15 resultados para Identification problem

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The study of algorithms for active vibration control in flexible structures became an area of enormous interest for some researchers due to the innumerable requirements for better performance in mechanical systems, as for instance, aircrafts and aerospace structures. Intelligent systems, constituted for a base structure with sensors and actuators connected, are capable to guarantee the demanded conditions, through the application of diverse types of controllers. For the project of active controllers it is necessary, in general, to know a mathematical model that enable the representation in the space of states, preferential in modal coordinates to permit the truncation of the system and reduction in the order of the controllers. For practical applications of engineering, some mathematical models based in discrete-time systems cannot represent the physical problem, therefore, techniques of identification of system parameters must be used. The techniques of identification of parameters determine the unknown values through the manipulation of the input (disturbance) and output (response) signals of the system. Recently, some methods have been proposed to solve identification problems although, none of them can be considered as being universally appropriate to all the situations. This paper is addressed to an application of linear quadratic regulator controller in a structure where the damping, stiffness and mass matrices were identified through Chebyshev's polynomial functions.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Speech and language disorders are some of the most common referral reasons to child development centers accounting for approximately 40% of cases. Stuttering is a disorder in which involuntary repetition, prolongation, or cessation of the sound precludes the flow of speech. About 5% of individuals in the general population have a stuttering problem, and about 80% of the affected children recover naturally. The causal factors of stuttering remain uncertain in most cases; studies suggest that genetic factors are responsible for 70% of the variance in liability for stuttering, whereas the remaining 30% is due to environmental effects supporting a complex cause of the disorder. The use of high-resolution genome wide array comparative genomic hybridization has proven to be a powerful strategy to narrow down candidate regions for complex disorders. We report on a case with a complex set of speech and language difficulties including stuttering who presented with a 10Mb deletion of chromosome region 7q33-35 causing the deletion of several genes and the disruption of CNTNAP2 by deleting the first three exons of the gene. CNTNAP2 is known to be involved in the cause of language and speech disorders and autism spectrum disorder and is in the same pathway as FOXP2, another important language gene, which makes it a candidate gene for causal studies speech and language disorders such as stuttering. (C) 2010 Wiley-Liss, Inc.

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As hemoglobinopatias são um grupo de afecções genéticas que representam problema de saúde pública em muitos países em que sua incidência é alta, com significativa morbidade. Objetivamos identificar defeitos moleculares que pudessem explicar o perfil laboratorial obtido por eletroforese e HPLC com Hb F elevada, em um grupo de indivíduos adultos sem sinais ou sintomas de anemia. Encontramos cinco diferentes mutações que originam beta talassemia por PCR-ASO: três casos com CD 6 (-A), um CD 39, um IVS 1-5, um -87 todas de origem mediterrânea, e um IVS II-654 de origem asiática. As mutações CD 6 (-A), -87 e IVS II-654 foram descritas pela primeira vez na população brasileira.

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Colletotrichum spp. cause anthracnose in various fruits post-harvest and are a particularly important problem in tropical and subtropical fruits. The disease in fruits of avocado, guava, papaya, mango and passion fruit has been reported to be caused by C. gloeosporioides, and in banana by C. musae. In subtropical and temperate crops such apple, grape, peach and kiwi, the disease is caused by C. acutatum. The variation in pathogenic, morphological, cultural and molecular characteristics of Brazilian isolates of Colletotrichum acutatum Simmonds and isolates from post-harvest decays of avocado, banana, guava, papaya, mango and passion fruit was evaluated. The fruits were inoculated with mycelium of C. acutatum, Colletotrichum spp. and C. musae on a disc of potato dextrose agar. The morphological, cultural and molecular characteristics studied were conidia morphology, colony growth at different temperatures, colony coloration and PCR with primers CaInt2 and ITS4 for C. acutatum and CgInt and ITS4 for C. gloeosporioides. C. acutatum was pathogenic to avocado, guava, papaya, mango and passion fruit, but it was not pathogenic to banana. The morphological, cultural and molecular studies indicated that the avocado, papaya, mango and passion fruit isolates were C. gloeosporioides. The natural guava isolate was identified as C. acutatum, which had not been found previously to produce anthracnose symptoms on guava in Brazil.

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The on-line separation and identification of two important taxonomic markers for plant species of the Paepalanthus genus, the flavonoids 6-methoxykaempferol-3-O-β-D-glucopyranoside and 6-methoxykaempferol-3-O-β-D-6″(p-coumaroyl)glucopyranoside, has been performed with an HPLC-NMR coupling using C30 phase. 1D spectra have been recorded in the stopped-flow mode for the two predominant chromatographic peaks. This is the first application of HPLC-NMR coupling using C30 phase to a taxonomic problem. The technique drastically reduces the required amount of sampling for structure determination. © Springer-Verlag 2000.

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The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

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The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

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Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

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Oxacillin-resistant Staphylococcus aureus represents a serious problem in hospitals worldwide, increasing infected patients' mortality and morbidity and raising treatment costs and internment time. In this study, the results of using the Multiplex PCR technique to amplify fragments of the genes femA (specific-species), mecA (oxacillin resistance) and ileS-2 (mupirocin resistance) were compared with those of tests conventionally used to identify S. aureus isolates and ascertain their resistance to drugs. Fifty S. aureus strains were isolated from patients receiving treatment at UNOESTE University Hospital in Presidente Prudente, SP, Brazil. The 686 bp fragment corresponding to the gene femA was amplified and detected in all the isolates. On the other hand, the 310 bp fragment corresponding to the mecA gene was amplified in 29 (58%) of the isolates. All of the isolates showed sensitivity to mupirocin in the agar diffusion test, which was corroborated by the lack of any amplicon of the 456 bp fragment corresponding to the ileS-2 gene, in the PCR bands. The conventional tests to identify S. aureus and detect resistance to oxacillin and mupirocin showed 100% agreement with the PCR Multiplex results. The use of techniques for rapid and accurate identification of bacteria and assessment of their resistance may be valuable in the control of infection by resistant strains, allowing the rapid isolation and treatment of an infected patient. However, the results demonstrate that traditional phenotypic tests are also reliable, though they take more time.

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Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

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Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

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This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

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Introduction The incidence of opportunistic fungal infections has increased in recent years and is considered an important public health problem. Among systemic and opportunistic mycoses, cryptococcosis is distinguished by its clinical importance due to the increased risk of infection in individuals infected by human immunodeficiency virus. Methods To determine the occurrence of pathogenic Cryptococcus in pigeon excrement in the City of Araraquara, samples were collected from nine environments, including state and municipal schools, abandoned buildings, parks, and a hospital. The isolates were identified using classical tests, and susceptibility testing for the antifungal drugs (fluconazole, itraconazole, voriconazole, and amphotericin B) independently was also performed. After collection, the excrement samples were plated on Niger agar and incubated at room temperature. Results A total of 87 bird dropping samples were collected, and 66.6% were positive for the genus Cryptococcus. The following species were identified: Cryptococcus neoformans (17.2%), Cryptococcus gattii (5.2%), Cryptococcus ater (3.5%), Cryptococcus laurentti (1.7%), and Cryptococcus luteolus (1.7%). A total of 70.7% of the isolates were not identified to the species level and are referred to as Cryptococcus spp. throughout the manuscript. Conclusions Although none of the isolates demonstrated resistance to antifungal drugs, the identification of infested areas, the proper control of birds, and the disinfection of these environments are essential for the epidemiological control of cryptococcosis.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)