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Comparison of artificial neural network architectures in the task of tourism time series forecast

Teixeira, João Paulo; Fernandes, Paula O.
Fonte: World Academy of Science - Engineering and Technology Publicador: World Academy of Science - Engineering and Technology
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
96.11%
The authors have been developing several models based on artificial neural networks, linear regression models, Box-Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels” of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

HERNANDEZ NETO, Alberto; FIORELLI, Flavio Augusto Sanzovo
Fonte: ELSEVIER SCIENCE SA Publicador: ELSEVIER SCIENCE SA
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
96.05%
There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Bancos de dados geográficos e redes neurais artificiais: tecnologias de apoio à gestão do território.; Geographic data bank and artificial neural network: technologies of support for the territorial management.

Medeiros, José Simeão de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 27/08/1999 PT
Relevância na Pesquisa
96.21%
Este trabalho apresenta o desenvolvimento de um instrumento de apoio à gestão territorial, denominado Banco de Dados Geográficos – BDG, constituído de uma base de dados georreferenciadas, de um sistema de gerenciamento de banco de dados, de um sistema de informação geográfica – SIG e de um simulador de redes neurais artificiais – SRNA. O roteiro metodológico adotado permitiu a transposição do Detalhamento da Metodologia para Execução do Zoneamento Ecológico-Econômico pelos Estados da Amazônia Legal para um modelo conceitual materializado no BDG, que serviu de suporte para a criação de uma base de dados geográficos, na qual utilizou-se os conceitos de geo-campos e geo-objetos para modelagem das entidades geográficas definidas. Através deste ambiente computacional foram realizados procedimentos de correção e refinamento dos dados do meio físico e sócio-econômicos, de interpretação de imagens de satélite e análises e combinações dos dados, que permitiram definir unidades básicas de informação do território, a partir das quais foram geradas as sínteses referentes à potencialidade social e econômica, à sustentabilidade do ambiente, aos subsídios para ordenação do território, incluindo orientações à gestão do território na área de estudo localizada no sudoeste do estado de Rondônia. Sobre os dados do meio físico...

Fracionamento de carboidratos e proteínas e a predição da proteína bruta e suas frações e das fibras em detergentes neutro e ácido de Brachiaria brizantha cv. Marandu por uma rede neural artificial; Fractions of carbohydrates and proteins and the prediction of the crude protein and its fractions and of fibres in detergents neutral and acid of Brachiaria brizantha cv. marandu for artificial neural network

Brennecke, Käthery
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 28/02/2007 PT
Relevância na Pesquisa
96.25%
Numa área experimental de 25,2 ha formada com o capim-braquiarão (Brachiaria brizantha (Hochst) Stapf.) cv. Marandu e localizada no Campus da USP em Pirassununga/SP, durante o período de janeiro a julho de 2004, conduziu-se a presente pesquisa pela Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) com os seguintes objetivos: 1) Determinar as frações de carboidratos (A - açúcares solúveis com rápida degradação ruminal; B1- amido e pectina; B2 - parede celular com taxa de degradação mais lenta; C - fração não digerida) e as frações protéicas (A - NNP; B1 - peptídeos e oligopeptídeos; B2 - proteína verdadeira; B3 - NFDN; C - NIDA) na forragem da gramínea, baseados nas equações utilizadas pelo método de Cornell; 2) Relacionar outras variáveis com as medições em campo de experimentos paralelos e dados de elementos de clima com as frações protéicas e de carboidratos com o auxílio de um modelo computacional baseado em redes neurais artificiais (RNA). O delineamento foi em blocos completos e casualizados, com quatro tratamentos (ofertas de forragem de 5, 10, 15 e 20% - kg de massa seca por 100 kg de peso animal.dia) e quatro repetições. Cada bloco era dividido em quatro unidades experimentais de 1...

