 --- Experiment 7: Performance on real data set --- p.71 / Chapter 3.4 --- Discussion --- p.72 / Chapter 3.4.1 --- "Differences Between FCC, SFCC, and Others Clustering Algorithms" --- p.72 / Chapter 3.4.2 --- Variations on SFCC --- p.75 / Chapter 3.4.3 --- Why SFCC? --- p.75 / Chapter 4 --- Hierarchical Indexing based on Natural Clusters Information --- p.77 / Chapter 4.1 --- The Hierarchical Approach --- p.77 / Chapter 4.2 --- The Sequential Fuzzy Competitive Clustering Binary Tree (SFCC- b-tree) --- p.79 / Chapter 4.2.1 --- Data Structure of SFCC-b-tree --- p.80 / Chapter 4.2.2 --- Tree Building of SFCC-b-Tree --- p.82 / Chapter 4.2.3 --- Insertion of SFCC-b-tree --- p.83 / Chapter 4.2.4 --- Deletion of SFCC-b-Tree --- p.84 / Chapter 4.2.5 --- Searching in SFCC-b-Tree --- p.84 / Chapter 4.3 --- Experiments --- p.88 / Chapter 4.3.1 --- Experimental Setting --- p.88 / Chapter 4.3.2 --- Experiment 8: Test for different leaf node sizes --- p.90 / Chapter 4.3.3 --- Experiment 9: Test for different dimensionality --- p.97 / Chapter 4.3.4 --- Experiment 10: Test for different sizes of data sets --- p.104 / Chapter 4.3.5 --- Experiment 11: Test for different data distributions --- p.109 / Chapter 4.4 --- Summary --- p.113 / Chapter 5 --- A Case Study on SFCC-b-tree --- p.114 / Chapter 5.1 --- Introduction --- p.114 / Chapter 5.2 --- Data Collection --- p.115 / Chapter 5.3 --- Data Pre-processing --- p.116 / Chapter 5.4 --- Experimental Results --- p.119 / Chapter 5.5 --- Summary --- p.121 / Chapter 6 --- Conclusion --- p.122 / Chapter 6.1 --- An Efficiency Formula --- p.122 / Chapter 6.1.1 --- Motivation --- p.122 / Chapter 6.1.2 --- Regression Model --- p.123 / Chapter 6.1.3 --- Discussion --- p.124 / Chapter 6.2 --- Future Directions --- p.127 / Chapter 6.3 --- Conclusion --- p.128 / Bibliography --- p.129

Abstract not available