Дисертації з теми "Neural networks (NNs)"
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Fischer, Manfred M. "Neural networks. A class of flexible non-linear models for regression and classification." Elgar, 2015. http://epub.wu.ac.at/4763/1/NN%2DHandbook%2Dchapter_Fischer_20120809.pdf.
Повний текст джерелаZirpe, Milind A. "RAIN and NCS 5 benchmarks." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447612.
Повний текст джерелаMattos, CÃsar Lincoln Cavalcante. "ComitÃs de Classificadores Baseados nas Redes SOM e Fuzzy ART com Sintonia de ParÃmetros e SeleÃÃo de Atributos via MetaheurÃsticas EvolucionÃrias." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7034.
Повний текст джерелаO paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃs de modificaÃÃes impostas a algoritmos de aprendizado nÃo-supervisionado, tais como as redes Fuzzy ART e SOM, dando origem, respectivamente, Ãs arquiteturas ARTIE (ART in Ensembles) e MUSCLE (Multiple SOM Classifiers in Ensembles). A sintonia dos parÃmetros e a seleÃÃo dos atributos das redes neurais que compÃem as arquiteturas ARTIE e MUSCLE foram tratados por otimizaÃÃo metaheurÃstica, a partir da proposiÃÃo do algoritmo I-HPSO (Improved Hybrid Particles Swarm Optimization). As arquiteturas ARTIE e MUSCLE foram avaliadas e comparadas com comitÃs baseados nas redes Fuzzy ARTMAP, LVQ e ELM em 12 conjuntos de dados reais. Os resultados obtidos indicam que as arquiteturas propostas apresentam desempenhos superiores aos dos comitÃs baseados em redes neurais supervisionadas.
Bezerra, Alberto Guilherme de Oliveira. "Modelos de previsão de tarifa de água, aplicados a autarquias municipais e empresas privadas, nas regiões Sul e Sudeste do Brasil /." Ilha Solteira, 2019. http://hdl.handle.net/11449/183655.
Повний текст джерелаResumo: O objetivo do presente trabalho é avaliar modelos de previsão de tarifa de água, aplicados a autarquias municipais e empresas privadas, nas regiões Sul e Sudeste do Brasil. Utilizando a metodologia de cálculo e posterior comparação dos erros obtidos para as previsões, verificando também a aplicabilidade das tarifas previstas para cada sistema de abastecimento. Utilizou-se dois modelos de previsão, o primeiro, fundamentado em técnicas de regressão linear múltipla e o segundo, baseado na aplicação de redes neurais artificiais. Avaliando, dessa forma, a capacidade de os dois modelos em questão preverem os valores tarifários a serem cobrados pelos prestadores de serviços de abastecimento de água e coleta de esgoto, a partir da análise das tarifas anteriormente praticadas. Os dados subsidiários para elaboração dos modelos foram obtidos por meio do sistema nacional de informações sobre saneamento (SNIS). Confirmada a consistência do banco de dados primário, procedeu-se com processamento destes dados, e definição das variáveis mais intervenientes para a definição da tarifa por meio da técnica de análise de correlação. Propôs-se a classificação dos sistemas de acordo com a classe jurídica do prestador de serviço, os cenários financeiros (superávit ou déficit) destes prestadores e o porte populacional dos municípios atendidos. Os resultados obtidos indicaram que os processos de previsão, em ambos os modelos utilizados, foram capazes de prever com elevada acurácia as tarifas, e garanti... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The objective of the present work was evaluating forecasting models for water tariff applied to municipal and private companies in the South and Southeast regions of Brazil. Using the calculation methodology and subsequent comparison of the errors obtained for the forecasts, also verifying the applicability of the forecast tariffs for each supply system. Two prediction models are used, the first based on multiple linear regression techniques and the second based on the application of artificial neural networks. Evaluating, in this way, the ability of the two models in question to predict the tariff values to be charged by the water supply and wastewater collection service providers, based on the analysis of the tariffs previously practiced. The subsidiary data for the elaboration of the models were obtained through the national sanitation information system (SNIS). Confirming the consistency of the primary database, we proceeded with processing of these data and definition of the most intervening variables for the definition of the tariff through the correlation analysis technique. The classification of the systems according to the legal class of the service provider, the financial scenarios (surplus or deficit) of these providers and the population size of the municipalities served were proposed. The obtained results indicated that the forecasting processes, in both models used, were able to predict with high accuracy the tariffs, and guaranteed the maintenance of the surplu... (Complete abstract click electronic access below)
Mestre
Tavares, Guilherme Farias. "Modelagem matemática e sistemas inteligentes para predição do comportamento alimentar de suínos nas fases de crescimento e terminação." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-28072017-082242/.
