Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-29Bitstream added on 2014-06-13T19:08:09Z : No. of bitstreams: 1 spadotto_mm_me_bauru.pdf: 1459647 bytes, checksum: c67d870286e648ad917f7e25b8b18d56 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materiais cerâmicos tornou possível o emprego de processos de fabricação que antes eram somente empregados em metais. Dentre os processos de usinagem de cerâmicas avançadas, a retificação é o mais utilizado devido às maiores taxas de remoção diferentemente do brunimento e das limitações geométricas do processo de lapidação. A rugosidade é um do parâmetros de saída do processo de retificação que influi, dentre outros fatores, na qualidade do deslizamento entre estruturas, podendo gerar aquecimento. Além disso, o desgaste da ferramenta de corte gerado durante o processo está associado aos custos fixos e a problemas relacionados com o acabamento superficial bem como a danos estruturais. Essas duas variáveis, rugosidade e desgaste, são objetos de estudos de muitos pesquisadores. Entretanto, o controle automático tem sido uma difícil tarefa de ser realizada devido às variações de parâmetros ocorridas no processo. Dessa maneira, o presente trabalho tem por objetivo aplicar a lógica ANFIS (Adaptive Neuro-Fuzzy Inference System) na estimação da rogosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas. A ferramenta de corte aplicada para retificar os corpos-de-prova de alumina (96%) foi um rebolo diamantado. A partir do processamento digital dos sinais de emissão acústica e potência média de corte foram calculadas as estatísticas: média, desvio padrão, potência máxima, DPO e DPKS. As estatísticas foram aplicadas com entradas de duas redes ANFIS, uma estimando valores de rugosidade e outra estimando valores de desgaste... / The need for implementation of new equipaments in an increasingly agressive environmentl demanded a search for new products capable of withstanding high temperatures, inert to chemical corrosion and high mechanical stiffeness. Technological advances in the production of ceramic materials have become possible with the employment of manufacturing processes that previously were only employed in metals. Among the advanced ceramics machining processes, the grinding process is the most used, because of higher removal rates in constrast with the honing process and geometric limitations of lapping process. The surface reoughness is one of the output parameters of grinding process that affects, among other factors, the quality of sliding between structures that may generate heat. Moreover, the wear of the cutting tool generated during the process is associated with fixed costs and problems related to suface finishing as well as structural damages. These two variables, surface roughness and wear, have been studied by many researchers; however, the automatic control has been a difficult task to be carry out due to parameters variations occurring in the process. Hence, this work aims to apply logic ANFIS (Adaptive Neuro-Fuzzy Inference System) in the estimation of surface roughness and wear of the cutting tool in the tangential griding process of advanced ceramics. The cutting tools used to grind workpieces of alumina (96%) was a diamond grinding wheel. From the digital processing of acoustic emission and average cutting power signals some statistics were calculated: mean, standard deviation, maximum power, DPO and DPKS. The statistics were applied as inputs of two ANFIS networks estimating surface roughess and wear values. The results had demonstrated that the statistics associated with the ANFIS network can be used in the estimation of surface roughness and wear. However, the wear ANFIS network... (Complete abstract click electronic access below)

This thesis deals with suitable selection of suppliers and evaluates possible risk for Rybníkářství Pohořelice, a. s. company in this matter. The thesis submits sophisticated scoring pattern of possible supplier by using Fuzzy logic. The pattern is made in MS Excel and Fuzzy Logic Toolbox (MATLAB). This evaluation method should effectively resolve the suppliers scoring currently cooperating with the company and should be also available at affordable costs.