Constelação fônica e redes neurais artificiais: aplicabilidade na análise computacional da produção da fala; The phonic constellation and artificial neural network: computational analysis of speech production's aplicability

Prado, João Carlos Almeida
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 23/05/2007 PT
Relevância na Pesquisa
96.26%
Atualmente desenvolvem-se técnicas para a análise, identificação e o reconhecimento da fala. As mais eficientes mostram-se matematicamente complicadas, baseadas em análise estatísticas de dados, o que torna o sistema moroso, necessitando uma grande quantidade de dados para amostras. Este trabalho tem como objetivo apresentar a possibilidade do uso de Estruturas Neurais Artificiais Paraconsistentes no aprendizado e reconhecimento de sinais de fala, independentemente de análise estatística, ou número de amostras. A partir de um estudo piloto, identificou-se a necessidade de um aprofundamento no estudo dos Traços Formantes dos Fones. Com os Formantes dos Fones pode-se criar um sistema capaz de reconhecer sons produzidos em qualquer língua, pelas combinações da produção de sons através da emissão simultânea de um conjunto de Formantes. Como possível solução para a identificação dos Formantes dos Fones propõe-se neste trabalho a criação do conceito de Constelação Fônica, que consiste no reconhecimento de combinações de características matemáticas identificadas nos sinais sonoros de fala. Como uma forma de reconhecer estas Constelações, apresentam-se as Redes Neurais Artificiais Paraconsistentes, eficientes no reconhecimento de padrões por proximidade e com capacidade para tratamento de sinais contraditórios e paracompletos. Para a viabilização desta solução...

Predição de séries temporais econômicas por meio de redes neurais artificiais e transformada Wavelet: combinando modelo técnico e fundamentalista; Technique of economic time series prediction by artificial neural network and wavelet transform: joining technical and fundamental model

Soares, Anderson da Silva
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 07/03/2008 PT
Relevância na Pesquisa
96.2%
Este trabalho apresenta um método de predição não linear de séries temporais econômicas. O método baseia-se na análise técnica e fundamentalista de cotação de ações, filtragem wavelet, seleção de padrões e redes neurais artificiais. No modelo técnico emprega-se a transformada wavelet para filtrar a série temporal econômica de comportamentos aleatórios ou não econômicos. Após a filtragem dos dados o algoritmo de projeções sucessivas é utilizado para a seleção de padrões de treinamento para a rede neural artificial, com o objetivo de selecionar os padrões de comportamento mais importantes na série. No modelo fundamentalista utiliza-se variáveis econômicas que podem estar correlacionadas com a série, com o objetivo de aprimorar a predição da série na rede neural artificial. Para avaliação do método são utilizados dados de séries temporais econômicas referentes à cotação de preços de ações negociadas na bolsa de valores de São Paulo, onde os resultados da predição do comportamento futuro são comparados com modelos matemáticos clássicos e com o modelo convencional, que se baseia somente na análise técnica. Apresenta-se uma comparação dos resultados entre modelos técnicos, modelos matemáticos e o método proposto. O modelo matemático utilizado (ARIMA) apresentou seu melhor desempenho em séries com pouca variância...

Previsão de demanda de água na Região Metropolitana de São Paulo com redes neurais e artificiais e condições sócio-ambientais e meteorológicas.; Water demand forecasting in the metropolitan area São Paulo with Artificial Neural Network and socioenvironmental and meteorological conditions.

Santos, Cláudia Cristina dos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 17/05/2011 PT
Relevância na Pesquisa
96.17%
O presente trabalho apresenta a previsão de demanda de água em sistemas urbanos de abastecimento através de Rede Neural Artificial (RNA) utilizando dados de consumo de água e variáveis meteorológicas e socioambientais. A RNA utilizada foi uma de três camadas chamada de rede de múltiplas camadas alimentadas adiante com o algoritmo de treinamento LLSSIM (Hsu et al., 1996). Neste estudo, foram utilizados os dados de consumo de água (SABESP) e meteorológicos (IAG/USP) para o período de 2001 a 2005 para Região Metropolitana de São Paulo (RMSP). As variáveis socioambientais e meteorológicas que podem afetar o consumo de água foram analisadas. A ETA Cantareira e o setor Itaim Paulista foram utilizados para avaliar a relação entre o consumo e as variáveis antrópicas e meteorológicas para o ano de 2005. Esses conjuntos de dados foram utilizados para o treinamento, o teste e a previsão da RNA. Para a ETA Cantareira, foram criados 8 modelos e para o setor Itaim Paulista 57, sendo que os modelos 9 a 57 correspondem à previsão ideal. O desempenho dos modelos foi avaliado pelo o erro médio, erro médio absoluto, erro médio quadrático, o coeficiente de correlação, exatidão, viés, POD, FAR, CSI e POFD. Para a ETA Cantareira o melhor desempenho ocorreu para a média de 12 horas e para o Itaim Paulista a média de 6 horas. Na previsão ideal observou-se que a memória do sistema é um fator importante...

