Fault Detection in Power Distribution Systems Using Automated Integration of Computational Intelligence Tools

The main purpose of this paper is to present architecture of automated system that allows monitoring and tracking in real time (online) the possible occurrence of faults and electromagnetic transients observed in primary power distribution networks. Through the interconnection of this automated system to the utility operation center, it will be possible to provide an efficient tool that will assist in decisionmaking by the Operation Center. In short, the desired purpose aims to have all tools necessary to identify, almost instantaneously, the occurrence of faults and transient disturbances in the primary power distribution system, as well as to determine its respective origin and probable location. The compilations of results from the application of this automated system show that the developed techniques provide accurate results, identifying and locating several occurrences of faults observed in the distribution system.

Swarm Intelligence applied in synthesis of hunting strategies in a three-dimensional environment

Systems of distributed artificial intelligence can be powerful tools in a wide variety of practical applications. Its most surprising characteristic, the emergent behavior, is also the most answerable for the difficulty in. projecting these systems. This work proposes a tool capable to beget individual strategies for the elements of a multi-agent system and thereof providing to the group means on obtaining wanted results, working in a coordinated and cooperative manner as well. As an application example, a problem was taken as a basis where a predators` group must catch a prey in a three-dimensional continuous ambient. A synthesis of system strategies was implemented of which internal mechanism involves the integration between simulators by Particle Swarm Optimization algorithm (PSO), a Swarm Intelligence technique. The system had been tested in several simulation settings and it was capable to synthesize automatically successful hunting strategies, substantiating that the developed tool can provide, as long as it works with well-elaborated patterns, satisfactory solutions for problems of complex nature, of difficult resolution starting from analytical approaches. (c) 2007 Elsevier Ltd. All rights reserved.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Inteligência artificial aplicada à Zootecnia

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.

Benign focal seizures of adolescence and neuropsychological findings in patients from community

RATIONALE: Benign focal seizures of adolescence (BFSA) described by Loiseau et al in 1972, is considered a rare entity, but maybe underdiagnosed. Although mild neuropsychological deficits have been reported in patients with benign epilepsies of childhood, these evaluations have not so far been described in BFSA. The aim of this study is to evaluate neuropsychological functions in BFSA with new onset seizures (<12 months). METHODS: Eight patients with BFSA (according to Loiseau et al, 1972, focal or secondarily tonic clonic generalized seizures between the ages of 10-18 yrs., normal neurologic examination, normal EEG or with mild focal abnormalities) initiated in the last 12 months were studied between July 2008 to May 2009. They were referred from the Pediatric Emergency Section of the Hospital Universitário of the University of Sao Paulo, a secondary care regionalized facility located in a district of middle-low income in Sao Paulo city, Brazil. The study was approved by the Ethics Committee of the Institution. All patients performed neurological, EEG, brain CT and neuropsychological evaluation which consisted of Raven's Special Progressive Matrices - General and Special Scale (according to different ages), Wechsler Children Intelligence Scale-WISC III with ACID Profile, Trail Making Test A/B, Stroop Test, Bender Visuo-Motor Test, Rey Complex Figure, Rey Auditory Verbal Learning Test-RAVLT, Boston Naming Test, Fluency Verbal for phonological and also conceptual patterns - FAS/Animals and Hooper Visual Organization Test. For academic achievement, we used a Brazilian test for named "Teste do Desempenho Escolar", which evaluates abilities to read, write and calculate according to school grade. RESULTS: There were 2 boys and 6 girls, with ages ranging from 10 yrs. 9 m to 14 yrs. 3 m. Most (7/8) of the patients presented one to two seizures and only three of them received antiepileptic drugs (AEDs). Six had mild EEG focal abnormalities and all had normal brain CT. All were literate, attended regular public schools and scored in a median range for IQ, and seven showed discrete higher scores for the verbal subtests. There were low scores for attention in different modalities in six patients, mainly in alternated attention as well as inhibitory subtests (Stroop test and Trail Making Test part B). Four of the latter cases who showed impairment both in alternated and inhibitory attention were not taking AEDs. Visual memory was impaired in five patients (Rey Complex Figure). Executive functions analysis showed deficits in working memory in five, mostly observed in Digits Indirect Order and Arithmetic tests (WISC III). Reading and writing skills were below the expected average for school grade in six patients according to the achievement scholar performance test utilized. One patient of this series who had the best scores in all tests was taking phenobarbital. CONCLUSIONS: Neuropsychological imbalance between normal IQ and mild dysfunctions such as in attention domain and in some executive abilities like working memory and planning, as well as difficulties in visual memory and in reading and writing, were described in this group of patients with BFSA from community. This may reflect mild higher level neurological dysfunctions in adolescence idiopathic focal seizures probably caused by an underlying dysmaturative epileptogenic process. Although academic problems often have multiple causes, a specific educational approach may be necessary in these adolescents, in order to improve their scholastic achievements, helping in this way, to decrease the stigma associated to epileptic seizures in the community.

