Sistema de Controle Preditivo Multimodelos Fuzzy TS-BFO embarcado em um Controlador Lógico Programável.

Este trabalho aborda o problema da identificação e controle de sistemas industriais não-lineares através de um algoritmo de controle preditivo que utiliza multimodelos lineares. Algoritmos de controle preditivo baseados em modelos (MBPC - Model Based Predictive Controller) utilizam o modelo do processo para a determinação do conjunto de previsões de saída e desta forma determinar qual a ação de controle ótima a ser adotada. Neste contexto, a proposta deste trabalho é implementar um sistema de controle preditivo em um controlador lógico programável (CLP), utilizando para a representação dos sistemas não-lineares modelos fuzzy Takagi-Sugeno (TS) com base de funções ortonormais nos consequentes das regras. As bases de funções ortonormais apresentam características estruturais interessantes para representação de sistemas dinâmicos, com destaque para a ausência de realimentação de saída, característica de suma importância em algoritmos de controle preditivo. Dentre as bases de funções ortonormais utilizadas na modelagem de sistemas dinâmicos, destacam-se as bases de funções ortonormais generalizadas (GOBF) com funções internas em estrutura Ladder. Com a utilização de tais funções o sistema dinâmico sob análise é parametrizado utilizando somente valores reais, independente da natureza de seus polos. Os modelos fuzzy TS-GOBF neste trabalho são obtidos através de amostras da entrada e saída do sistema. Os antecedentes das regras fuzzy são determinados através da técnica de agrupamento fuzzy (fuzzy clustering), sendo o número ideal de grupos obtido através de critérios de avaliação de agrupamento fuzzy. Os parâmetros dos consequentes das regras, formados por GOBFs, são inicialmente obtidos utilizando-se o método dos mínimos quadrados locais. Determinados os modelos fuzzy TS-GOBF inicial, são utilizadas técnicas de simplificação da base de regras fuzzy e um algoritmo para a otimização dos parâmetros do modelo TS-GOBF, como as funções de pertinência nos antecedentes das regras e os parâmetros nos consequentes. Obtido o modelo fuzzy TS-GOBF otimizado, os controladores preditivos lineares que atuarão nos modelos locais são embarcados no CLP, juntamente com a base de regras fuzzy e com os parâmetros das GOBFs. A ação de controle global é obtida através da combinação ponderada das ações dos controladores locais. A cada ciclo do CLP a ação de controle global é atualizada e aplicada no processo sob controle. A abordagem proposta neste trabalho apresenta vantagens com relação a outras metodologias de controle não-linear utilizadas na indústria, uma vez que o sistema de controle em questão pode ser implementado em CLPs comerciais de baixo custo utilizando a linguagem Texto Estruturado. Para ilustrar a proposta dessa dissertação, são apresentados, no final deste trabalho, exemplos de modelagem e controle de processos reais

Nonlinear sytems modeling based on ladder-strutured generalized orthonormal basis functions