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

Murta Jr.,L.O.; Ruiz,E.E.S.; Pazin-Filho,A.; Schmidt,A.; Almeida-Filho,O.C.; Simões,M.V.; Marin-Neto,J.A.; Maciel,B.C.
Fonte: Associação Brasileira de Divulgação Científica Publicador: Associação Brasileira de Divulgação Científica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2006 EN
Relevância na Pesquisa
96.05%
The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion...

A characterization for the constitutive relationships of 42CrMo high strength steel by Artificial Neural Network and its application in isothermal deformation

Quan,Guo-zheng; Liang,Jian-ting; Lv,Wen-quan; Wu,Dong-sen; Liu,Ying-ying; Luo,Gui-chang; Zhou,Jie
Fonte: ABM, ABC, ABPol Publicador: ABM, ABC, ABPol
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2014 EN
Relevância na Pesquisa
96.13%
In hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the artificial neural network models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental data from the isothermal compressions of 42CrMo high strength steel, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental data in a wide temperature range and strain rate range. In addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The θ -σ curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model...

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Meena,Ganga Sahay; Majumdar,Gautam Chandra; Banerjee,Rintu; Kumar,Nitin; Meena,Pankaj Kumar
Fonte: Instituto de Tecnologia do Paraná - Tecpar Publicador: Instituto de Tecnologia do Paraná - Tecpar
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2014 EN
Relevância na Pesquisa
96.05%
Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network

Tavakoli,Hamid Reza; Omran,Omid Lotfi; Kutanaei,Saman Soleimani; shiade,Masoud Falahtabar
Fonte: Associação Brasileira de Ciências Mecânicas Publicador: Associação Brasileira de Ciências Mecânicas
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/11/2014 EN
Relevância na Pesquisa
96.11%
The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC.

Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network

Tavakoli,Hamid Reza; Omran,Omid Lotfi; Shiade,Masoud Falahtabar; Kutanaei,Saman Soleimani
Fonte: Associação Brasileira de Ciências Mecânicas Publicador: Associação Brasileira de Ciências Mecânicas
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2014 EN
Relevância na Pesquisa
96.07%
In this research, the combined effect of nano-silica particles and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced self-compacting concrete(SCC) is evaluated. For this purpose, 70 mixtures in A, B, C, D, E, F and G series representing 0, 1, 2, 3, 4, 5 and 6 percent of nano-silica particles in replacing cement content are cast. Each series involves three different fiber types and content; 0.2, 0.3 and 0.5% volume for steel fiber, 0.1, 0.15 and 0.2% of volume for polypropylene fiber and finally 0.15, 0.2 and 0.3% of volume for glass fiber. The results show that the simultaneous usage of an optimum percentage of fiber and nano-silica particles will improve the mechanical properties of SCC. Moreover, the obtained results from the experimental data are used to train a multi-layer perception (MLP)type artificial neural network(ANN). The trained network is then used to predict the effect of various parameters on the desired output namely the flexural tensile strength, tensile strength behavior and compressive strength.

Determinação de constituintes químicos em madeira de eucalipto por PI-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte; Determination of chemical constituents in eucalyptus wood by py-gc/ms and multivariate calibration: comparison between artificial neural network and support vector machines

Fonte: Sociedade Brasileira de Química Publicador: Sociedade Brasileira de Química
Tipo: Artigo de Revista Científica
PT_BR
Relevância na Pesquisa
96.05%
Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

Forecasting chlorine residuals in a water distribution system using an artificial neural network