Повний текст джерелаThe swine production in an activity of great importance to Brazil and to the world. However, because they maintain a constant body temperature and, alterations in the thermic accommodation environment can directly affect their physiological and behavioral responses for maintaining the internal temperature. Thus, the objective of this study was to access the feeding behavior of growing-finishing pigs of different sirelines and gender and its relationship with climate variables (thermic environment). Furthermore, mathematical models based on classic logic was developed as well as an intelligent system for predicting the total time spent eating (TM, min day -1). The data was collected in an experimental farm located in Clay Center, Nebraska, United States. The experimental period contemplated two seasons (summer and winter), totalizing 63 days (9 weeks) of information collected for each season. The housed animals were from three different commercial sirelines: Landrace, Duroc and Yorkshire. Each pen presented a mix composition, being housed 40 animals of different sirelines and gender. In total, there were 240 housed animals, being 80 animals for each sireline among barrows and gilts. The data registered were air temperature (Tar, °C), dew point temperature (Tpo, °C) and relative humidity of the air (UR, %) every 5 minutes inside the facility. For TM, the data were collected and registered every 20 seconds by a radio frequency data collection system. The thermal comfort was analyzed from the Temperature and Humidity Index (THI) and Specific Enthalpy (H, kJ kg-1 of dry air). In order to evaluate the relationship between the thermic environment and TM, the multivariate statistics through principal component analysis (PCA) and grouping was utilized for obtaining the selection standards of variables to enter in the models. The fuzzy model and the artificial neural networks were developed in a MATLAB® R2015a environment through the Fuzzy and the Neural Network toolboxes with the objective to predict TM, having as entry variables: sireline, gender, age and THI. On the whole, the Tar averages were inside the thermoneutral zone (ZCT), however, these values were below the superior critic THI. In the face of the results analysis, it could be observed in ration to the feeding behavior that the Landrace gilt presented the shortest time eating with averages of 42.19 min day-1 and 43.73 min day-1 for winter and summer respectively followed by the barrow from the same sireline, while the other sirelines presented values above 60 min day-1. It was not observed a significative linear correlation between the thermic environment and TM once the animals were inside their ZCT throughout all the experimentation period, indicating that the feeding behavior was influenced mainly by the homeostatic and cognitivehedonic factors. The multivariate statistics divided the animals in 8 groups, being observed that animals of different sirelines and gender behave the same way throughout the experimentation period, making the mathematical modeling difficult. However, some groups presented a bigger amount of animals of determined sireline and gender, being utilized as \"standard groups\" for the development of the fuzzy model and the artificial neural network. The fuzzy model presented an R2 of 0,858 when utilizing the \"standard group\" data, however, for all the values the R2 was 0.549. In the other hand the neural network presented an R2 of 0.611 for the complete data and an R2 of 0.914 for the \"standard group\". Thus, the artificial neural network appeared to be a tool of a better precision and accuracy when predicting the feeding behavior of pigs on growing-finishing phases.
Cambraia, Mario Sergio. "Automação da redução de perdas técnicas nos sistemas reticulados de distribuição utilizando redes neurais artificiais em redes inteligentes (smart grid)." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-05032018-102829/.
Повний текст джерелаThis work presents the methodology, development and testing of an independent automation system, based on Artificial Neural Networks, to reduce technical losses in reticulated underground distribution networks by means of the optimal control of the capacitor banks present in the network. The proposed methodology includes typical functionalities of Intelligent Networks, including practical solutions for the positioning of current sensors in underground networks, collection of field measurements and transmission to the Distribution Operation Center and real-time control of field equipment (capacitors banks). Therefore, this work consists in the implementation of the solution through a low cost of investment in the mitigation of the control of the power factor in the points of delivery to the consumer, and with this there are improvements in the indicators of quality and reliability taking into account the regulatory and contractual requirements of supply of the distributors. The energy concessionaire AES Eletropaulo had great participation in this research project, providing the necessary data of the Reticulated Underground Distribution Network of the city center of São Paulo. The steps of the proposed methodology and the main aspects of system development are also described, as well as the tests performed to prove the results and validate the system.
Moretti, José Fernando [UNESP]. "Sistema inteligente baseado nas redes neurais artificiais para dosagem do concreto." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/100325.
Повний текст джерелаCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
O concreto é o material estrutural mais utilizado na construção civil. Há mais de um século e meio ele vem sendo estudado e aperfeiçoado. É confeccionado utilizando-se de matérias primas regionais, com características técnicas diferentes de outras regiões. O cimento também se apresenta com diversas formulações. Quantificar adequadamente esses materiais é o objetivo do estudo da dosagem do concreto, de tal modo a se obter um concreto que atenda às necessidades estruturais exigidas. Sendo a principal delas a resistência à compressão. A dosagem do concreto é uma prática essencialmente laboratorial quando se pensa em resultados aceitáveis. Através de experimentos são idealizados ábacos e diagramas que podem fornecer a resistência do concreto endurecido com diversas combinações de matérias primas utilizadas. Não há uma formulação matemática abrangente e bem definida para um processo generalizado de dosagem. A complexidade aumenta quando se adicionam outros componentes a mais no concreto simples e tradicional. Obter a relação entre eles é um trabalho contínuo. As redes neurais vêm sendo utilizadas na solução de problemas da engenharia civil, com ênfase na aplicação da técnica da retropropagação. Ela realiza satisfatoriamente as iterações entre as diversas variáveis, num processo de treinamento e aprendizagem, e tem sido capaz de generalizar soluções aceitáveis. Nesta pesquisa de doutorado é utilizada uma rede neural feedfoward com algoritmo retropropagação para prever a resistência e o módulo de elasticidade do concreto. Os dados de entrada são quantidades de materiais utilizadas para confeccionar 1 m3 de concreto adensado, de forma semelhante a dosagem por diagramas. Será aplicada na interpretação de diagramas de dosagem. Tais diagramas são amplamente utilizados por empresas na confecção de concretos,...
