Influenza is a public health problem that causes serious illness and deaths all over the world. Vaccination has been shown to be the most effective mean to prevent infection. The primary component of influenza vaccine is the weakened strains. Vaccination triggers the immune system to develop antibodies against those strains whose viral surface glycoprotein hemagglutinin (HA) is similar to that of vaccine strains. However, influenza vaccine must be updated annually since the antigenic structure of HA is constantly mutation. Hemagglutination inhibition (HI) assay is a laboratory procedure frequently applied to evaluate the antigenic relationships of the influenza viruses. It enables the World Health Organization (WHO) to recommend appropriate updates on strains that will most likely be protective against the circulating influenza strains. However, HI assay is labour intensive and time-consuming since it requires several controls for standardization. We use two machine-learning methods, i.e. Artificial Neural Network (ANN) and Logistic Regression, and a Mixed-Integer Optimization Model to predict antigenic variety. The ANN generalizes the input data to patterns inherent in the data, and then uses these patterns to make predictions. The logistic regression model identifies and selects the amino acid positions, which contribute most significantly to antigenic difference. The output of the logistic regression model will be used to predict the antigenic variants based on the predicted probability. The Mixed-Integer Optimization Model is formulated to find hyperplanes that enable binary classification. The performances of our models are evaluated by cross validation.

Current activated sludge models are deterministic in character and are constructed by basing on the fundamental biokinetics. However, calibrating these models are extremely time consuming and laborious. An easy-to-calibrate and user friendly computer model, one of the artificial intelligence techniques, Artificial Neural Networks (ANNs) were used in this study. These models can be used not only directly as a substitute for deterministic models but also can be plugged into the system as error predictors. Three systems were modeled by using ANN models. Initially, a hypothetical wastewater treatment plant constructed in Simulation of Single-Sludge Processes for Carbon Oxidation, Nitrification &amp / Denitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.

In the first part of this study, two entropy methods under different distribution assumptions are examined on a network of stream gauging stations located in Kizilirmak Basin to rank the stations according to their level of importance. The stations are ranked by using two different entropy methods under different distributions. Thus, showing the effect of the distribution type on both entropy methods is aimed. In the second part of this study, autoregressive models with asymmetric innovations and an artificial neural network model are introduced. Autoregressive models (AR) which have been developed in hydrology are based on several assumptions. The normality assumption for the innovations of AR models is investigated in this study. The main reason of making this assumption in the autoregressive models established is the difficulties faced in finding the model parameters under the distributions other than the normal distributions. From this point of view, introduction of the modified maximum likelihood procedure developed by Tiku et. al. (1996) in estimation of the autoregressive model parameters having non-normally distributed residual series, in the area of hydrology has been aimed. It is also important to consider how the autoregressive model parameters having skewed distributions could be estimated. Besides these autoregressive models, the artificial neural network (ANN) model was also constructed for annual and monthly hydrologic time series due to its advantages such as no statistical distribution and no linearity assumptions. The models considered are applied to annual and monthly streamflow data obtained from five streamflow gauging stations in Kizilirmak Basin. It is shown that AR(1) model with Weibull innovations provides best solutions for annual series and AR(1) model with generalized logistic innovations provides best solution for monthly as compared with the results of artificial neural network models.

Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-10-07Bitstream added on 2014-06-13T20:00:54Z : No. of bitstreams: 1 furini_ma_dr_ilha.pdf: 763780 bytes, checksum: c5cdfef6ac6feb2737b71cbcd978ec47 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho é proposta a utilização da rede neural artificial (RNA) ARTMAP Nebulosa (fuzzy) no ajuste de parâmetros de controladores suplementares para o amortecimento de oscilações eletromecânicas de sistemas elétricos de potência, visando tornar este ajuste mais eficiente. Análises comparativas da atuação das redes neurais artificiais ARTMAP Nebulosa e Perceptron Multicamadas (PM) são realizadas para dois sistemas multimáquinas considerando o ajuste individual e coordenado dos controladores. Tais redes são utilizadas para o projeto dos controladores ESP (Estabilizadores de Sistemas de Potência) e POD (Power Oscillation Damping) acoplado ao dispositivo FACTS (Flexible Alternating Current Transmission Systems) UPFC (Unified Power Flow Controller). Será evidenciado que a RNA ARTMAP Nebulosa pode ser utilizada na melhora da estabilidade dinâmica, fornecendo resultados muito semelhantes aos da RNA Perceptron Multicamadas. Entretanto, é importante enfatizar que a vantagem da utilização da RNA ARTMAP Nebulosa está no fato da garantia da estabilidade e plasticidade associadas a um rápido treinamento, o que não ocorre com a RNA Perceptron Multicamadas / This work proposes the use of artificial neural network (ANN) Fuzzy ARTMAP to adjust the parameters of additional controllers to damp electromechanical oscillations in electric power systems in order to make this adjustment more efficient due to variations in load. Comparative analysis of the performance of artificial neural networks Fuzzy ARTMAP and Multilayer Perceptron are performed for two multimachine systems, considering individual and coordinated controller adjustment. Those networks are used for the design of Power System Stabilizers (PSS) and Power Oscillation Damping (POD) that is coupled to the FACTS (Flexible Alternating Current Transmission Systems) UPFC (Unified Power Flow Controller). It will be shown that the ANN Fuzzy ARTMAP can be used in the improvement of dynamic stability, providing very similar results to the ANN Multilayer Perceptron. However, it is important to emphasize that the advantage of using ANN Fuzzy ARTMAP is the guarantee of stability and plasticity associated with a fast training process which does not occur for the ANN Multilayer Perceptron

Made available in DSpace on 2014-06-11T19:30:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-10-27Bitstream added on 2014-06-13T20:00:56Z : No. of bitstreams: 1 lopes_mlm_dr_ilha.pdf: 1509538 bytes, checksum: 3842df54e0429972a030219c885bd09a (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Nos dias atuais, principalmente pelo fato de alguns sistemas serem desregulamentados, o estudo dos problemas de análise, planejamento e operação de sistemas de energia elétrica é de extrema importância para o funcionamento do sistema. Para isso é necessário que se obtenha, com antecedência, o comportamento da carga elétrica com o propósito de garantir o fornecimento de energia aos consumidores de forma econômica, segura e contínua. Este trabalho propõe o desenvolvimento de redes neurais artificiais utilizadas para resolver o problema de previsão de cargas elétricas. Para tanto, inicialmente, propôs-se a introdução de melhorias na rede neural feedforward com treinamento realizado utilizando o algoritmo retropropagação. Neste caso, foi desenvolvida/implementada a adaptação dos parâmetros de inclinação e translação da função sigmóide (função de ativação da rede neural). A inclusão desta nova estrutura de redes neurais produziu melhores resultados, se comparado à rede neural retropropagação convencional. Essas arquiteturas proporcionam bons resultados, porém, são estruturas de redes neurais que possuem o problema de convergência. O problema de previsão de cargas elétricas a curto-prazo necessita de uma rede neural que forneça uma saída de forma rápida e eficaz. No intuito de solucionar os problemas encontrados com o algoritmo retropropagação foi desenvolvida/implementada uma rede neural baseada na arquitetura ART (Adaptive Rossonance Theory), denominada rede neural ART&ARTMAP nebulosa, aplicada ao problema de previsão de carga elétrica. Trata-se, por conseguinte, da principal contribuição desta tese. As redes neurais, baseadas na arquitetura ART, possuem duas características fundamentais que são de extrema importância para o desempenho da rede (estabilidade e plasticidade), que permite a implementação do treinamento de modo contínuo... / Nowadays due to the deregulamentation it is very important to study the problems of analyzing, planning and operation of electric power systems. For a reliable operation it is necessary to know previously the behavior of the load to guarantee the energy providing to the users with security and continuity and in an economic way. This work proposes to develop artificial neural networks to solve the problem of electric load forecasting. First, it is introduced some improvements on the feedforward neural network, with the training effectuated with the backpropagation algorithm. The improvement was the adaptation of the inclination and translation parameters of the sigmoid function (activation function of the neural network). The inclusion of this new structure provides better results if compared to the conventional backpropagation algorithm. These architectures provide good results, although they are structures that have some convergence problems. The short term electric load forecasting problem needs a neural network that provide a fast and efficient output. To solve this problem a neural network based on the ART (Adaptive Ressonance Theory), called_ fuzzy ART&ARTMAP applied to the load-forecasting problem, was developed and implemented._This is one of the contributions of this work. Neural networks based on the ART architecture have two important characteristics for the network performance, which are stability and plasticity, allowing the continuous training. The fuzzy ART&ARTMAP neural network reduces the imprecision of the results by a mechanism that separates the binary and analogical data and processing them separately. This represents a quality and an improvement on the results (reduction of the processing time and better precision), if compared to the neural network with backpropagation training (often considered as a benchmark in precision by the specialized...(Complete abastract click electronic access below)