Orientadores: Wagner Caradori do Amaral, Ricardo Jose Grabrielli Barreto CampelloTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho enfoca a modelagem e identificação de sistemas dinâmicos não-lineares estáveis através de modelos fuzzy Takagi-Sugeno (TS) e/ou Volterra, ambos com estruturas formadas por bases de funções ortonormais (BFO), principalmente as bases de funções ortonormais generalizadas (GOBF - Generalized Orthonormal Basis Functions) com funções internas. As GOBF¿s com funções internas modelam sistemas dinâmicos com múltiplos modos através de uma parametrização que utiliza somente valores reais, sejam os polos do sistema reais e/ou complexos. Uma das principais contribuições desta tese concentra-se na proposta da otimização e ajuste fino dos parâmetros destes modelos não-lineares. Realiza-se a identificação dos modelos fuzzy TS-BFO utilizando-se de medidas dos sinais de entrada e saída do sistema a ser modelado. Os modelos fuzzy TS-BFO são inicialmente determinados utilizando-se uma técnica de agrupamento fuzzy (fuzzy clustering) e simplificados por algoritmos que eliminam eventuais redundâncias. Em sequência desenvolve-se o cálculo analítico dos gradientes da saída do modelo TS-BFO em relação aos parâmetros do modelo (polos da BFO, coeficientes da expansão da BFO e parâmetros das funções de pertinência). Utilizando-se técnicas de otimização não-linear e o valor dos gradientes, realiza-se a sintonia fina dos parâmetros dos modelos inicialmente obtidos. Para os modelos de Volterra-GOBF desenvolve-se uma nova abordagem utilizando-se GOBF com funções internas nos kernels dos modelos. São calculados os gradientes analíticos da saída do modelo de Volterra-GOBF, seja com kernels simétricos ou não simétricos, com relação aos parâmetros a serem determinados. Estes valores são utilizados em algoritmos de otimização que possibilitam a obtenção de modelos mais precisos do sistema sem nenhum conhecimento a priori de suas características. Além da identificação de sistemas não-lineares por modelos BFO, abordou-se também, nesta tese, uma nova metodologia para a otimização de modelos lineares BFO no domínio da frequência. Neste contexto, destaca-se como principal contribuição o desenvolvimento, no domínio da frequência, do cálculo analítico dos gradientes da resposta em frequência das funções de Kautz e Laguerre, com relação aos seus parâmetros de projeto. Os valores dos gradientes fornecem a direção de busca dos parâmetros dos modelos em processos de otimização não-linear. Também foram otimizados os modelos GOBF com funções internas, com o cálculo numérico dos seus gradientes, pois, ainda não foi possível estabelecer uma fórmula genérica para o cálculo analítico dos gradientes dos modelos GOBF, de qualquer ordem, em relação aos parâmetros a serem determinados. Exemplos ilustram a aplicação e eficiência dos métodos de identificação e otimização propostos na modelagem de sistemas lineares (domínio do tempo e da frequência) e não-lineares utilizando BFO¿s.Abstract: This work is concerned with the modeling and identification of stable nonlinear dynamic systems using Takagi-Sugeno fuzzy and Volterra models within the framework of orthonormal basis functions (OBF), mainly ladder-structured generalized orthonormal basis functions (GOBF). The ladderstructured GOBFs allows to model dynamic systems with multiple modes, real and/or complex poles, through a parameterization, which uses only real values. The main contribution of this thesis is the optimization and fine tuning of the parameters of OBF nonlinear models. The GOBF models identification are performed using only input and output measurements. The initial GOBF-TS fuzzy model is obtained using a fuzzy clustering technique and simplified by algorithms that eliminate any redundancies. Next, the analytical calculation of the gradients of GOBF-TS model concerning model parameters (GOBF poles, OBF expansion coefficients and the parameters of membership functions) is developed. A fine tuning of the model parameters is obtained by using a nonlinear optimization technique and the calculated gradients. For Volterra-GOBF models a new approach using kernels with ladder-structured GOBF is also proposed. Furthermore, Volterra-GOBF model optimization, with symmetrical or asymmetrical kernels, using an analytical gradients calculation of the output model regarding their parameters is presented. Following, a new approach for linear OBF models optimization, in frequency domain, is also addressed. In this context, the analytical calculation of the gradients of the Laguerre and Kautz frequency response concerning its parameters is presented The ladder-structured GOBF models optimization, in the frequency domain, is performed using only numerical calculation of its gradients, as it has not yet been possible to derive a generic analytical gradients. Examples illustrate the performance and effectiveness of identification methods proposed here in the modeling and optimization of linear (time domain and frequency) and non-linear systems.DoutoradoAutomaçãoDoutor em Engenharia Elétric

Program Similarity Analysis for Malware Classification and its Pitfalls

Malware classification, specifically the task of grouping malware samples into families according to their behaviour, is vital in order to understand the threat they pose and how to protect against them. Recognizing whether one program shares behaviors with another is a task that requires semantic reasoning, meaning that it needs to consider what a program actually does. This is a famously uncomputable problem, due to Rice\u2019s theorem. As there is no one-size-fits-all solution, determining program similarity in the context of malware classification requires different tools and methods depending on what is available to the malware defender. When the malware source code is readily available (or at least, easy to retrieve), most approaches employ semantic \u201cabstractions\u201d, which are computable approximations of the semantics of the program. We consider this the first scenario for this thesis: malware classification using semantic abstractions extracted from the source code in an open system. Structural features, such as the control flow graphs of programs, can be used to classify malware reasonably well. To demonstrate this, we build a tool for malware analysis, R.E.H.A. which targets the Android system and leverages its openness to extract a structural feature from the source code of malware samples. This tool is first successfully evaluated against a state of the art malware dataset and then on a newly collected dataset. We show that R.E.H.A. is able to classify the new samples into their respective families, often outperforming commercial antivirus software. However, abstractions have limitations by virtue of being approximations. We show that by increasing the granularity of the abstractions used to produce more fine-grained features, we can improve the accuracy of the results as in our second tool, StranDroid, which generates fewer false positives on the same datasets. The source code of malware samples is not often available or easily retrievable. For this reason, we introduce a second scenario in which the classification must be carried out with only the compiled binaries of malware samples on hand. Program similarity in this context cannot be done using semantic abstractions as before, since it is difficult to create meaningful abstractions from zeros and ones. Instead, by treating the compiled programs as raw data, we transform them into images and build upon common image classification algorithms using machine learning. This led us to develop novel deep learning models, a convolutional neural network and a long short-term memory, to classify the samples into their respective families. To overcome the usual obstacle of deep learning of lacking sufficiently large and balanced datasets, we utilize obfuscations as a data augmentation tool to generate semantically equivalent variants of existing samples and expand the dataset as needed. Finally, to lower the computational cost of the training process, we use transfer learning and show that a model trained on one dataset can be used to successfully classify samples in different malware datasets. The third scenario explored in this thesis assumes that even the binary itself cannot be accessed for analysis, but it can be executed, and the execution traces can then be used to extract semantic properties. However, dynamic analysis lacks the formal tools and frameworks that exist in static analysis to allow proving the effectiveness of obfuscations. For this reason, the focus shifts to building a novel formal framework that is able to assess the potency of obfuscations against dynamic analysis. We validate the new framework by using it to encode known analyses and obfuscations, and show how these obfuscations actually hinder the dynamic analysis process

