Student Number : 0006036T - MSc(Eng) project report - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Demographic and medical history information obtained from annual South African antenatal surveys is used to estimate the risk of acquiring HIV. The estimation system consists of a classifier: a neural network trained to perform binary classification, using supervised learning with the survey data. The survey information contains discrete variables such as age, gravidity and parity, as well as the quantitative variables race and location, making up the input to the neural network. HIV status is the output. A multilayer perceptron with a logistic function is trained with a cross entropy error function, providing a probabilistic interpretation of the output. Predictive and classification performance is measured, and the sensitivity and specificity are illustrated on the Receiver Operating Characteristic. An auto-associative neural network is trained on complete datasets, and when presented with partial data, global optimisation methods are used to approximate the missing entries. The effect of the imputed data on the network prediction is investigated.

No description available.

We start by showing that in an active learning setting, the Perceptron algorithm needs $\Omega(\frac{1}{\epsilon^2})$ labels to learn linear separators within generalization error $\epsilon$. We then present a simple selective sampling algorithm for this problem, which combines a modification of the perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization error $\epsilon$ after asking for just $\tilde{O}(d \log \frac{1}{\epsilon})$ labels. This exponential improvement over the usual sample complexity of supervised learning has previously been demonstrated only for the computationally more complex query-by-committee algorithm.

Panama has a warm, wet, tropical climate. Unlike countries that are farther from the equator, Panama does not experience seasons marked by changes in temperature. Instead, Panama's seasons are divided into Wet and Dry. The Dry Season generally begins around mid-December, but this may vary by as much 3 to 4 weeks. Around this time, strong northeasterly winds known as "trade winds" begin to blow and little or no rain may fall for many weeks in a row. Daytime air temperatures increase slightly to around 30-31 Celsius (86-88 Fahrenheit), but nighttime temperatures remain around 22-23 Celsius (72-73 Fahrenheit). Relative humidity drops throughout the season, reaching average values as low as 70 percent. The Wet Season usually begins around May 1, but again this may vary by 1 or 2 weeks. May is often one of the wettest months, especially in the Panama Canal area, so the transition from the very dry conditions at the end of the Dry Season to the beginning of Wet Season can be very dramatic. With the arrival of the rain, temperatures cool down a little during the day and the trade winds disappear. Relative humidity rises quickly and may hover around 90 to 100% throughout the Wet Season. Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. The presented thesis compares forecast of drought indices based on seven different models of artificial neural networks model. The analyzed drought indices are SPI and SPEI-ANN Drought forecast, and was derived for the period of 1985-2014 on Panama Canal basin; I've selected seven of sixty-one Hydro-meteorological networks, existing in the Panama Canal basin. The rainfall is 1784 mm per year. The meteorological data were obtained from the PANAMA CANAL AUTHORITY, Section of Water Resources, and Panama Canal Authority, Panama. The performance of all the models was compared using ME, MAE, RMSE, NS, and PI. The results of drought indices forecast, explained by the values of seven model performance indices, show, that in Panama Canal has problem with the drought. Even though The Panama is generally seen as a wet country, droughts can cause severe problems. Significant drought conditions are observed in the index based on precipitation and potential evaporation found in this thesis; The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), were used to quantify drought in the Panama Canal basin, Panama Canal, at multiple time scales within the period 1985-2014. The results indicate that drought indices based on different variables show the same major drought events. Drought indices based on precipitation and potential evaporation are more variable in time while drought indices based on discharge. Spatial distribution of meteorological drought is uniform over Panama Canal.

Orientadores: Edson Bim, Fernando José Von Zuben / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T01:48:41Z (GMT). No. of bitstreams: 1 Marchi_RodrigoAndreolide_M.pdf: 5160130 bytes, checksum: 8e043b48f5cd0e86cc078b3adc27f74b (MD5) Previous issue date: 2011 / Resumo: Neste trabalho é apresentada a estratégia de Controle Direto de Potência para o Gerador de Indução Duplamente Alimentado utilizando um controlador Perceptron de Múltiplas Camadas. O controlador tem a função de gerar os sinais das componentes de eixo direto e quadratura da tensão do rotor, sem a necessidade de controladores de corrente. A estratégia de controle apresentada permite operar o conversor de potência, conectado aos terminais do rotor, com frequência de chaveamento constante. A rede neural foi treinada off-line, a partir de um algoritmo de otimização de segunda ordem baseado no gradiente conjugado estendido, utilizando um conjunto de amostras obtido por meio da simulação digital de uma máquina de rotor bobinado de potência igual a 2 MW. Resultados de simulação digital com os dados dessa máquina, operando no modo gerador e com dupla alimentação, são apresentados para vários valores de potência ativa e reativa, e para velocidades fixas e variáveis, compreendidas na faixa de -15% a +15% da velocidade síncrona. Com o controlador implementado por uma rede neural artificial e treinada para uma máquina de 2 MW, testes de simulação digital e experimentais para uma máquina de 2,2 kW, operando na velocidade subsíncrona, são apresentados para validar a proposta / Abstract: This work presents a direct power control strategy for the doubly fed induction generator using a controller artificial neural networks, more specifically a multilayer perceptron. The controller has the role of generating the direct and quadrature-axis component signals of the rotor voltage, without the need of current controllers. The proposed control strategy allows to operate the converter, connected to the rotor terminals, with a fixed switching frequency. The multilayer perceptron was subject to an off-line training procedure using a second order algorithm based on an extend version of the conjugate gradient algorithm, using a set of samples produced by a 2 MW machine's digital simulation. Results of digital simulation for this machine are presented for several values of active and reactive power, with the generator operating on fixed and variable speed, in the range of -15% and +15% of the synchronous speed, considering the parameters of 2 MW machine. With the artificial neural network controller designed for this machine, digital simulation tests and experimental tests for a 2,2 kW machine, operating in a sub-synchronous speed, arc presented to validate the proposal / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