Sexual abuse is a horrible reality for many children around the world. As technology improves the availability of encryption schemes and anonymity over the internet, the perpetrators of these acts are increasingly hard to track. There have been several advances in recent time to automate the work of trying to catch these perpetrators and especially image recognition has seen great promise. While image recognition is a natural approach to these subjects as many abuses are documented and shared between perpetrators, there are potentially many leads that go unexplored if only focusing on images and videos. This study evaluates how methods of supervised machine learning solely based on textual data can point us to posts on forums which are connected to the distribution of child sexual abusive material. Feature representation techniques such as word-vectors, paragraphvectors and the FastText algorithm were used in conjunction with supervised machine learning methods based on deep learning, including methods of multilayer perceptrons, convolutional neural networks and long-short term memory models. The models were trained and evaluated on a dataset based on forum posts from a Dark Net leak from last year, and are evaluated as well on text collected from websites that had been manually verified by Ecpat. Those models were compared to a baseline model based on logistic regression. It was found that those state-of-the-art models achieve a similar performance, all outperforming the 'benchmark' logistic regression model. Further improvements can be achieved based on the availability of more annotated data.

Este estudo utilizou uma série histórica de dados de quatro etapas de uma produção avícola: reprodutoras pesadas, um incubatório, produção de frangos de corte e um abatedouro de frangos de corte pertencente a uma integração avícola do Rio Grande do Sul, no período de junho de 2009 a janeiro de 2010. As linhagens utilizadas foram COBB, ROSS e AVIAN. A diferença entre as médias das variáveis dos dados iniciais e a estatística descritiva foram calculadas com o programa computacional SigmaStat® Statistical Software para Windows 2.03. Foram analizados dados de 27 produtores de matrizes de frango de corte, um incubatório, 147 produtores de frango de corte e um abatedouro onde continham registro de: origem do nascedouro no incubatório, origem da incubadoura no incubatório, quantificação da contaminação por Salmonella sp., Aspergillus sp., Escherichia Coli, Pseudomonas sp. nos nascedouros, número de aviários por incubadoura, ovo de cama/ninho, percentual de linhagem, ovo trincado, minutos de incubação, minutos de nascedouro, horas de estoque, eclosão total, eclosão vendável, ovos incubáveis, aproveitamento de ovos, idade da matriz, perda de peso de ovo, peso de pinto, peso de ovo, contaminação na transferência, tipo de pinto, fertilidade, tipo de máquina, produtor, extensionista, peso do frango de primeira semana, peso do frango de segunda semana, peso do frango de terceira semana, peso do frango de quarta semana, peso do frango de quinta semana, mortalidade do frango na primeira semana, mortalidade do frango na segunda semana, mortalidade do frango na terceira semana, mortalidade do frango na quarta semana, mortalidade do frango na quinta semana, linhagem, condenação total, condenação parcial. As redes neurais foram construídas através do programa computacional NeuroShell®Predictor e NeuroShell®Classifier, desenvolvido pela Ward Systems Group. O programa identificou as variáveis escolhidas como “entradas” para o cálculo do modelo preditivo e variável de “saída” aquela a ser predita. Na primeira parte foram apresentados o treinamento das redes neurais artificiais onde foram utilizadas 50% das linhas de registro de junho de 2009 a janeiro de 2010, utilizou-se todas as variáveis de entrada que antecedem as seguintes variáveis de saída para cada rede: eclosão total, eclosão vendável, fertilidade, mortalidade de 1 semana, mortalidade de 5 semanas, perda de peso de ovo, peso de 5 semanas, tipo de pinto, condenação parcial e condenação total. A segunda parte destinou-se à validação dos modelos, onde se utilizou os outros 50% das linhas de registro com todas as variáveis de entrada que antecedem as mesmas variáveis de saída. Pode-se concluir que as redes neurais artificiais foram capazes de explicar os fenômenos envolvidos entre as quatro etapas da cadeia avícola, matrizes de frango de corte, incubatório, produção de frangos de corte e abatedouro. Esta técnica demonstra cientificamente que se podem criar critérios objetivos, onde estes se tornam uma importante ferramenta nas decisões que serão tomadas pelos gestores destes importantes setores da cadeia avícola. / This study used a historic series of four stages of poultry production: breeders, hatchery, production of broilers and broiler chicken slaughterhouse owned by a poultry integration of Rio Grande do Sul in the period from June 2009 to January 2010. The strains used were COBB, ROSS and AVIAN. The