[pt] Hoje em dia, grande parte do conhecimento acumulado está armazenado em forma de dados. Para muitos problemas de classificação, tenta-se aprender a relação entre um conjunto de variáveis (atributos) e uma variável alvo de interesse. Dentre as ferramentas capazes de atuar como modelos representativos de sistemas reais, os Sistemas de Inferência Fuzzy são considerados excelentes com respeito à representação do conhecimento de forma compreensível, por serem baseados em regras linguísticas. Este quesito de interpretabilidade linguística é relevante em várias aplicações em que não se deseja apenas um modelo do tipo caixa preta, que, por mais precisão que proporcione, não fornece uma explicação de como os resultados são obtidos. Esta dissertação aborda o desenvolvimento de um Sistema de Inferência Fuzzy de forma automática, buscando uma base de regras que valorize a interpretabilidade linguística e que, ao mesmo tempo, forneça uma boa acurácia. Para tanto, é proposto o modelo AutoFIS-Class, um método automático para a geração de Sistemas de Inferência Fuzzy para problemas de classificação. As características do modelo são: (i) geração de premissas que garantam critérios mínimos de qualidade, (ii) associação de cada premissa a um termo consequente mais compatível e (iii) agregação de regras de uma mesma classe por meio de operadores que ponderem a influência de cada regra. O modelo proposto é avaliado em 45 bases de dados benchmark e seus resultados são comparados com modelos da literatura baseados em Algoritmos Evolucionários. Os resultados comprovam que o Sistema de Inferência gerado é competitivo, apresentando uma boa acurácia com um baixo número de regras. / [en] Nowadays, much of the accumulated knowledge is stored as data. In many classification problems the relationship between a set of variables (attributes) and a target variable of interest must be learned. Among the tools capable of modeling real systems, Fuzzy Inference Systems are considered excellent with respect to the knowledge representation in a comprehensible way, as they are based on inference rules. This is relevant in applications where a black box model does not suffice. This model may attain good accuracy, but does not explain how results are obtained. This dissertation presents the development of a Fuzzy Inference System in an automatic manner, where the rule base should favour linguistic interpretability and at the same time provide good accuracy. In this sense, this work proposes the AutoFIS-Class model, an automatic method for generating Fuzzy Inference Systems for classification problems. Its main features are: (i) generation of premises to ensure minimum, quality criteria, (ii) association of each rule premise to the most compatible consequent term; and (iii) aggregation of rules for each class through operator that weigh the relevance of each rule. The proposed model was evaluated for 45 datasets and their results were compared to existing models based on Evolutionary Algorithms. Results show that the proposed Fuzzy Inference System is competitive, presenting good accuracy with a low number of rules.

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

Linear repetitive projects, which are resource-driven in nature, are characterized by a series of repetitive activities in which the resources share the same space either in sequential or parallel manner. The frequent movement of resources over limited shared space needs to be well-planned to avoid potential issues during the execution of linear projects. As such, schedules developed for these projects needs not only to take into account all the logical, project-dependent and precedence constraints of activities but also to incorporate the space and time constraints that co-exist for the movement of thei8r resources. Negligence in incorporating spatial and temporal constraints in developing and improving schedules of linear projects increases the risk of delays and workspace congestions that can substantially hinder the performance of the activity resources. The study presented here proposes and develops an uncertainty-aware scheduling and control framework for linear projects to address the needs mentioned above. For this purpose, first, a new type of float was introduced as the Space-Time Float. The Space-Time Float is an envelope for all possible movement patterns that a linear activity or its associated resources can take considering the time and space constraints of that activity. The next endeavor in the development of the uncertainty-aware linear scheduling and control framework was to augment the current linear scheduling methods by presenting an uncertainty-aware optimization method to optimize the duration of linear projects while minimizing their potential congestions. A constraint satisfaction approach was used for the two-tier optimization of duration and congestion, and a fuzzy inference system was incorporated to assess the inherent uncertainty in linear activities. A new type of buffer, Uncertainty-Aware Productivity Buffer is also