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REDES NEURONALES PARA MODELAR PREDICCIóN DE HELADAS
Ovando,Gustavo; Bocco,Mónica; Sayago,Silvina;
Agricultura Técnica , 2005, DOI: 10.4067/S0365-28072005000100007
Abstract: in this work models based on neural networks of the backpropagation type were developed in order to predict the occurrence of frosts from meteorological data such as temperature, relative humidity, cloudiness and wind direction and speed. the training and the validation of the networks were made on the basis of 24 years of meteorological data corresponding to the río cuarto station, córdoba, argentina. these data were grouped as follows: 10 years for the training data set and 14 years for the validation data set. different models were built to evaluate the performance of the networks when different numbers of input variables and/or neurons in the hidden layer are used, and the probabilities of success in the prediction results on considering different input variables. in the models used, the percentage of days with prediction error was 2%, approximately, for the 14 years of application; when effective frosts days are considered the percentage varies between 10 and 23%, for the same period. the simulation results demonstrated the good performance and the relevance of this methodology for the estimation of the behavior of non-linear phenomena like frosts.
Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina
Bocco, Mónica;Ovando, Gustavo;Sayago, Silvina;
Pesquisa Agropecuária Brasileira , 2006, DOI: 10.1590/S0100-204X2006000200001
Abstract: the objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. data from córdoba, argentina, were used for development and validation. the behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. these estimations showed root mean square error between 3.15 and 3.88 mj m-2 d-1 . the latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. in all models, results show good adjustment to seasonal solar radiation. these results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.
REDES NEURONALES PARA MODELAR PREDICCIóN DE HELADAS Neural networks for modeling frost prediction
Gustavo Ovando,Mónica Bocco,Silvina Sayago
Agricultura Técnica , 2005,
Abstract: En este trabajo se desarrollaron modelos basados en redes neuronales del tipo "backpropagation", para predecir la ocurrencia de heladas, a partir de datos meteorológicos de temperatura, humedad relativa, nubosidad, dirección y velocidad del viento. El entrenamiento y la validación de las redes se realizaron utilizando 24 a os de datos meteorológicos correspondientes a la estación de Río Cuarto, Córdoba, Argentina, separados en 10 a os como conjunto de datos de entrenamiento y 14 como conjunto de datos de validación. Se construyeron diferentes modelos para evaluar el comportamiento de las redes cuando se usan distintos números de variables de entrada y/o neuronas en la capa oculta y las probabilidades de aciertos en los resultados de predicción para los mismos, al considerar distintas variables de entrada. En los modelos realizados, el porcentaje de días con error de pronóstico fue de 2%, aproximadamente, para 14 a os de aplicación; cuando se consideran días de heladas efectivas no pronosticadas los porcentajes oscilan entre un 10% y un 23%, para el mismo período. Los resultados de la simulación muestran el buen desempe o y la pertinencia general de esta metodología en la estimación de fenómenos de comportamiento no lineal como las heladas In this work models based on neural networks of the backpropagation type were developed in order to predict the occurrence of frosts from meteorological data such as temperature, relative humidity, cloudiness and wind direction and speed. The training and the validation of the networks were made on the basis of 24 years of meteorological data corresponding to the Río Cuarto station, Córdoba, Argentina. These data were grouped as follows: 10 years for the training data set and 14 years for the validation data set. Different models were built to evaluate the performance of the networks when different numbers of input variables and/or neurons in the hidden layer are used, and the probabilities of success in the prediction results on considering different input variables. In the models used, the percentage of days with prediction error was 2%, approximately, for the 14 years of application; when effective frosts days are considered the percentage varies between 10 and 23%, for the same period. The simulation results demonstrated the good performance and the relevance of this methodology for the estimation of the behavior of non-linear phenomena like frosts.