Concrete is the most widely used structural material in construction, for more than a century and a half it has been studied and improved. It's prepared using regional raw materials with different technical characteristics of other regions. The cement also performs with various formulations. Adequately quantify these materials is the goal of the study of the concrete mixtures proportion, to obtain a concrete that meets the structural needs required. The main one being the compressive strength. The strength of concrete is essentially a practice laboratory when one considers acceptable results. Through experiments are idealized abacus and diagrams that can provide the strength of hardened concrete with various combinations of raw materials used. There is no mathematical formulation of comprehensive and well defined for a generalized process of mixes. The complexity increases when other components is added in the most simple and traditional concrete. Obtain the relationship between them is a work in progress. Neural networks have been used in solving engineering problems, with emphasis on applying the technique of backpropagation. It performs satisfactorily iterations between the different variables in a process of training and learning, and has been able to generalize acceptable solutions. In this work is used a feedforward neural network with backpropagation algorithm to predict the compressive strength and modulus of elasticity of the concrete. The input data are quantities of materials used to fabricate 1,0 m3 of concrete hardened, similar dosing for diagrams and abacus. Such diagrams are widely used by companies in the manufacturing of concrete, yielding good precision in the final results. They are produced on the vast experience with the same materials and are highly regional representative to provide subsidies for training neural networks. This... (Complete abstract click electronic access below)
Barbato, Daniela Maria Lemos. "O efeito das lesões nas capacidades de memorização e generalização de um perceptron." Universidade de São Paulo, 1993. http://www.teses.usp.br/teses/disponiveis/54/54131/tde-05092008-144618/.
Повний текст джерелаPerceptrons are layered, feed-forward neural networks. In this work we consider a per-ceptron composed of one input layer with N sensor neurons Si = ±1; i = 1, ... , N which are connected to a single motor neuron δ through the synaptic weights Wj; i = 1, ... , N, which are constrained to take on the values ±1 only. Using the Statistical Mechanics formalism developed by Gardner (1988), we study the effects of eliminating a fraction of synaptic weights on the memorization and generalization capabilities of the neural network described above. We consider also the effects of noise acting during the perceptron training stage. We consider two types of dilution: annealed dilution, where the weights are cut so as to minimize the training error and quenched dilution, where the weights are cut randomly. The annealed dilution which models brain damage in very young patients can improve the memorization ability and, in the case of training with noise, it can also improve the generalization ability. On the other hand, the quenched dilution which models lesions on adult brains always degrades the performance of the network, its main effect being to introduce an effective noise in the training examples.
Janes, Ricardo. "Proposição de um algoritmo para identificação biométrica de pessoas baseado nos padrões de veias das mãos." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-20072016-082931/.
Повний текст джерелаThe system has been assembled as a prototype then were acquired and storaged 520 images from the dorsal side of the right hand of 52 different users, and then is accomplished an extracting of a region of interest defined by the largest square portion of the dorsal hand. Then a pre-processing of image has been applied using three different methods of image equalization and smoothing for later extraction of the veins characteristics using the Curvelet Transform in \"wrapping\" function and application of the Local Binary Pattern algorithm (LBP) for scanning the extracted content. On the next step, an identification analysis has been performed using five different classification methods. First, a probabilistic Naive Bayes classifier was used, second a classifier based on linear regression called Kernel Nearest Neighbor (K-NN) was applied, third and fourth two algorithms based on decision trees, C4.5 and Random Forest were tested, and finally an algorithm based on artificial neural networks Multilayer Perceptron was performed. The classifiers have been tested using the cross-validation method, and the information was separated by 10 folds wherein 10% of the data were used for training and 90% of the data were used for testing. From the same data resulted of the pre-processing step, two algorithms have been applied for selection features, the first based on the correlation based feature selection and the second in selecting attributes based to the concept of entropy data. The results proof that the equalization method by contrast limited adaptive histogram equalization, in the pre-processing stage, shown the best results. From the application of classifiers, the best result was achieved by using the artificial neural network proposal and the false acceptance rate (FAR) and false rejection rate (FRR) found through the processing were estimated in 0.038 and 0.003 respectively. Tests were also performed to assess the minimum amount of images needed to identify people and as result five images per user were found as the ideal number. Finally, the assessment of the biometric system permanence was performed using acquired images after a year of the first analysis and the results shown that the system is robust, even that the pictures contain minor changes proportional to index variations of body mass of users.
Mon-Ma, Marly Mitiko. "Análise da importância das variáveis intervenientes nos acidentes de trânsito em interseções urbanas utilizando redes neurais artificiais." Universidade Federal de São Carlos, 2005. https://repositorio.ufscar.br/handle/ufscar/4403.