introduced to account for the uncertainties inherent in project activities. Spatial progress of activities needs not only to be considered in the planning phase but also to be closely monitored during construction. The framework presented in this study also applies to the monitoring and control of linear projects. While most of the current methods still do not accommodate real-time bi-directional control of linear projects, this framework is based on the Cyber-Physical Systems (CPS) architecture and bi-directional communication of data. To this end, a CPS-based application for Earned Value (EV) monitoring and control of road and highway projects is presented. Different steps of the generated framework are validated through various literature and field-based case studies. The results demonstrate the effectiveness of the presented method in planning and control of unforeseen variations from the planned schedules of linear projects. As such, the present study contributes and adds to the current body of knowledge of linear projects by presenting an efficient scheduling and control framework that takes into account logical, spatio-temporal and project-based constraints of linear activities. / Thesis / Doctor of Philosophy (PhD)

The objective of this thesis is to investigate the flow conditions that lead to coarse grain entrainment at near incipient motion conditions. Herein, a new conceptual approach is proposed, which in addition to the magnitude of hydrodynamic force or flow power, takes into account the duration of the flow event. Two criteria for inception of grain entrainment, namely the critical impulse and critical energy concepts, are proposed and compared. These frameworks adopt a force or energy perspective, considering the momentum or energy transfer from each flow event to the particle respectively, to describe the phenomenon. A series of conducted mobile particle experiments, are analyzed to examine the validity of the proposed approaches. First a set of bench-top experiments incorporates an electromagnet which applies pulses of known magnitude and duration to a steel spherical particle in a controlled fashion, so as to identify the critical level for entrainment. The utility of the above criteria is also demonstrated for the case of entrainment by the action of turbulent flow, via analysis of a series of flume experiments, where both the history of hydrodynamic forces exerted on the particle as well as its response are recorded simultaneously. Statistical modeling of the distribution of impulses, as well as conditional excess impulses, is performed using distributions from Extreme Value Theory to effectively model the episodic nature of the occurrence of these events. For the examined uniform and low mobility flow conditions, a power law relationship is proposed for describing the magnitude and frequency of occurrence of the impulse events. The Weibull and exponential distributions provide a good fit for the time between particle entrainments. In addition to these statistical tools, a number of Adaptive Neuro-Fuzzy Inference Systems employing different input representations are used to learn the nonlinear dynamics of the system and perform statistical prediction. The performance of these models is assessed in terms of their broad validity, efficiency and forecast accuracy. Even though the impulse and energy criteria are deeply interrelated, the latter is shown to be advantageous with regard to its performance, applicability and extension ability. The effect of single or multiple highly energetic events carried by certain coherent flow structures (mainly strong sweep events) with regard to the particle response is also investigated. / Ph. D.

Finite Element (FE) analysis is a powerful numerical technique widely utilized to simulate the real-world response of complex engineering structures. With the advancements in adaptive optimization frameworks, multi-fidelity (coupled shell-solid) FE models are increasingly sought during the early design stages where a large design space is being explored. This is because multi-fidelity models have the potential to provide accurate solutions at a much lower computational cost. However, the time and effort required to create accurate and optimal multi-fidelity models with acceptable meshes for highly complex structures is still significant and is a major bottleneck in the FE modeling process. Additionally, there is a significant level of subjectivity involved in the decision-making about the multi-fidelity element topology due to a high dependence on the analyst's experience and expertise, which often leads to disagreements between analysts regarding the optimal modeling approach and heavy losses due to schedule delays. Moreover, this analyst-to-analyst variability can also result in significantly different final engineering designs. Thus, there is a greater need to accelerate the FE modeling process by automating the development of robust and adaptable multi-fidelity models as well as eliminating the subjectivity and art involved in the