Neural Network Model for Land Cover Classification from Satellite Images
Bocco,Mónica; Ovando,Gustavo; Sayago,Silvina; Willington,Enrique;
Agricultura Técnica , 2007, DOI: 10.4067/S0365-28072007000400009
Abstract: land cover data represent environmental information for a variety of scientific and policy applications, so its classification from satellite images is important. since neural networks (nn) do not require a hypothesis about data distribution, they are valuable tools to classify satellite images. the objectives of this work were to develop nn models to classify land cover data from information from satellite images and to evaluate them when different input variables are used. modis-myd13q1 satellite images and data of 85 plots in córdoba, argentina, were used. five nn models of multi-layer feed-forward perceptron were designed. four of these received ndvi (normalized difference vegetation index), evi (enhanced vegetation index), red (red) and near infrared (nir) reflectance values as input patterns, respectively. the fifth nn had red and nir reflectances as input values. by comparing the information taken in the field and the classification made during the validation phase, it can be concluded that all models presented good performance in the classification. the model that shows better behavior is the one that jointly considers red and nir reflectance as input; this model shows an overall classification accuracy of 93% and an excellent kappa statistic. the networks constructed with ndvi and evi values have a similar behavior (86 and 83% accuracy, respectively). the kappa statistics correspond to the categories of very good and good, respectively. the networks including only red or nir reflectance values get the lowest accuracy results (76 and 81%, respectively) and kappa values within fair and good ranks, respectively.
Neural Network Model for Land Cover Classification from Satellite Images Modelos de Redes Neuronales para la Clasificación de Cobertura del Suelo a Partir de Imágenes Satelitales
Mónica Bocco,Gustavo Ovando,Silvina Sayago,Enrique Willington
Agricultura Técnica , 2007,
Abstract: Land cover data represent environmental information for a variety of scientific and policy applications, so its classification from satellite images is important. Since neural networks (NN) do not require a hypothesis about data distribution, they are valuable tools to classify satellite images. The objectives of this work were to develop NN models to classify land cover data from information from satellite images and to evaluate them when different input variables are used. MODIS-MYD13Q1 satellite images and data of 85 plots in Córdoba, Argentina, were used. Five NN models of multi-layer feed-forward perceptron were designed. Four of these received NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), red (RED) and near infrared (NIR) reflectance values as input patterns, respectively. The fifth NN had RED and NIR reflectances as input values. By comparing the information taken in the field and the classification made during the validation phase, it can be concluded that all models presented good performance in the classification. The model that shows better behavior is the one that jointly considers RED and NIR reflectance as input; this model shows an overall classification accuracy of 93% and an excellent Kappa statistic. The networks constructed with NDVI and EVI values have a similar behavior (86 and 83% accuracy, respectively). The Kappa statistics correspond to the categories of very good and good, respectively. The networks including only RED or NIR reflectance values get the lowest accuracy results (76 and 81%, respectively) and Kappa values within fair and good ranks, respectively. Los datos de cobertura de suelo representan información ambiental clave para aplicaciones científicas y políticas, por esto su clasificación a partir de imágenes satelitales es importante. Las redes neuronales (NN) constituyen una herramienta valiosa para clasificar imágenes satelitales pues no requieren hipótesis sobre la distribución de los datos. Los objetivos de este trabajo fueron desarrollar modelos de NN para clasificar datos de cobertura de suelo a partir de información proveniente de imágenes satelitales y evaluarlos cuando se utilizan diferentes variables de entrada. Se utilizaron imágenes satelitales MODIS-MYD13Q1 y datos de 85 parcelas en Córdoba (Argentina). Se dise aron cinco NN del tipo perceptrón multicapa feed-forward. Cuatro de ellas recibieron como patrones de entrada valores de NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), de reflectancias en la bandas roja (RED) y en infrarroja cercana
Antioxidant Capacity and Bioaccessibility of Synergic Mango (cv. Ataulfo) Peel Phenolic Compounds in Edible Coatings Applied to Fresh-Cut Papaya  [PDF]
Gustavo Rubén Velderrain-Rodríguez, Maribel Ovando-Martínez, Mónica Villegas-Ochoa, Jesús Fernando Ayala-Zavala, Abraham Wall-Medrano, Emilio álvarez-Parrilla, Tomás Jesús Madera-Santana, Humberto Astiazarán-García, Orlando Tortoledo-Ortiz, Gustavo Adolfo González-Aguilar
Food and Nutrition Sciences (FNS) , 2015, DOI: 10.4236/fns.2015.63037
Abstract: Edible coatings (EC) applied to fresh-cut fruits are used to increase their shelf-life and to deliver antioxidant bioactives such as phenolic compounds (PC) that reduce their oxidative damage while enhance their functional value. However, the combination of different PC may have synergetic, additive or antagonic effects on the final antioxidant capacity (AOXC). The aim of this study was to examine the AOXC of binary combinations of selected PC from mango peel and their bioaccessibility from 6% alginate-based EC applied to fresh-cut papaya, under simulated gastrointestinal conditions. Among equimolar (0.1 mM) combinations, gallic + protocatechuic acids (AB) were synergic in radical scavenging activity (RSA) as assayed by DPPH (90% RSA) and FRAP (0.39 mg TE/mL) methods; when assayed in 6% alginate-based EC, their RSA increased (117.85% RSA, 0.88 mg TE/mL). The application of EC + AB to papaya cubes and further in vitro digestion decreased their AOXC probably due to interactions between EC and papaya’s matrix. Therefore, further studies are needed in order to evaluate the effect of combination of phenolic and EC applied in other fruits matrix on antioxidants bioaccessibility.