With the development of big data computing, the speech recognition has been popular for serving human’s life. However, when place the speech recognition system into noisy environments, the background noises greatly degrades the speech recognition system accuracy as it adds in unuseful information into the desired speech. Thus for a speech recognition system, obtaining a good performance under noises has become a vital issue. To tackle the noise effect problem of automatic speech recognition (ASR), a method to reduce the noise effect is essential. Recently, multiple of methods have been developed to enhance the speech signal, they usually follow the principle of suppressing the noise in a noisy speech signal. This thesis simulated the popular techniques for speech recogniton and speech enhancement, which are the multilayer perceptron and the spectral subtraction. The aim of this work is to use MATLAB to build an automatic speech recognition system that can be used in noisy environment. MATLAB simulations are used to verify the success of recognition with clean speech and show the system performance improvements after applying speech enhancement method in seven kinds of noisy environments. The result is presented by using comparative histograms between noisy signals and corresponding denoised signals. It shows that, using denoised signal will obtain a higher recognition rate, thus we can say the system performance is improved in noisy environments.

O objetivo deste trabalho foi desenvolver um algoritmo baseado em redes neurais para o reconhecimento facial. O algoritmo contém dois módulos principais, um módulo para a extração de características e um módulo para o reconhecimento facial, sendo aplicado sobre imagens digitais nas quais a face foi previamente detectada. O método utilizado para a extração de características baseia-se na aplicação de assinaturas horizontais e verticais para localizar os componentes faciais (olhos e nariz) e definir a posição desses componentes. Como entrada foram utilizadas imagens faciais de três bancos distintos: PICS, ESSEX e AT&T. Para esse módulo, a média de acerto foi de 86.6%, para os três bancos de dados. No módulo de reconhecimento foi utilizada a arquitetura perceptron multicamadas (MLP), e para o treinamento dessa rede foi utilizado o algoritmo de aprendizagem backpropagation. As características faciais extraídas foram aplicadas nas entradas dessa rede neural, que realizou o reconhecimento da face. A rede conseguiu reconhecer 97% das imagens que foram identificadas como pertencendo ao banco de dados utilizado. Apesar dos resultados satisfatórios obtidos, constatou-se que essa rede não consegue separar adequadamente características faciais com valores muito próximos, e portanto, não é a rede mais eficiente para o reconhecimento facial / This research presents a facial recognition algorithm based in neural networks. The algorithm contains two main modules: one for feature extraction and another for face recognition. It was applied in digital images from three database, PICS, ESSEX and AT&T, where the face was previously detected. The method for feature extraction was based on previously knowledge of the facial components location (eyes and nose) and on the application of the horizontal and vertical signature for the identification of these components. The mean result obtained for this module was 86.6% for the three database. For the recognition module it was used the multilayer perceptron architecture (MLP), and for training this network it was used the backpropagation algorithm. The extracted facial features were applied to the input of the neural network, that identified the face as belonging or not to the database with 97% of hit ratio. Despite the good results obtained it was verified that the MLP could not distinguish facial features with very close values. Therefore the MLP is not the most efficient network for this task

O presente trabalho teve como objetivo apresentar uma metodologia que permita encontrar o posicionamento e rastrear as diferentes localizações de um suíno em uma baia, utilizando o valor do Receiver Signal Strenght Indicator (RSSI), entre o dispositivo móvel (suíno) e três dispositivos fixos, e uma Rede Neural Artificial do tipo Perceptron Multicamadas (PMC), responsável por interpretar os sinais RSSI e transformá-los em valores conhecidos, como em um plano cartesiano, com coordenadas no eixo X e eixo Y. A região de teste foi dividida em 289 pontos, sendo 286 utilizados para coleta de dados e para o treinamento da rede PMC. Para cada ponto, foram armazenados a sua posição dentro da baia e o valor RSSI entre o dispositivo móvel e os três dispositivos fixos. O processo foi repetido para 8 pontos escolhidos aleatoriamente dentro do espaço de teste e inseridos como entradas na rede PMC. Após treinamentos e operações realizadas com diversas arquiteturas foi possível concluir que àquela dotada de 10 neurônios na camada intermediária consistiu na melhor alternativa, cujos resultados de monitoramento e rastreamento das posições do dispositivo móvel foram encontradas com valores aceitáveis de exatidão. / This paper aims to present a methodology to find the positioning and tracking of the different locations of a pig in a stall, using the value of the Receiver Signal Strength Indicator (RSSI), between the mobile device (pig) and three devices fixed, and an Artificial Neural Network Multilayer Perceptron (MLP), responsible for interpreting the RSSI signals and turning them into known values, such as on a Cartesian plane, with coordinates on X axis and Y axis. The test region was divided into 289 points, with 286 points used for data collection and training of PMC network, and for each point, it was stored its position inside the stall and its RSSI value between the mobile devices and the three fixed. The process was repeated for 8 points chosen randomly within the space of test and entered as inputs into the PMC network. After training and operations with various architectures it was concluded that the architecture with 10 neurons in the hidden layer was the best alternative, whose the results of monitoring and tracking the positions of mobile device were found with acceptable accuracy.

No description available.

No description available.