difference between the averages of the initial data and descriptive statistics were calculated with the computer program SigmaStat ® Statistical Software for Windows 2.03. We analyzed data from 27 breeders, 1 hatchery, 147 broiler producers and a slaughterhouse where contained the records of: origin of the hatcher in the hatchery, the origin of incubator in the hatchery, and quantification of Salmonella sp., Aspergillus sp., E. coli, Pseudomonas sp. contamination in hatcher, number of poultry per incubator, egg floor / nest, percentage of lineage, cracked egg, minutes of incubation, the birthplace of minutes, hours in inventory, total hatch, hatching salable, hatching eggs, usable eggs, breeder age, egg weight loss, chick weight, egg weight, contamination in the transfer, type of chick, fertility, machine type, producer, extension workers, the chicken weight of the first week, chicken weight of the second week, chicken weight of the third week, chicken weight of the fourth week, chicken weight of the fifth week, mortality of the chicken in the first week, mortality of chickens in the second week, mortality of the chicken in the third week, mortality of the chicken in the fourth week, mortality of the chicken in the fifth week, lineage, total condemnation, partial condemnation. The neural networks have been built through the computer program NeuroShell Predictor ® and NeuroShell®Classifier, developed by Ward Systems Group. The program identified the variables selected entries as “inputs” for the calculation of the predictive model and the variable “output” those to be predicted. In the first part were presented the training of artificial neural networks were used 50% of the lines of record from June 2009 to January 2010, was used all the input variables that precedes the following output variables for each network: total hatching , salable hatch, fertility, mortality of one week, mortality of five week , egg weight loss, weight of five weeks, type of chick, partial-condemnation and total condemnation. The second part was intended to validate the models, where were used the other 50% of the records lines with all input variables s that precedes the same output variables. It can be concluded that artificial neural networks were able to explain the phenomena involved between the four stages of poultry production, breeders, hatchery, broiler production and slaughterhouse. This technique proves scientifically that we can create objective criteria, and this methodology become an important tool in making decisions taken by managers of these important sectors of the poultry chain.

Este trabalho apresenta o desenvolvimento de um fluxo de projeto de circuitos digitais integrados, visando a incluir células geradas automaticamente pela ferramenta ASTRAN. Como parte integrante deste novo fluxo, desenvolveu-se uma nova técnica de comparação entre células, utilizando Redes Neurais Artificiais, para a modelagem das células ASTRAN, esta técnica se mostrou flexível ao se adaptar a diversos tipos de células e com resultados robustos tendo 5% de desvio padrão e 4% para o erro relativo. Também, foi criada uma ferramenta capaz de substituir células comerciais por células ASTRAN, tendo como objetivo melhorar as características de potência consumida e área utilizada pelo circuito, e por fim gerando um circuito misto composto de células comerciais feitas à mão e células ASTRAN geradas automaticamente. O foco principal deste trabalho encontra-se na integração do fluxo de geração de células geradas automaticamente a um fluxo de síntese comercial de circuitos digitais. Os resultados obtidos mostraram-se promissores, obtendo-se ganhos em redução de área e potência dos circuitos analisados. Em média os circuitos tiveram uma redução de 3,77% na potência consumida e 1,25% menos área utilizada. Com um acréscimo de 0,64% por parte do atraso total do circuito. / This work presents the development of a design flow for digital integrated circuits, including cells generated automatically by the ASTRAN tool. Moreover, a new technique, using Artificial Neural Networks, was developed to perform a comparison between two different cells, i.e. commercial and ASTRAN’s cell. This technique proved to be flexible when adapting to several types of cells and with robust results having 5% of standard deviation and 4% for relative error. Also, a new tool was developed, capable of performing cell replacement between ASTRAN and commercial cells, to improve power consumption an used area. Finally a mixed circuit composed of handmade commercial cells and cells automatically generated by ASTRAN was generated. A target was to mix an automatic cell synthesis tool with commercial synthesis tools dedicated to standard cells. Comparisons have shown that our approach was able to produce satisfactory results related area and power consumption. In average the circuits had a reduction of 3.77% in the power consumed and 1.25% less used area. With an increase of 0.64% due to the total delay of the circuit.

