This research project aims to develop a framework to manage uncertainty in cloud manufacturing for small and medium enterprises (SMEs). The framework includes a cloud manufacturing taxonomy; guidance to deal with uncertainty in cloud manufacturing, by providing a process to identify uncertainties; a detailed step-by-step approach to managing the uncertainties; a list of uncertainties; and response strategies to security and privacy uncertainties in cloud manufacturing. Additionally, an online assessment tool has been developed to implement the uncertainty management framework into a real life context. To fulfil the aim and objectives of the research, a comprehensive literature review was performed in order to understand the research aspects. Next, an uncertainty management technique was applied to identify, assess, and control uncertainties in cloud manufacturing. Two well-known approaches were used in the evaluation of the uncertainties in this research: Simple Multi-Attribute Rating Technique (SMART) to prioritise uncertainties; and a fuzzy rule-based system to quantify security and privacy uncertainties. Finally, the framework was embedded into an online assessment tool and validated through expert opinion and case studies. Results from this research are useful for both academia and industry in understanding aspects of cloud manufacturing. The main contribution is a framework that offers new insights for decisions makers on how to deal with uncertainty at adoption and implementation stages of cloud manufacturing. The research also introduced a novel cloud manufacturing taxonomy, a list of uncertainty factors, an assessment process to prioritise uncertainties and quantify security and privacy related uncertainties, and a knowledge base for providing recommendations and solutions.

Made available in DSpace on 2015-03-03T15:47:46Z (GMT). No. of bitstreams: 1 AndersonPC_DISSERT.pdf: 1402040 bytes, checksum: 960b15bc1392a94fb7ba8ba980e3a0b4 (MD5) Previous issue date: 2012-12-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Atualmente, h? diferentes defini??es de implica??es fuzzy aceitas na literatura. Do ponto de vista te?rico, esta falta de consenso demonstra que h? discord?ncias sobre o real significado de "implica??o l?gica" nos contextos Booleano e fuzzy. Do ponto de vista pr?tico, isso gera d?vidas a respeito de quais "operadores de implica??o" os engenheiros de software devem considerar para implementar um Sistema Baseado em Regras Fuzzy (SBRF). Uma escolha ruim destes operadores pode implicar em SBRF's com menor acur?cia e menos apropriados aos seus dom?nios de aplica??o. Uma forma de contornar esta situa??o e conhecer melhor os conectivos l?gicos fuzzy. Para isso se faz necess?rio saber quais propriedades tais conectivos podem satisfazer. Portanto, a m de corroborar com o significado de implica??o fuzzy e corroborar com a implementa??o de SBRF's mais apropriados, v?rias leis Booleanas t?m sido generalizadas e estudadas como equa??es ou inequa??es nas l?gicas fuzzy. Tais generaliza??es s?o chamadas de leis Boolean-like e elas n?o s?o comumente v?lidas em qualquer sem?ntica fuzzy. Neste cen?rio, esta disserta??o apresenta uma investiga??o sobre as condi??es suficientes e necess?rias nas quais tr?s leis Booleanlike ?like ? y ? I(x, y), I(x, I(y, x)) = 1 e I(x, I(y, z)) = I(I(x, y), I(x, z)) ?? se mant?m v?lidas no contexto fuzzy, considerando seis classes de implica??es fuzzy e implica??es geradas por automorfismos. Al?m disso, ainda no intuito de implementar SBRF's mais apropriados, propomos uma extens?o para os mesmos / There are more than one acceptable fuzzy implication definitions in the current literature dealing with this subject. From a theoretical point of view, this fact demonstrates a lack of consensus regarding logical implication meanings in Boolean and fuzzy contexts. From a practical point of view, this raises questions about the implication operators" that software engineers must consider to implement a Fuzzy Rule Based System (FRBS). A poor choice of these operators generates less appropriate FRBSs with respect to1 their application domain. In order to have a better understanding of logical connectives, it is necessary to know the properties that they can satisfy. Therefore, aiming to corroborate with fuzzy implication meaning and contribute to implementing more appropriate FRBSs to their domain, several Boolean laws have been generalized and studied as equations or inequations in fuzzy logics. Those generalizations are called Booleanlike laws and a lot of them do not remain valid in any fuzzy semantics. Within this context, this dissertation presents the investigation of sucient and necessary conditions under which three Boolean-like laws | y I(x; y), I(x; I(y; x)) = 1 and I(x; I(y; z)) = I(I(x; y); I(x; z)) | hold for six known classes of fuzzy implications and for implications generated by automorphisms. Moreover, an extension to FRBSs is proposed

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.