Nixon, J.; Bowden, G.; Dandy, G.; Maier, H.; Holmes, M.
Fonte: Australian Water Association Publicador: Australian Water Association
Tipo: Conference paper
Publicado em //2003 EN
Relevância na Pesquisa
96.05%
Chlorine residual in a water distribution system (WDS) was forecast using two statistical models: multiple linear regression (MLR); and an artificial neural network (ANN). The case study was the trunk main from the Myponga water treatment plant (WTP) south of Adelaide, South Australia. The models were constructed using inputs including residual, water temperature, flow rate, turbidity, and pH. These values were obtained from telemetry and other WTP and WDS operational data, and from on-line measurements. Models were produced to forecast—over an 11 week period in Autumn 2002—residual at Almond Grove, which was calculated to be 24 hrs travel time—on average over this period—from Cactus Canyon. Accurate ANN models were produced to predict residual both 24 and 72 hrs in advance, using 1 hrly data points. Rejection of insignificant inputs, carried out using both the MLR and ANN models themselves, determined that residual from Cactus Canyon—at the time of prediction, and from Almond Grove—at 24 hrs before prediction, were the most significant. For MLR, residual from Cactus Canyon 24 hrs and 27 hrs before prediction at Almond Grove were the next two statistically significant inputs. For the ANN these inputs at Cactus Canyon, other time-lagged inputs from there...

Artificial neural network classification of pharyngeal high-resolution manometry with impedance data

Hoffman, M.; Mielens, J.; Omari, T.; Rommel, N.; Jiang, J.; McCulloch, T.
Fonte: Lippincott Williams & Wilkins Publicador: Lippincott Williams & Wilkins
Tipo: Artigo de Revista Científica
Publicado em //2013 EN
Relevância na Pesquisa
96.05%
OBJECTIVES/HYPOTHESIS: To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance. STUDY DESIGN: Case series evaluating new method of data analysis. METHODS: Multilayer perceptron, an artificial neural network (ANN), was evaluated for its ability to classify swallows as safe, penetration, or aspiration. Data were collected from 25 disordered subjects swallowing 5- or 10-mL boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the ANN. RESULTS: A classification accuracy of 89.4 ± 2.4% was achieved when including all parameters. Including only manometry-related parameters yielded a classification accuracy of 85.0 ± 6.0%, whereas including only impedance-related parameters yielded a classification accuracy of 76.0 ± 4.9%. Receiver operating characteristic analysis yielded areas under the curve of 0.8912 for safe, 0.8187 for aspiration, and 0.8014 for penetration. CONCLUSIONS: Classification models show high accuracy in classifying swallows from dysphagic patients as safe or unsafe. HRM-impedance with ANN represents one method that could be used clinically to screen for patients at risk for penetration or aspiration.; Matthew R. Hoffman...

Damage detection in phase II structural health monitoring benchmark problem using Bayesian designed artificial neural network

Ng, C.T.
Fonte: Hong Kong Polytechnic University; CD Publicador: Hong Kong Polytechnic University; CD
Tipo: Conference paper
Publicado em //2013 EN
Relevância na Pesquisa
96.05%
Pattern recognition using artificial neural network (ANN) is one of the promising approaches for detecting damages in structures. The basic idea of applying ANN in structural damage detection is to treat the calculated pattern features from a structural model as input and the corresponding damage scenarios as target in training an ANN. The trained ANN is then able to estimate the damage scenario by fitting the measured pattern features to the input of it. However, the design of the ANN is critical to the damage detection performance. This study presents a Bayesian model class selection method for optimal design of the ANN based on the given set of input-target training pairs, and hence, it avoids any subjective judgment and ad hoc assumption in the ANN design. The ANN designed by the Bayesian model class selection was applied to detect damages in the IASC-ASCE Structural Health Monitoring (SHM) Phase II Simulated Benchmark structure. In this study the damage induced changes in modal parameters were used as pattern features in the damage identification. Four damage cases were considered, in which single and multiples damages were considered in the IASC-ASCE SHM Phase II Benchmark structure. The results have shown that the ANN designed by the Bayesian model class selection method was able to accurately identify the damage locations and severities in all damage cases.; C.T. Ng

Estudo da caracterização de nódulos em mamogramas através de uma configuração de rede neural artificial; Study of breast masses characterization in mammograms by an artificial neural network configuration