Uso del TAM en la implementación de la plataforma educativa móvil
Carlos Alberto Pérez Ovando,Walter Alexánder Mata López,Adriana Lorena I?íguez Carrillo,Mónica Cobián Alvarado
El Hombre y la Máquina , 2006,
Abstract: Hoy día, previo a la realización del estudio de factibilidad y viabilidad del proyecto, se propone la aplicación de los modelos de aceptación de tecnología (TAM, Technology Acceptance Model), el cual presenta una perspectiva enfocada al usuario final del proyecto, sus expectativas en relación con un nuevo sistema, y la optimización de funciones del individuo con el uso del producto final esperado del proyecto. El TAM basa su funcionalidad en el uso de cuestionarios que midan la usabilidad y facilidad de uso que se predispone en la persona frente a una herramienta, una aplicación, etc. Este trabajo presenta el modelo de implementación de la plataforma educativa móvil (Soporte Educativo Móvil -SEM), desarrollado como apoyo a docentes y alumnos, de la Facultad de Ingeniería Mecánica y Eléctrica (FIME).
Lie bialgebras of complex type and associated Poisson Lie groups
A. Andrada,M. L. Barberis,G. Ovando
Mathematics , 2006,
Abstract: In this work we study a particular class of Lie bialgebras arising from Hermitian structures on Lie algebras such that the metric is ad-invariant. We will refer to them as Lie bialgebras of complex type. These give rise to Poisson Lie groups G whose corresponding duals G* are complex Lie groups. We also prove that a Hermitian structure on the Lie algebra $\mathfrak{g}$ with ad-invariant metric induces a structure of the same type on the double Lie algebra ${\mathcal D}\mathfrak{g}= \mathfrak{g}\oplus\mathfrak{g}^*$, with respect to the canonical ad-invariant metric of neutral signature on ${\mathcal D}\mathfrak{g}$. We show how to construct a 2n-dimensional Lie bialgebra of complex type starting with one of dimension 2(n-2). This allows us to determine all solvable Lie algebras of dimension $\leq 6$ admitting a Hermitian structure with ad-invariant metric. We exhibit some examples in dimension 4 and 6, including two one-parameter families, where we identify the Lie-Poisson structures on the associated simply connected Lie groups, obtaining also their symplectic foliations.
Product structures on four dimensional solvable Lie algebras
A. Andrada,M. L. Barberis,I. Dotti,G. Ovando
Mathematics , 2004,
Abstract: It is the aim of this work to study product structures on four dimensional solvable Lie algebras. We determine all possible paracomplex structures and consider the case when one of the subalgebras is an ideal. These results are applied to the case of Manin triples and complex product structures. We also analyze the three dimensional subalgebras.
Facts and Perceptions Regarding Software Measurement in Education and in Practice: Preliminary Results  [PDF]
Mónica Villavicencio, Alain Abran
Journal of Software Engineering and Applications (JSEA) , 2011, DOI: 10.4236/jsea.2011.44025
Abstract: How is software measurement addressed in undergraduate and graduate programs in universities? Do organizations consider that the graduating students they hire have an adequate knowledge of software measurement? To answer these and related questions, a survey was administered to participants who attended the IWSM-MENSURA 2010 conference in Stuttgart, Germany. Forty-seven of the 69 conference participants (including software development practitioners, software measurement consultants, university professors, and graduate students) took part in the survey. The results indicate that software measurement topics are: 1) covered mostly at the graduate level and not at the undergraduate level, and 2) not mandatory. Graduate students and professors consider that, of the measurement topics covered in university curricula, specific topics, such as measures for the requirements phase, and measurement techniques and tools, receive more attention in the academic context. A common observation of the practitioners who participated in the survey was that students hired as new employees bring limited software measurement-related knowledge to their organizations. Discussion of the findings and directions for future research are presented.
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