Neuro-fuzzy modeling is an efficient computing paradigm for system modeling problems. It mainly integrates two well-known approaches, neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. We propose in this thesis two self-constructing rule generation methods, i.e., similarity-based rule generation (SRG) and similarity-and-merge-based rule generation (SMRG), and one hybrid learning algorithm (HLA) for structure identification and parameter identification, respectively, of neuro-fuzzy modeling. SRG and SMRG group the input-output training data into a set of fuzzy clusters incrementally based on similarity tests on the input and output spaces. Membership functions associated with each cluster are defined according to statistical means and deviations of the data points included in the cluster. Additionally, SMRG employs a merging mechanism to merge similar clusters dynamically. Then a zero-order or first-order TSK-type fuzzy IF-THEN rule is extracted from each cluster to form an initial fuzzy rule-base which can be directly employed for fuzzy reasoning or be further refined in the next phase of parameter identification. Compared with other methods, both our SRG and SMRG have advantages of generating fuzzy rules quickly, matching membership functions closely with the real distribution of the training data points, and avoiding the generation of the whole set of clusters from the scratch when new training data are considered. Besides, SMRG supports a more reasonable and quick mechanism for cluster merging to alleviate the problems of data-input-order bias and redundant clusters, which are encountered in SRG and other incremental clustering approaches. To refine the fuzzy rules obtained in the structure identification phase, a zero-order or first-order TSK-type fuzzy neural network is constructed accordingly in the parameter identification phase. Then, we develop a HLA composed by a recursive SVD-based least squares estimator and the gradient descent method to train the network. Our HLA has the advantage of alleviating the local minimal problem. Besides, it learns faster, consumes less memory, and produces lower approximation errors than other methods. To verify the practicability of our approaches, we apply them to the applications of function approximation and classification. For function approximation, we apply our approaches to model several nonlinear functions and real cases from measured input-output datasets. For classification, our approaches are applied to a problem of human object segmentation. A fuzzy self-clustering algorithm is used to divide the base frame of a video stream into a set of segments which are then categorized as foreground or background based on a combination of multiple criteria. Then, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network which is constructed with the fuzzy rules previously obtained and is trained by our proposed HLA. Experimental results show that our approaches can improve the accuracy of human object identification in video streams and work well even when the human object presents no significant motion in an image sequence.

Wireless sensor networks are application-specific networks that necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. The most important challenge related to wireless sensor networks is the limited energy and computational resources of the battery powered sensor nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted and the total energy consumed by sensor nodes, but also produces scalable and fault-tolerant networks. Another important challenge associated with wireless sensor networks is that the possibility of sensory data being imperfect and imprecise is high. The requirement of precision necessitates employing expensive mechanisms such as redundancy or use of sophisticated equipments. Therefore, approximate computing may need to be used instead of precise computing to conserve energy. This thesis presents two schemes that distribute information processing for event-driven reactive applications, which are interested in higher-level information not in the raw sensory data of individual nodes, to appropriate nodes in sensor networks. Furthermore, based on these schemes, a fuzzy rule-based system is proposed that handles imprecision, inherently present in sensory data.

Made available in DSpace on 2014-12-17T14:56:03Z (GMT). No. of bitstreams: 1 FabioSL.pdf: 932823 bytes, checksum: c32fee13ca97b70482987a8228b171cd (MD5) Previous issue date: 2004-07-15 / The main objective of work is to show procedures to implement intelligent control strategies. This strategies are based on fuzzy scheduling of PID controllers, by using only standard function blocks of this technology. Then, the standardization of Foundation Fieldbus is kept. It was developed an environment to do the necessary tests, it validates the propose. This environment is hybrid, it has a real module (the fieldbus) and a simulated module (the process), although the control signals and measurement are real. Then, it is possible to develop controllers projects. In this work, a fuzzy supervisor was developed to schedule a network of PID controller for a non-linear plant. Analyzing its performance results to the control and regulation problem / Com o objetivo de se manter a padroniza??o Foundation Fieldbus, neste trabalho s?o apresentados procedimentos para se implementar estrat?gias de controle inteligente, baseadas em escalonamento nebuloso de controladores PID, utilizando-se apenas blocos funcionais padr?es dessa tecnologia. Para validar a proposta, foi desenvolvido um ambiente para realiza??o dos testes necess?rios. Este ambiente ? h?brido, ou seja, possui uma parte real (a rede industrial) e uma parte simulada (o processo), por?m os sinais de controle e medi??o s?o reais. Desta forma, ? poss?vel desenvolver projetos de controladores. Neste trabalho desenvolveu-se um supervisor fuzzy para escalonar uma rede de controladores PID para uma determinada planta n?o-linear, sendo analisados seus resultados de desempenho tanto para o problema de controle quanto de regula??o

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

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The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.