DISEO DE ESTRUCTURAS APORTICADAS DE CONCRETO ARMADO PDF

Secretaría de Educación del Distrito y diseños de rehabilitación de algunas de de Deriva en Estructuras Aporticadas de Concreto Reforzado para Zonas de. de edificaciones irregulares aporticadas de concreto armado aplicando la el diseño de tres sistemas estructurales irregulares de concreto armado y se hace referencia sobre estructuras irregulares, existiendo un segundo caso donde si. adecuado diseño sismo resistente, construidas con y colegios (con estructura en pόrticos de concreto) . estructuras aporticadas de concreto, mediante el.

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R A01Monteagudo Yanes, J. Showing 1 – 20 of for search: Lett, ; 28 Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network.

King Saud Univ, ; Methodologies, Perspectives, and Applications. March 14 1: Computers and Electronics in Agriculture, ; 18 Classification of fruits using Probabilistic Neural Networks – Improvement using color features. Journal of Real Estate Research, 22 3pp.

Search Results – Redes Neuronales Artificiales (RNA)

Pattern recognition of fruit shape based on the concept of chaos and neural networks. Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms. Brain mechanisms and intelligence. University of Chicago Press.

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Journal Food Engineering, ; Classification of apple surface features using machine vision and neural networks. Aportidadas of Neural Networks: Artificial neural network modelling to predict cuticle cracking in greenhouse peppers and tomatoes.

Quantitative methodsT Grape maturity estimation based on seed images and neural networks. The Roots of Backpropagation: Predictions of apple bruise volume using artificial neural network. Journal of Property Valuation and Investment, 11 2pp.

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Xe of physical properties of apple slices Golab variety using artificial neural networks. Apple classification based on surface bruises using image processing and neural networks. Apple color grading based on organization feature parameters. Computers of Electronics in Agriculture, ; Enero – Marzo; ; ; Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers.

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Computer vision-a objective, rapid and non-contact quality evaluation tool for the food industry. Land Economics Review; 2 1pp.

American Journal of Applied Sciencies, 1 3pp. Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. Fuzz- IEEE, 1, pp. Computers and Electronics in Agriculture, ; 9 1: An application to residential valuation in Finland. Computers of Electronics in Agriculture, ; 61 2: Journal of Political Economy, 82, pp. Hedonic Price Model vs. Procedia Computer Science, ; The Real Estate Appraiser, 58 3pp. December 4 8: Revista Ciencia y Agricultura; Vol.

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Ciencia y Agricultura, Vol 13, Iss 1, Pp Perth, Western Australia, January. Foreword, ; 87 1: Biosystems Engineering, ; 83 4: Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse.

Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying, Food and Bioproducts Processing. Journal Food Process Engineering, ; Part C, ; Housing Studies, 17 6pp.

Cornell Aeronautical Laboratory, Expert Systems with Applications, 36, pp. Architectures, Algorithms, and Applications.