Повний текст джерелаThe technological development has generated great amount of potential data bases in order to supply information for several aspects related to road safety. However, the transformation of these great amount of data in useful information for the technicians, public managers and the population in general, requests the modeling and the treatment of these data using some analysis tools that allow a visualization of the results in form easily understandable. This work presents a new methodology of traffic accidents analysis based in the artificial neural network (ANN). ANN can be very useful for organizations, public or particular, mainly to those that propose to understand the phenomena of the traffic in order to looking for solutions integrated to several areas such as education, engineering and fiscalization. This research had as general objective to identify the patterns of traffic accidents that happened at urban intersections. The data of accidents that happened in the period from 2000 to 2003, in the city of São Carlos were used for the case study, in order to subsidize the elaboration and the evaluation of public policies of traffic accidents reduction and specially the reduction of accident severity. The study explores the assumption that different accident types are related to different patterns. The patterns obtained by ANN showed that there are significant differences in the factors that can affect the different types of accidents. The knowledge of the patterns of each accident type is essential to develop actions corrective or preventive road safety's improvement in order to avoid undesirable effects when these actions are implemented. However, the comparison between the patterns of the different types of accidents was difficult due to the heterogeneity of the situations and the different elements that compose the road environment that can affect the occurrence of the accident.
O desenvolvimento tecnológico tem gerado grandes bases de dados com potencial para fornecer informações sobre diversos aspectos relacionados com a segurança viária. No entanto, a conversão de um grande volume de dados em informações úteis para os técnicos, gestores públicos e a população em geral, requer a modelagem e o tratamento destes dados utilizando ferramentas de análise que permitam uma visualização dos resultados de forma facilmente compreensível. Este trabalho apresenta uma nova metodologia para análise de acidentes de trânsito fundamentada na rede neural artificial (RNA). A RNA pode ser de grande utilidade para organizações públicas e privadas, principalmente para aquelas que se propõem compreender os fenômenos do trânsito a fim de buscar soluções integradas em diversas áreas tais como educação, engenharia e fiscalização. A pesquisa teve como objetivo geral identificar os padrões de acidentes de trânsito que ocorreram nas interseções urbanas. Os dados de acidentes que ocorreram no período de 2000 a 2003, na cidade de São Carlos foram utilizados para o estudo de caso, visando fornecer subsídios para a elaboração e a avaliação de políticas públicas voltadas para redução do número de acidentes de trânsito e essencialmente na redução global da severidade. O estudo explora a suposição de que diferentes tipos de acidente estão relacionados com padrões distintos. Os padrões obtidos através da RNA mostram que há divergências significativas nos fatores que podem influenciar os diferentes tipos de acidentes. Conhecer padrões de cada tipo de acidente se faz necessária para que as medidas corretivas ou preventivas voltadas para a melhoria da segurança viária não resultem em efeitos indesejados quando são implementadas, no entanto comparações entre padrões de diferentes tipos de acidentes mostraram-se particularmente difíceis devido à heterogeneidade das situações e dos diferentes elementos que compõem o ambiente viário e que podem influenciar na ocorrência do acidente.
Moretti, José Fernando. "Sistema inteligente baseado nas redes neurais artificiais para dosagem do concreto /." Ilha Solteira : [s.n.], 2010. http://hdl.handle.net/11449/100325.
Повний текст джерелаAbstract: Concrete is the most widely used structural material in construction, for more than a century and a half it has been studied and improved. It's prepared using regional raw materials with different technical characteristics of other regions. The cement also performs with various formulations. Adequately quantify these materials is the goal of the study of the concrete mixtures proportion, to obtain a concrete that meets the structural needs required. The main one being the compressive strength. The strength of concrete is essentially a practice laboratory when one considers acceptable results. Through experiments are idealized abacus and diagrams that can provide the strength of hardened concrete with various combinations of raw materials used. There is no mathematical formulation of comprehensive and well defined for a generalized process of mixes. The complexity increases when other components is added in the most simple and traditional concrete. Obtain the relationship between them is a work in progress. Neural networks have been used in solving engineering problems, with emphasis on applying the technique of backpropagation. It performs satisfactorily iterations between the different variables in a process of training and learning, and has been able to generalize acceptable solutions. In this work is used a feedforward neural network with backpropagation algorithm to predict the compressive strength and modulus of elasticity of the concrete. The input data are quantities of materials used to fabricate 1,0 m3 of concrete hardened, similar dosing for diagrams and abacus. Such diagrams are widely used by companies in the manufacturing of concrete, yielding good precision in the final results. They are produced on the vast experience with the same materials and are highly regional representative to provide subsidies for training neural networks. This... (Complete abstract click electronic access below)
Orientador: Carlos Roberto Minussi
Coorientador: Jorge Luís Akasaki
Banca: Anna Diva Plasencia Lotufo
Banca: Marco Antonio Morais Alcantara
Banca: David Calhau Jorge
Banca: Cesar Fabiano Fioriti
Doutor
Arochena, H. E. "Modelling and prediction of parameters affecting attendance to the NHS breast cancer screening programme." Thesis, Coventry University, 2003. http://curve.coventry.ac.uk/open/items/3d5373c6-9442-4479-77a2-c1bc37662cf5/1.