development of multi-fidelity models. This dissertation presents techniques and frameworks for accelerating the finite element modeling process of multi-fidelity models. A framework for the automated development of multi-fidelity models with adaptable 2-D/3-D topology using the parameterized full-fidelity and structural fidelity models is presented. Additionally, issues related to the automated meshing of highly complex assemblies is discussed and a strategic volume decomposition technique blueprint is proposed for achieving robust hexahedral meshes in complicated assembly models. A comparison of the full-solid, full-shell, and different multi-fidelity models of a highly complex stiffened thin-walled pressure vessel under external and internal tank pressure is presented. Results reveal that automation of multi-fidelity model generation in an integrated fashion including the geometry creation, meshing and post-processing can result in considerable reduction in cost and efforts. Secondly, the issue of analyst-to-analyst variability is addressed using a Decision Tree (DT) based Fuzzy Inference System (FIS) for recommending optimal 2D-3D element topology for a multi-fidelity model. Specifically, the FIS takes the structural geometry and desired accuracy as inputs (for a range of load cases) and infers the optimal 2D-3D topology distribution. Once developed, the FIS can provide real-time optimal choices along with interpretability that provides confidence to the analyst regarding the modeling choices. The proposed techniques and frameworks can be generalized to more complex problems including non-linear finite element models and as well as adaptable mesh generation schemes. / Doctor of Philosophy / Structural analysis is the process of determining the response (mainly, deformation and stresses) of a structure under specified loads and external conditions. This is often performed using computational modeling of the structure to approximate its response in real-life conditions. The Finite Element Method (FEM) is a powerful and widely used numerical technique utilized in engineering applications to evaluate the physical performance of structures in several engineering disciplines, including aerospace and ocean engineering. As optimum designs are increasing sought in industries, the need to develop computationally efficient models becomes necessary to explore a large design space. As such, optimal multi-fidelity models are preferred that utilize higher fidelity computational domain in the critical areas and a lower fidelity domain in less critical areas to provide an optimal trade-off between accuracy and efficiency. However, the development of such optimal models involves a high level of expertise in making a-priori and a-posteriori optimal modeling decisions. Such experience based variability between analysts is often a major cause of schedule delays and considerable differences in final engineering designs. A combination of automated model development and optimization along with an expert system that relieves the analyst of the need for experience and expertise in making software and theoretical assumptions for the model can result in a powerful and cost-effective computational modeling process that accelerates technological advancements. This dissertation proposes techniques for automating robust development of complex multi-fidelity models. Along with these techniques, a data-driven expert system framework is proposed that makes optimal multi-fidelity modeling choices based on the structural configuration and desired accuracy level.

The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.

[pt] Este trabalho tem por objetivo criar um novo sistema de inferência fuzzy intervalar do tipo 2 para tratamento de incertezas com aprendizado automático e que proporcione um intervalo de confiança para as suas saídas defuzzificadas através do cálculo dos conjuntos tipo-reduzidos correspondentes. Para viabilizar este objetivo, este novo modelo combina os paradigmas de modelagem dos sistemas de inferência fuzzy do tipo 2 e redes neurais com técnicas de particionamento recursivo BSP. Este modelo possui principalmente a capacidade de modelar e manipular a maioria dos tipos de incertezas existentes em situações reais, minimizando os efeitos destas para produzir um melhor desempenho. Além disso, tem a capacidade autônoma de criar e expandir automaticamente a sua própria estrutura, de reduzir a limitação quanto ao número de entradas e de extrair regras de conhecimento a partir de um conjunto de dados. Este novo modelo fornece um intervalo de confiança, que se constitui em uma informação importante para aplicações reais. Neste contexto, este modelo supera as limitações dos sistemas de inferência fuzzy do tipo 2 - complexidade computacional, reduzido número de entradas permissíveis e forma limitada, ou inexistente, de criarem a sua própria estrutura e regras - e dos sistemas de inferência fuzzy do tipo 1 - adaptação incompleta a incertezas e não fornecimento de um intervalo de confiança para a saída. Os