Made available in DSpace on 2014-06-11T19:31:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-12-18Bitstream added on 2014-06-13T19:41:53Z : No. of bitstreams: 1 fontes_ir_dr_botfca.pdf: 6105136 bytes, checksum: 23e89a651c99005b7465ec2d3aae3e9b (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O presente trabalho teve como objetivo a configuração de um sistema de supervisão e controle de irrigação utilizando técnicas de inteligência artificial. De acordo com metodologia adotada, o seu desenvolvimento foi realizado em três fases distintas. Inicialmente foram construídos os seguintes módulos de hardware: Unidade de Sensores, Unidade Concentradora de Dados e Sensor de Umidade do Solo do tipo Capacitivo. Em seguida foi criada uma aplicação através do programa de supervisão e controle do tipo SCADA, Elipse PRO, dedicada à supervisão e controle de uma casa de vegetação. Na fase final foram coletados os dados necessários para o treinamento de uma rede neural artificial que é parte integrante do sensor de umidade de solo do tipo capacitivo. Os resultados obtidos através de um conjunto de teste de medidas demonstraram que o sensor capacitivo apresenta comportamento e desempenho similares ao do sensor de do tipo TDR, o que permite concluir que esta solução pode representar uma significativa contribuição, viabilizando a implantação de sistemas de supervisão e controle em processos de irrigação com uma relação custo/benefício em níveis aceitáveis. / The present work had as objective the configuration of a supervisory and control system for irrigation using artificial intelligence techniques. In agreement with adopted methodology, its development was accomplished in three different phases. Initially the following hardware modules were built: Sensors Unit, Data Concentrator Unit and a Capacitive type Soil Moisture Sensor. Soon afterwards an application was created through the supervisory and control program of the type SCADA, Ellipse PRO, dedicated to the supervision and control of a green house. In the final phase the necessary data were collected for the training of an artificial neural network that is integral part of the capacitive type soil moisture sensor. With the application developed in the Ellipse PRO a database was created for the training of the artificial neural network, containing a group of 2440 measures of soil moisture obtained through a capacitive type sensor and a TDR type sensor. The results obtained through a group of test of measures demonstrated that the capacitive sensor presents a similar behavior to the of the TDR type sensor, the one that allows conclude that this solution can represent a significant contribution, making possible the implantation of supervisory and control systems in irrigation processes with a cost/benefit relationship in acceptable levels.

Master of Science / Department of Civil Engineering / Hayder A. Rasheed / The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the issue, economic strains limit the resources available for repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proved to be a cost-effective method for detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of this method suffers due to its reliance on the availability and experience of qualified personnel performing largely qualitative damage evaluations. The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection methods by developing a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate. Simply-supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. Two feedforward ANNs utilizing backpropagation learning algorithms were then trained on the FE model database with beam properties serving as inputs for both neural networks. The outputs for the first network consisted of the nodal stiffness ratios, and the sole output for the second ANN was a health index parameter, computed by normalizing the area under the stiffness ratio profile over the span length of the beam. The ANNs achieved excellent prediction accuracies with coefficients of determination (R²) exceeding 0.99 for both networks. Additional FE models were created to further assess the networks’ prediction capabilities on data not utilized in the training process. The ANNs displayed good prediction accuracies (R² > 0.8) even when predicting damage levels in beams with geometric, material, and cracking parameters dissimilar from those found in the training database. A touch-enabled user interface was developed to allow the ANN models to be utilized for on-site damage evaluations. The results of this study indicate that application of ANNs with FE modeling shows great promise in SHM for damage evaluation.