Повний текст джерелаRava, Eleonora Maria Elizabeth. "Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/62789.
Повний текст джерелаThesis (PhD)--University of Pretoria, 2017.
Chemical Engineering
PhD
Unrestricted
Santos, Augusto Dias Pereira dos. "Descobrindo eventos locais utilizando análise de séries temporais nos dados do Twitter." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/71953.
Повний текст джерелаThe increasing use of social networks generates enormous amounts of data that can be employed for various types of analysis. Some of these data have temporal and geographical information, which can be used to precisely position information in time and space. In this document, a new method is proposed to analyze the massive volume of messages available in Twitter to identify events such as TV shows, climate change, disasters, and sports that are occurring in specific regions of the globe. The proposed approach is based on a neural network used to detect outliers from a time series, which is built upon statistical data from tweets located in different political divisions (i.e., countries, cities). These outliers are used to identify events as an abnormal behavior in Twitter's data. The effectiveness of the method is evaluated by comparing the events identified on the news media.
Queiroz, Altamira de Souza. "Algoritmos de inteligência computacional utilizados na detecção de fraudes nas redes de distribuição de energia elétrica." Universidade Estadual do Oeste do Paraná, 2016. http://tede.unioeste.br:8080/tede/handle/tede/1030.
Повний текст джерелаCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
One of the main problems currently faced by electric utilities is the occurrence of energy losses in the distribution network caused by fraud and electricity theft. Because of the financial losses and risks to public safety, the development of solutions to detect and combat fraud in the distribution networks is of the utmost importance. This work presents an analysis of computational intelligence algorithms to extract knowledge in databases with information from monthly energy consumption to identify consumption patterns with anomalies which could represent fraud. The algorithms Artificial Neural Networks and Support Vector Machines were tested to see which one perform better on the identification consumption patterns with abnormalities. Tests have shown that the algorithms used are able to detect patterns in electricity consumption curves, including special situations of fraud that manual techniques did not detect.
Um dos principais problemas que enfrentam atualmente as empresas concessionárias de energia elétrica é a ocorrência de perdas de energia na rede de distribuição, causadas por fraudes e furtos de energia elétrica. Sendo que tais problemas provocam prejuízos financeiros e também colocam em risco a segurança pública, é de grande interesse das concessionárias encontrar soluções para detectar e combater fraudes nas redes de distribuição de energia elétrica. Neste conceito, o presente trabalho apresenta uma análise dos algoritmos de Inteligência Computacional para extrair conhecimento de bases de dados de informações de consumo mensal de energia elétricas de usuários de uma determinada concessionária, a fim de identificar padrões de consumo com anomalias que representem possíveis fraudes nas redes de distribuição de energia elétrica. Para detectar padrões nas curvas de consumo, foram utilizados algoritmos de Redes Neurais Artificiais e Máquinas de Vetores de Suporte. Após a criação dos modelos, estes foram testados para verificar qual seria o melhor algoritmo para a detecção de padrões de consumo com anomalias, e os resultados obtidos, foram então, comparados com uma base de dados fornecida pela concessionária com a verificação manual dos usuários. Os testes demonstraram que os algoritmos utilizados são capazes de detectar padrões nas curvas de consumo de energia elétrica, inclusive detectando situações especiais de fraudes que técnicas manuais não detectaram.
Souza, Sheila. "Sistema de reconhecimento de caracteres numéricos manuscritos baseado nas redes neurais artificiais paraconsistentes." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/5/5160/tde-16012014-144713/.
