59 resultados para multivariate hidden Markov model
em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"
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Service provisioning is a challenging research area for the design and implementation of autonomic service-oriented software systems. It includes automated QoS management for such systems and their applications. Monitoring, Diagnosis and Repair are three key features of QoS management. This work presents a self-healing Web service-based framework that manages QoS degradation at runtime. Our approach is based on proxies. Proxies act on meta-level communications and extend the HTTP envelope of the exchanged messages with QoS-related parameter values. QoS Data are filtered over time and analysed using statistical functions and the Hidden Markov Model. Detected QoS degradations are handled with proxies. We experienced our framework using an orchestrated electronic shop application (FoodShop).
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Ciência da Computação - IBILCE
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The linear quadratic Gaussian control of discrete-time Markov jump linear systems is addressed in this paper, first for state feedback, and also for dynamic output feedback using state estimation. in the model studied, the problem horizon is defined by a stopping time τ which represents either, the occurrence of a fix number N of failures or repairs (T N), or the occurrence of a crucial failure event (τ δ), after which the system paralyzed. From the constructive method used here a separation principle holds, and the solutions are given in terms of a Kalman filter and a state feedback sequence of controls. The control gains are obtained by recursions from a set of algebraic Riccati equations for the former case or by a coupled set of algebraic Riccati equation for the latter case. Copyright © 2005 IFAC.
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This paper addresses the H ∞ state-feedback control design problem of discretetime Markov jump linear systems. First, under the assumption that the Markov parameter is measured, the main contribution is on the LMI characterization of all linear feedback controllers such that the closed loop output remains bounded by a given norm level. This results allows the robust controller design to deal with convex bounded parameter uncertainty, probability uncertainty and cluster availability of the Markov mode. For partly unknown transition probabilities, the proposed design problem is proved to be less conservative than one available in the current literature. An example is solved for illustration and comparisons. © 2011 IFAC.
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Traditionally, ancillary services are supplied by large conventional generators. However, with the huge penetration of distributed generators (DGs) as a result of the growing interest in satisfying energy requirements, and considering the benefits that they can bring along to the electrical system and to the environment, it appears reasonable to assume that ancillary services could also be provided by DGs in an economical and efficient way. In this paper, a settlement procedure for a reactive power market for DGs in distribution systems is proposed. Attention is directed to wind turbines connected to the network through synchronous generators with permanent magnets and doubly-fed induction generators. The generation uncertainty of this kind of DG is reduced by running a multi-objective optimization algorithm in multiple probabilistic scenarios through the Monte Carlo method and by representing the active power generated by the DGs through Markov models. The objectives to be minimized are the payments of the distribution system operator to the DGs for reactive power, the curtailment of transactions committed in an active power market previously settled, the losses in the lines of the network, and a voltage profile index. The proposed methodology was tested using a modified IEEE 37-bus distribution test system. © 1969-2012 IEEE.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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In many movies of scientific fiction, machines were capable of speaking with humans. However mankind is still far away of getting those types of machines, like the famous character C3PO of Star Wars. During the last six decades the automatic speech recognition systems have been the target of many studies. Throughout these years many technics were developed to be used in applications of both software and hardware. There are many types of automatic speech recognition system, among which the one used in this work were the isolated word and independent of the speaker system, using Hidden Markov Models as the recognition system. The goals of this work is to project and synthesize the first two steps of the speech recognition system, the steps are: the speech signal acquisition and the pre-processing of the signal. Both steps were developed in a reprogrammable component named FPGA, using the VHDL hardware description language, owing to the high performance of this component and the flexibility of the language. In this work it is presented all the theory of digital signal processing, as Fast Fourier Transforms and digital filters and also all the theory of speech recognition using Hidden Markov Models and LPC processor. It is also presented all the results obtained for each one of the blocks synthesized e verified in hardware
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Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.
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Sao Paulo State Research Foundation-FAPESP
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Infection with human papilloma virus (HPV) is the most common sexually transmitted disease in the world. Among the 630 million new cases of HPV that occur each year, 30 million develop anogenital warts. Although subclinical infection with HPV is the most common cause, genital warts are also associated with immunosuppression caused by HIV. In view of the high prevalence of HPV/HIV co-infection particularly among men who have sex with men, the objectives of this study were to determine the prevalence of anogenital warts in men with HIV/AIDS and to identify associated factors. A cross-sectional study was conducted on 159 men with HIV/AIDS consecutively selected at a referral service in Botucatu, São Paulo, Brazil, in which the association between sociodemographic, behavioral and clinical variables and the presence of anogenital warts was evaluated. After hierarchical analysis of the data, variables presenting a p value ≤ 0.2 were entered into an unconditional multivariate logistic regression model. Forty-nine (31%) of the HIV-positive patients had anogenital warts. The mean age was 44.6 ± 9.6 years. The main factors associated with the presence of anogenital warts were irregular antiretroviral treatment and genital herpes(HSV). The present study demonstrate that anogenital warts occur in almost one-third of the male population infected with HIV and factors associated with a higher risk of being diagnosed with anogenital warts were irregular cART use and co-infection with HSV, other variables could not be associated.
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Background: The role of serum metalloproteinases (MMP) after myocardial infarction (MI) is unknown. Objective: The aim of this study was to evaluate the role of serum MMP-2 and -9 as predictors of ventricular remodeling six months after anterior MI. Methods: We prospectively enrolled patients after their first anterior MI. MMP activity was assayed 12 to 72 hours after the MI. An echocardiogram was performed during the hospitalization and six months later. Results: We included 29 patients; 62% exhibited ventricular remodeling. The patients who exhibited remodeling had higher infarct size based on creatine phosphokinase (CPK) peak values (p = 0.037), higher prevalence of in-hospital congestive heart failure (p = 0.004), and decreased ejection fraction (EF) (p = 0.007). The patients with ventricular remodeling had significantly lower serum levels of inactive MMP-9 (p = 0.007) and significantly higher levels of the active form of MMP-2 (p = 0.011). In a multivariate logistic regression model, adjusted by age, CPK peak, EF and prevalence of heart failure, MMP-2 and -9 serum levels remained associated with remodeling (p = 0.033 and 0.044, respectively). Conclusion: Higher serum levels of inactive MMP-9 were associated with the preservation of left ventricular volumes, and higher serum levels of the active form of MMP-2 were a predictor of remodeling 6 months after MI. (Arq Bras Cardiol. 2013;100(4):315-321).