Kinoshita, Sérgio Koodi
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/10/1998 PT
Relevância na Pesquisa
96.19%
Este trabalho apresenta um estudo de classificação de nódulos em mamograma digitalizados através de um classificador de rede neural artificial (RNA). O algoritmo de treinamento de "backpropagation" foi utilizado para ajustar os pesos da RNA. O objetivo principal deste trabalho foi determinar um método para analisar e selecionar a melhor configuração de atributos e topologia da RNA para classificar lesões mamárias do tipo nódulo. Foram escolhidas 118 imagens de regiões de interesse (ROI), sendo 68 benignas e 50 malignas de duas bases de imagens: uma do Hospital das Clínicas de Ribeirão Preto, da Universidade de São Paulo, e outra do MIAS-UK (Mammographic Image Analysis Society). O processo completo envolveu quatro etapas: detecção, extração e seleção de atributos, e classificação. Na etapa de detecção, as imagens foram submetidas ao processo combinado das técnicas segmentação de thresholding, morfologia matemática e crescimento de região. Foram extraídos 14 atributos de textura e 14 atributos de forma. Para selecionar os atributos mais discriminantes, foi utilizado o método de Jeffries-Matusita. Foram selecionados três grupos de atributos de forma, três de atributos de textura e três de atributos combinados. Análise pela curva ROC foram dirigidas para avaliar o desempenho do classificador de rede neural artificial (RNA). Os melhores resultados obtidos foram: para o grupo de atributos de forma com 5 unidades escondidas...

The use of artificial neural network in the classification of pulse-echo and TOFD ultra-sonic signals

Veiga,J. L. B. C.; Carvalho,A. A. de; Silva,I. C. da; Rebello,J. M. A.
Fonte: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM Publicador: Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2005 EN
Relevância na Pesquisa
96.2%
The present work evaluates the application of artificial neural networks for pattern recognition of ultrasonic signals using pulse-echo and TOFD (Time of Flight Diffraction) techniques in weld beads. In this study pattern classifiers are implemented by artificial neural network of backpropagation type using MATLAB®. The ultrasonic signals acquired from pulse-echo and TOFD were introduced, separately, in the artificial neural network with and without preprocessing. The preprocessing was only used to smoothen the signal improving the classification. Four conditions of weld bead were evaluated: lack of fusion (LF), lack of penetration (LP), porosity (PO) and non-defect (ND). The defects were intentionally inserted in a weld bead of AISI 1020 steel plates of 20 mm thickness and were confirmed using radiographic tests. The results obtained show that it is possible to classify ultrasonic signals of weld joints by the pulse-echo and TOFD techniques using artificial neural networks. The results showed a performance superior a 72% of success for test. Although the preprocessing of the signal improved the classification performance of the signals acquired by the TOFD technique considerably, the same didn't happen with the signals acquired by the pulse-echo technique.

Dynamic Modelling and Optimisation of Cyanobacterial C-phycocyanin Production Process by Artificial Neural Network

del Rio-Chanona, Ehecatl Antonio; Manirafasha, Emmanuel; Zhang, Dongda; Yue, Qian; Jing, Keju
Fonte: Elsevier Publicador: Elsevier
Tipo: Article; accepted version
EN
Relevância na Pesquisa
96.24%
This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.algal.2015.11.004; Artificial neural networks have been widely applied in bioprocess simulation and control due to their advantageous properties. However, their feasibility in long-term photo-fermentation process modelling and prediction as well as their efficiency on process optimisation have not been well studied so far. In the current study, an artificial neural network was constructed to simulate a 15-day fed-batch process for cyanobacterial C-phycocyanin production, which to the best of our knowledge has never been conducted. To guarantee the accuracy of artificial neural network, two strategies were implemented. The first strategy is to generate artificial data sets by adding random noise to the original data set, and the second is to choose the change of state variables as training data output. In addition, the first strategy showed the distinctive advantage of reducing the experimental effort in generating training data. By comparing with current experimental results, it is concluded that both strategies give the network great modelling and predictive power to estimate the entire fed-batch process performance...

Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Li,Qiong; Jiang,Xingwen; Liu,Donghan
Fonte: Water SA Publicador: Water SA
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2013 EN
Relevância na Pesquisa
96.16%
Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multidimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.