Повний текст джерелаComputer pattern recognition is one of the most important Artificial Intelligence tools present in numerous knowledge areas with applications in several themes, including the character recognition. The aim of this dissertation is the investigation of an automated computational process - Paraconsistent Computational System - able to recognize Handwritten Numeric Characters and Magnetic Ink Character Recognition used on Brazilian bank checks furnishing a technical basis to recognize digital documents, digital images and biological signals. Although there are several studies on character recognition, it was chosen to study this theme due to its intrinsic importance and constant improvement, besides enabling adjustments to the recognition of different kinds of signals such as, biological signals. The methodology employed for the task is based on Paraconsistent Artificial Neural Networks for being a tool with the ability to work with imprecise, inconsistent and paracomplete data without trivialization. The recognition process of this system is performed from some previously selected character features based on some Graphics techniques and, it performs the analysis of these features as well as the character recognition are performed through the Paraconsistent Artificial Neural Networks. The system was built to recognize numeric characters with a previously defined pattern where it was chosen the Magnetic Ink Character Recognition used on Brazilian bank checks and then the system was improved to recognize handwritten numeric characters. It was presented real data as checks\' batches and scanned handwritten numeric characters to validate the proposed study and the system reached 97.85% hits for Magnetic Ink Character Recognition and 91.62% hits for Handwritten Numeric Characters. The obtained result demonstrates that the system is robust enough for signal analysis study in correlated areas with similar precision level
Gontijo, Erik Sartori Jeunon [UNESP]. "Distribuição, complexação e mobilidade de íons arsênio em águas superficiais do Quadrilátero Ferrífero-MG/Brasil: ênfase nas interações com substâncias húmicas aquáticas." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150398.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Deutscher Akademischer Austauschdienst (DAAD)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
O As está distribuído em diversas formas químicas em sistemas aquáticos, o que determina o seu comportamento e destino no ambiente. Nesse contexto, as substâncias húmicas (SH) têm um importante papel por serem capazes de complexar esse metaloide e alterar sua mobilidade e biodisponibilidade. O Fe também tem grande importância por poder formar complexos ternários SH-Fe-As. Apesar da química do As já ter sido bem estudada, o seu comportamento em ambientes ricos em SH e Fe ainda não é totalmente compreendido. Os objetivos desse trabalho foram investigar a distribuição do As, Al e Fe em águas superficiais de uma região mineira no sudeste do Brasil (Quadrilátero Ferrífero, QF) e entender como características de SH extraídas de diferentes regiões (Brasil e Alemanha) afetam a complexação do As(V) na presença de Fe(III). Amostras de águas foram coletadas em 12 pontos do QF, filtradas (0,45 µm) e ultrafiltradas (1 kDa) para separar as frações particulada (>0,45 µm), coloidal (<0,45 µm e >1 kDa) e livre (<1 kDa) de As, Al e Fe. A técnica de difusão em filmes finos por gradientes de concentração (DGT) foi usada em 5 dos 12 pontos para estudar a fração lábil dos elementos estudados. Carbono orgânico total (COT) e dissolvido (COD) também foram medidos. SH foram extraídas de quatro pontos (um no Brasil nas estações seca e chuvosa e três na Alemanha) para testar a influência de diferentes tipos de SH e Fe(III) na complexação do As(V). As SH foram caracterizadas e foram feitos testes de complexação utilizando sistema de ultrafiltração com membrana de 1 kDa. Todos os dados foram analisados pela rede neural de Kohonen. Os resultados mostraram que a maior parte do Al e Fe total no QF estava presente na fração particulada e o As na fração livre. A maior parte do Al e Fe dissolvido estava na fração coloidal e inerte, diferente do As que era mais lábil e potencialmente biodisponível. A maioria das amostras apresentou comportamento similar nas estações seca e chuvosa no QF. Diferenças entre os resultados de ultrafiltração e DGT foram atribuídos a distinções metodológicas e processos químicos. Os resultados de caracterização dos extratos mostraram que a maior parte do As e Fe estavam predominantemente nas frações de maior tamanho molecular. Todos os extratos de SH complexaram quantidades similares de As(V) nos testes de complexação, exceto o extrato do rio Selke, onde foi encontrado menos As(V) livre (mais complexado). Essa diferença foi atribuída ao S e à grupos N-C aromático na estrutura de SH. Isso reforça que a qualidade das SH é um importante fator capaz de influenciar o comportamento do As em ambientes ricos em matéria orgânica e Fe, que também pareceram ser fatores limitantes nas interações com o As. A rede neural de Kohonen foi uma ferramenta importante nas investigações de distribuição do As e complexação do As(V) por Fe(III) e SH.
As is distributed in different chemical forms in aquatic systems. These different forms control its behaviour and fate in the environment. The humic substances (HS) have an important role in the As cycle since they can complex this metalloid and change its mobility and bioavailability. Fe is also important because it can form ternary complexes HS-Fe-As. Although the As chemistry is well studied, the behaviour of As in HS and Fe-rich environments is not totally known. This thesis aimed to investigate the distribution of As, Al and Fe in surface waters from a mining region in the southeast of Brazil (Quadrilátero Ferrífero, QF) and understand how characteristics of HS extracted from different regions (Brazil and Germany) affect the complexation of As(V) in the presence of Fe(III). Water samples were taken in 12 points in QF, filtered (0.45 µm) and ultrafiltered (1 kDa) to separate the fractions particulate (>0.45 µm), colloidal (<0.45 µm and >1 kDa) and free (<1 kDa) of As, Al and Fe. The technique of diffusive gradients in thin films (DGT) was used in 5 of the 12 points to study the labile fraction of the elements studied. Total organic carbon (TOC) and dissolved organic carbon (DOC) were also measured. HS were extracted from four points (one in Brazil in dry and rainy seasons and three in Germany) to analyse the influence of HS from different origins and Fe(III) on the complexation of As(V). The HS were characterised and complexation experiments were performed using an ultrafiltration system with 1 kDa membrane. All data were analysed using the Kohonen neural network. The results showed that most of total Al and Fe in QF was in the particulate fraction and As was in the free fraction. Most of the dissolved Al and Fe was in the colloidal and inert fraction, while As was more labile and potentially more bioavailable. Most samples had similar behaviour in the dry and rainy seasons in the QF. Differences between results of ultrafiltration and DGT were attributed to methodological distinctions and chemical processes. The results of characterisation of extracts showed that most of As and Fe was in the fractions of higher molecular size. All HS extracts complexed similar amounts of As(V), except Selke, where a lower amount of free As(V) was detected (more complexed). This difference was attributed to S and N-aromatic C groups in HS structure. It supports that the quality of HS is an important factor able to influence the behaviour of As in environments rich in organic matter and Fe. The Kohonen neural network was an important tool in the investigation of the distribution of As and As(V) complexation by Fe(III) and HS.