sistemas de inferência fuzzy do tipo1 também apresentam limitações quanto ao reduzido número de entradas permissíveis, mas o uso de particionamentos recursivos, já explorado com excelentes resultados [SOUZ99], reduz significativamente estas limitações. O trabalho constitui-se fundamentalmente em quatro partes: um estudo sobre os diferentes sistemas de inferência fuzzy do tipo 2 existentes, análise dos sistemas neuro-fuzzy hierárquicos que usam conjuntos fuzzy do tipo 1, modelagem e implementação do novo modelo neuro-fuzzy hierárquico BSP do tipo 2 e estudo de casos. O novo modelo, denominado modelo neuro-fuzzy hierárquico BSP do tipo 2 (NFHB-T2), foi definido a partir do estudo das características desejáveis e das limitações dos sistemas de inferência fuzzy do tipo 2 e do tipo 1 e dos sistemas neuro-fuzzy hierárquicos que usam conjuntos fuzzy do tipo 1 existentes. Desta forma, o NFHB-T2 é modelado e implementado com os atributos de interpretabilidade e autonomia, a partir da concepção de sistemas de inferência fuzzy do tipo 2, de redes neurais e do particionamento recursivo BSP. O modelo desenvolvido é avaliado em diversas bases de dados benchmark e aplicações reais de previsão e aproximação de funções. São feitas comparações com outros modelos. Os resultados encontrados mostram que o modelo NFHB-T2 fornece, em previsão e aproximação de funções, resultados próximos e em vários casos superiores aos melhores resultados proporcionados pelos modelos utilizados para comparação. Em termos de tempo computacional, o seu desempenho também é muito bom. Em previsão e aproximação de funções, os intervalos de confiança obtidos para as saídas defuzzificadas mostram-se sempre coerentes e oferecem maior credibilidade na maioria dos casos quando comparados a intervalos de confiança obtidos por métodos tradicionais usando as saídas previstas pelos outros modelos e pelo próprio NFHB-T2 . / [en] The objective of this thesis is to create a new type-2 fuzzy inference system for the treatment of uncertainties with automatic learning and that provides an interval of confidence for its defuzzified output through the calculation of corresponding type-reduced sets. In order to attain this objective, this new model combines the paradigms of the modelling of the type-2 fuzzy inference systems and neural networks with techniques of recursive BSP partitioning. This model mainly has the capacity to model and to manipulate most of the types of existing uncertainties in real situations, diminishing the effects of these to produce a better performance. In addition, it has the independent capacity to create and to expand its own structure automatically, to reduce the limitation referred to the number of inputs and to extract rules of knowledge from a data set. This new model provides a confidence interval, that constitutes an important information for real applications. In this context, this model surpasses the limitations of the type-2 fuzzy inference systems - complexity computational, small number of inputs allowed and limited form, or nonexistent, to create its own structure and rules - and of the type-1 fuzzy inference systems - incomplete adaptation to uncertainties and not to give an interval of confidence for the output. The type-1 fuzzy inference systems also present limitations with regard to the small number of inputs allowed, but the use of recursive partitioning, already explored with excellent results [SOUZ99], reduce significantly these limitations. This work constitutes fundamentally of four parts: a study on the different existing type-2 fuzzy inference systems, analysis of the hierarchical neuro- fuzzy systems that use type-1 fuzzy sets, modelling and implementation of the new type-2 hierarchical neuro-fuzzy BSP model and study of cases. The new model, denominated type-2 hierarchical neuro-fuzzy BSP model (T2-HNFB) was defined from the study of the desirable characteristics and the limitations of the type-2 and type-1 fuzzy inference systems and the existing hierarchical neuro-fuzzy systems that use type- 1 fuzzy sets. Of this form, the T2-HNFB model is modelling and implemented with the attributes of interpretability and autonomy, from the conception of type-2 fuzzy inference systems, neural networks and recursive BSP partitioning. The developed model is evaluated in different benchmark databases and real applications of forecast and approximation of functions. Comparisons with other models are done. The results obtained show that T2-HNFB model provides, in forecast and approximation of functions, next results and in several cases superior to the best results provided by the models used for comparison. In terms of computational time, its performance also is very good. In forecast and approximation of functions, the intervals of confidence obtained for the defuzzified outputs are always coherent and offer greater credibility in most of cases when compared with intervals of confidence obtained through traditional methods using the forecast outputs by the other models and the own T2-HNFB model.