Reaction time assessment in children with ADHD

Attention deficit, impulsivity and hyperactivity are the cardinal features of attention deficit hyperactivity disorder (ADHD) but executive function (EF) disorders, as problems with inhibitory control, working memory and reaction time, besides others EFs, may underlie many of the disturbs associated with the disorder. OBJECTIVE: To examine the reaction time in a computerized test in children with ADHD and normal controls. METHOD: Twenty-three boys (aged 9 to 12) with ADHD diagnosis according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, 2000 (DSM-IV) criteria clinical, without comorbidities, Intelligence Quotient (IQ) >89, never treated with stimulant and fifteen normal controls, age matched were investigated during performance on a voluntary attention psychophysical test. RESULTS: Children with ADHD showed reaction time higher than normal controls. CONCLUSION: A slower reaction time occurred in our patients with ADHD. This findings may be related to problems with the attentional system, that could not maintain an adequate capacity of perceptual input processes and/or in motor output processes, to respond consistently during continuous or repetitive activity.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

As relações entre afetividade e inteligência no desenvolvimento psicológico

Este texto apresenta considerações sobre as relações entre afetividade e inteligência no desenvolvimento psicológico, a partir de quatro modelos teóricos: as perspectivas psicogenéticas de Piaget, Wallon, Vygotsky e concepções extraídas da teoria psicanalítica de Freud. O objetivo é apontar as ênfases de cada abordagem para os aspectos afetivos e cognitivos e seu papel no desenvolvimento psicológico. Como conclusão, pode-se dizer que os modelos, interessados pela gênese da construção do conhecimento ou pela constituição do psiquismo, apresentam diferentes tipos de relação entre afetividade e inteligência. Uns propõem relações de alternância (Wallon); complementaridade de um em relação ao outro (Vygotsky) ou correspondência (Piaget) entre afetividade e inteligência, enquanto outro enfatiza aspectos pulsionais que interferem no funcionamento psicológico afetivo e cognitivo (Freud).

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Striatal volume abnormalities in treatment-naive patients diagnosed with pediatric major depressive disorder

Objective: The striatum, including the putamen and caudate, plays an important role in executive and emotional processing and may be involved in the pathophysiology of mood disorders. Few studies have examined structural abnormalities of the striatum in pediatric major depressive disorder (MDD) patients. We report striatal volume abnormalities in medication-naive pediatric MDD compared to healthy comparison subjects. Method: Twenty seven medication-naive pediatric Diagnostic and Statistical Manual of Mental Disorders, 4(th) edition (DSM-IV) MDD and 26 healthy comparison subjects underwent volumetric magnetic resonance imaging (MRI). The putamen and caudate volumes were traced manually by a blinded rater, and the patient and control groups were compared using analysis of covariance adjusting for age, sex, intelligence quotient, and total brain volumes. Results: MDD patients had significantly smaller right striatum (6.0% smaller) and right caudate volumes (7.4% smaller) compared to the healthy subjects. Left caudate volumes were inversely correlated with severity of depression in MDD subjects. Age was inversely correlated with left and right putamen volumes in MDD patients but not in the healthy subjects. Conclusions: These findings provide fresh evidence for abnormalities in the striatum of medication-naive pediatric MDD patients and suggest the possible involvement of the striatum in the pathophysiology of MDD.

O Itamaraty nos anos de chumbo: o Centro de Informações do Exterior (CIEX) e a repressão no Cone Sul (1966-1979)

Neste artigo, examina-se o funcionamento do Centro de Informações do Exterior (CIEX), órgão do Itamaraty e vinculado ao Serviço Nacional de Informações (SNI) que foi encarregado de espionar políticos e militantes contrários ao regime militar brasileiro que se exilaram nos países vizinhos. Trata-se de um estudo que visa a desvendar como agia um dos elos do sistema repressivo montado pela ditadura brasileira, que tinha um relativo grau de interação com as outras ditaduras militares da região do Cone Sul. O artigo demonstra que o Itamaraty colaborou intensamente com o regime militar brasileiro, inclusive com a repressão.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

SAB(IO): A BIOLOGICALLY PLAUSIBLE CONNECTIONIST APPROACH TO AUTOMATIC TEXT SUMMARIZATION

An implementation of a computational tool to generate new summaries from new source texts is presented, by means of the connectionist approach (artificial neural networks). Among other contributions that this work intends to bring to natural language processing research, the use of a more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expectation that it may bring an increase in computational efficiency when compared to the sa-called biologically implausible algorithms.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.