FAPESP: 2012/17727-8
Gibelli, Gerson Bessa. "Metodologia para diagnóstico e análise da influência dos afundamentos e interrupções de tensão nos motores de indução trifásicos." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-29072016-164050/.
Повний текст джерелаThis study proposes a methodology to detect and classify the disturbances observed in an Industrial Electric System (IES), in addition to, non-intrusively, estimate the electromagnetic torque and speed associated with the Three-Phase Induction Motor (TPIM) under analysis. The proposed methodology is based on the use of the Wavelet Transform WT) for the detection and location in time of voltage sags and interruptions, and on the application of the Probability Density Function (PDF) and Cross Correlation (CC) for the classification of events. After the process of events classification, the methodology, as implemented, provides the estimation of the electromagnetic torque and the TPIM speed through the three-phase voltages and currents via Artificial Neural Networks (ANN). The necessary computer simulations of a real industrial system, as well as the modeling of the TPIM, were performed by using the DIgSILENT PowerFactory software. The logic responsible for the detection and location in time correctly detected 93.4% of the assessed situations. Regarding the classification of disturbances, the index reflected 100% accuracy of the assessed situations. The ANN associated with the estimation of the electromagnetic torque and speed at the TPIM shaft showed a maximum standard deviation of 1.68 p.u. and 0.02 p.u., respectively.
(12971010), Masoud Mohammadian. "Integrating neural networks, fuzzy logic and genetic algorithms for intelligent control." Thesis, 1993. https://figshare.com/articles/thesis/Integrating_neural_networks_fuzzy_logic_and_genetic_algorithms_for_intelligent_control/20174402.
Повний текст джерелаIn this thesis, the integration of Neural Networks (NNs), Fuzzy Logic (FL) and Genetic Algorithms (GAs) for intelligent control is proposed and applied to the classical problem of docking a truck.
A backpropagation neural network architecture using a "step" update weight modification is used to obtain quickly and efficiently trajectory data from given initial states. A new algorithm to define fuzzy logic rules is used on the trajectory data to build a fuzzy logic knowledge base. This fuzzy logic knowledge base is then optimised using a genetic algorithm to obtain a fuzzy logic controller that effectively simulates a full neural network solution to the problem of docking of a truck.
Silva, Joaquim António Ribeiro da. "Previsão das dormidas mensais nos alojamentos turísticos da região norte de Portugal." Master's thesis, 2017. http://hdl.handle.net/10437/8694.
Повний текст джерелаO setor do Turismo tem uma importância estratégica para a região Norte de Portugal e está em franco crescimento. Esta dissertação considera o problema da previsão das dormidas mensais nos alojamentos turísticos da região. Trata-se de um problema atual e relevante para a gestão dos alojamentos turísticos. A abordagem proposta para o resolver baseia-se na aplicação de redes neuronais artificiais.
The Tourism sector is of strategic importance to the Northern region of Portugal and is growing. This Master’s thesis considers the problem of forecasting the monthly guest nights in tourist accommodations in the region. This is a current and relevant problem to the management of tourist accommodations. The proposed approach to solve it is based on the application of artificial neural networks.
Monteiro, Tiago Henrique Filipe. "A importância da maturidade da dívida na tomada de decisão nas PME europeias: evidência de Portugal, Espanha, França, Itália, Alemanha e Grécia." Master's thesis, 2015. http://hdl.handle.net/10071/11633.
Повний текст джерелаA estrutura de capitais e a maturidade da dívida das empresas tem constituído, nas últimas décadas, um dos temas de maior interesse na literatura financeira. Desde o estudo de Modigliani & Miller (1958), vários estudos e teorias têm sido desenvolvidas nesta área, inicialmente aplicadas nas grandes empresas, e entretanto também aplicadas ao universo das PME. O principal objetivo desta investigação é analisar quais os fatores que mais contribuem para a tomada de decisão por parte dos gestores relativamente à maturidade da dívida nas PME Europeias. A análise baseou-se na Metodologia CRISP-DM, recorrendo-se na fase da modelação a diversas técnicas de regressão, nomeadamente Regressão Linear Múltipla. Árvores de Decisão e Redes Neuronais Artificiais. A amostra é composta por 86.158 empresas de Portugal, Espanha, Itália, Alemanha, França e Grécia, com dados referentes ao ano 2013, extraídos da base de dados Amadeus. Esta investigação apresenta-se como uma das primeiras a fazer a comparação de diferentes PME de países da União Europeia bem como a utilizar técnicas de DM para a explicação da maturidade da dívida. As Árvores de Decisão e as Redes Neuronais Artificiais apresentam resultados consideravelmente superiores aos demonstrados na Regressão Linear Múltipla na previsão da maturidade da dívida de curto e, também, de longo prazo. As conclusões apontam para que o risco, a estrutura dos ativos e a alavancagem sejam os fatores que melhor explicam a tomada de decisão por parte dos gestores relativamente à maturidade da dívida. Os resultados demonstram ainda que a localização, o setor de atividade e tipo de empresa não são importantes para a explicação da maturidade da dívida.