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Design of OBF-TS fuzzy models based on multiple clustering validity criteria

Takagi-Sugeno Fuzzy Models within the framework of Orthonormal Basis Functions (OBF-TS Fuzzy Models) have shown to be an effective approach to nonlinear system identification and control due to several advantages they exhibit over those dynamic model topologies most commonly adopted in the literature. Despite all the theoretical advances and encouraging application results obtained so far, the automatic determination of the number of local OBF models remains an issue. This paper elaborates on the use of a mixture of clustering validity criteria to automatically determine the number of local models based on product space fuzzy clustering of I/O data

Design Of Obf-ts Fuzzy Models Based On Multiple Clustering Validity Criteria

Takagi-Sugeno Fuzzy Models within the framework of Orthonormal Basis Functions (OBF-TS Fuzzy Models) have shown to be an effective approach to nonlinear system identification and control due to several advantages they exhibit over those dynamic model topologies most commonly adopted in the literature. Despite all the theoretical advances and encouraging application results obtained so far, the automatic determination of the number of local OBF models remains an issue. This paper elaborates on the use of a mixture of clustering validity criteria to automatically determine the number of local models based on product space fuzzy clustering of I/O data. © 2007 IEEE.2336339Babuška, R., (1998) Fuzzy Modeling for Control, , KluwerBezdek, J.C., Pal, N.R., Some new indexes of cluster validity (1998) IEEE Trans. on Systems, Man and Cybernetics B, 28 (3), pp. 301-315Campello, R.J.G.B., Hruschka, E.R., A fuzzy extension of the silhouette width criterion for cluster analysis (2006) Fuzzy Sets and Systems, 157, pp. 2858-2875R. J. G. B. Campello, L. A. C. Meleiro, W. C. Amaral, and R. M. Filho. Identification of a bioprocess using Laguerre function based models. In World Congress of Chemical Engineering, page CD, Melbourne, Australia, 2001R. J. G. B. Campello, L. A. C. Meleiro, and W. C. Amaral. Takagi-sugeno fuzzy models within orthonormal basis function framework and their application to process control. In IEEE Int. Conf. Fuzzy Systems, pages 1399-1404, Honolulu, USA, 2002R. J. G. B. Campello, L. A. C. Meleiro, and W. C. Amaral. Control of a bioprocess using orthonormal basis function fuzzy models. In IEEE Int. Conf. Fuzzy Systems, pages 801-806, Budapest, Hungary, 2004Campello, R.J.G.B., Von Zuben, F.J., Amaral, W.C., Meleiro, L.A.C., Maciel Filho, R., Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control (2003) Chemical Engineering Science, 58, pp. 4259-4270Heuberger, P.S.C., Van den Hof, P.M.J., Wahlberg, B., (2005) Modelling and Identification with Rational Orthogonal Basis Functions, , SpringerHöppner, F., Klawonn, F., Kruse, R., Runkler, T., (1999) Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, , John Wiley & SonsMedeiros, A.V., Amaral, W.C., Campello, R.J.G.B., GA optimization of generalized OBF TS fuzzy models with global and local estimation approaches (2006) Proc. IEEE Int. Conf. Fuzzy Systems, pp. 8494-8501. , Vancouver, CanadaMedeiros, A.V., Amaral, W.C., Campello, R.J.G.B., GA optimization of OBF TS fuzzy models with linear and non linear local models (2006) Proc. Brazilian Symposium on Neural Networks, , Ribeirão Preto, BrazilMedeiros, A.V., (2006) Modeling of Nonlinear Dynamic Systems using Fuzzy Systems, Genetic Algorithms and Orthonormal Basis Functions, , Master's Degree Thesis, School of Electrical and Computer Engineering FEEC, UNICAMP, Brazil, Jan, In PortugueseNelles, O., (2001) Nonlinear System Identification, , Springer-VerlagG. H. C. Oliveira, R. J. G. B. Campello, and W. C. Amaral. Fuzzy models within orthonormal basis function framework. In IEEE Int. Conf. Fuzzy Systems, pages 957-962, Seoul, Korea, 1999Johansen, R.S.T.A., Murray-Smith, R., On the interpretation and identification of dynamic takagi-sugeno fuzzy models (2000) IEEE Transactions on Fuzzy Systems, 8 (3), pp. 297-31

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201