Since the past decade the Capital Structure and more specifically the debt maturity has become, one of the topics of greatest interest in financial literature. After the Modigliani & Miller (1958) study publication, several studies and theories have been developed in this area. Those studies were initially applied in large companies and only applied to the universe of SME’s afterwards. The purpose of this research is to analyze which factors mostly contribute to the decision making by managers in relation of debt maturity according in European SME’s. The analysis was based on the CRISP-DM Methodology, with the modeling approach of using different regression techniques, including Multiple Linear Regression; Decision Trees and Artificial Neural Networks. The sample is composed of 86.158 companies from Portugal, Spain, Italy, Germany, France and Greece, in 2013, and the data was extracted from Amadeus database. This research appears as one of the firsts studying the debt maturity using the comparison between different SME’s in the European Union countries and using data mining techniques. The Decision Trees and Artificial Neural Networks show considerably better results than those shown in Multiple Linear Regression. The conclusions indicate that the factors that better explain the managers’ decision making processes regarding the debt to maturity are the risk, the structure of assets and the leverage. The results also show that the location, the business sector and the type of company are not important to explain the debt maturity.
Barmpatsalou, Konstantia. "Uma integração de métodos de aquisição forense em tempo real nos sistemas PPDR da próxima geração." Doctoral thesis, 2021. http://hdl.handle.net/10316/95279.
Повний текст джерелаOs dispositivos móveis substituíram os computadores pessoais e portáteis em muitos aspectos da rotina diária das pessoas. Na practica, eles transformaramse em impressões digitais que carregam uma quantidade crítica de informações pessoais, que variam desde conteúdo multimedia e registos de comunicação, a geolocalização e dados de transações eletrônicas. No entanto, o uso de dispositivos móveis não se limita às interacções pessoais de um indivíduo. Os dispositivos móveis podem constituir partes de redes de comunicação corporativas ou dedicadas. As redes corporativas e da emergência como os sistemas de Proteção Pública e Mitigação de Desastres (PPDR), exigem o estabelecimento de um ambiente altamente seguro, para proteger vários bens críticos. Além disso, organizações como a Polícia Judiciária acedem dados de dispositivos móveis de terceiras entidades como provas para investigações criminais. A aquisição e análise forense móvel têm um papel crucial tanto na proteção de um ambiente PPDR contra ataques intencionais ou uso indevido dos utilizadores, como na condução de uma investigação criminal robusta. Esta tese estuda o papel da aquisição e análise forense para sistemas PPDR, introduzindo uma metodologia para perfs digitais automatizados e identifcação de padrões suspeitos a partir de dados e metadados de dispositivos móveis. Três técnicas de computação inteligente, nomeadamente Fuzzy Systems, Redes Neuronais (RNs) e Adaptive Neuro-Fuzzy Inference System (ANFIS) são usadas para construir perfs criminais e identifcar padrões suspeitos em dados e metadados provenientes de chamadas e SMS para três cenários de casos de uso diferentes. Mais especifcamente, os Sistemas Fuzzy servíram como prova de conceito para detectar a deserção de agentes PPDR realizada por SMS. Um cenário mais complexo envolveu o uso de RNs e ANFIS, que foram empregados como meio de identifcação de padrões suspeitos de chamadas e SMS para casos de cyberbullying e de tráfico de droga. Os resultados que foram produzidos durante todas as fases experimentais foram bastante satisfatórios e foram comparados para defnir a técnica mais apropriada para a identifcação de padrões suspeitos.
Mobile devices have substituted desktop and portable computers in many aspects of people’s everyday routine. Practically, they have become digital fngerprints that carry a critical amount of personal information, varying from multimedia and communication logs to geolocation and electronic transaction data. Moreover, the usage of mobile devices is not limited to an individual’s personal interactions. The aforementioned devices may also constitute parts of corporate or dedicated communication networks. Enterprise and frst-responder communication networks, such as Public Protection and Disaster Relief (PPDR) systems require the establishment of a highly secured environment, in order to protect various critical assets. Moreover, services such as law enforcement may need to access third-party mobile device data as evidence for criminal investigations. Mobile forensic acquisition and analysis play a crucial role towards both the protection of a PPDR environment against intentional attacks or potential user misuse and the conduction of a robust criminal investigation. The current thesis studies the role of forensic analysis in use cases related to law enforcement investigations by introducing a methodology for automated digital profling and suspicious pattern identifcation from mobile device data and metadata. Three intelligent computation techniques, namely Fuzzy Systems, Neural Networks (NNs) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for constructing criminal profles and identifying suspicious patterns in calls and SMS evidence data and metadata for three different use case scenarios. More specifcally, Fuzzy Systems served as proof-of-concept for detecting PPDR ofcers’ defection performed by SMS. A more complex scenario for call and SMS suspicious pattern identifcation of cyberbullying and low-level drug dealing cases was documented with the use of NNs and ANFIS.