Estimadores bayesianos de la fiabilidad con muestreo censurado

El estudio de la fiabilidad de componentes y sistemas tiene gran importancia en diversos campos de la ingenieria, y muy concretamente en el de la informatica. Al analizar la duracion de los elementos de la muestra hay que tener en cuenta los elementos que no fallan en el tiempo que dure el experimento, o bien los que fallen por causas distintas a la que es objeto de estudio. Por ello surgen nuevos tipos de muestreo que contemplan estos casos. El mas general de ellos, el muestreo censurado, es el que consideramos en nuestro trabajo. En este muestreo tanto el tiempo hasta que falla el componente como el tiempo de censura son variables aleatorias. Con la hipotesis de que ambos tiempos se distribuyen exponencialmente, el profesor Hurt estudio el comportamiento asintotico del estimador de maxima verosimilitud de la funcion de fiabilidad. En principio parece interesante utilizar metodos Bayesianos en el estudio de la fiabilidad porque incorporan al analisis la informacion a priori de la que se dispone normalmente en problemas reales. Por ello hemos considerado dos estimadores Bayesianos de la fiabilidad de una distribucion exponencial que son la media y la moda de la distribucion a posteriori. Hemos calculado la expansion asint6tica de la media, varianza y error cuadratico medio de ambos estimadores cuando la distribuci6n de censura es exponencial. Hemos obtenido tambien la distribucion asintotica de los estimadores para el caso m3s general de que la distribucion de censura sea de Weibull. Dos tipos de intervalos de confianza para muestras grandes se han propuesto para cada estimador. Los resultados se han comparado con los del estimador de maxima verosimilitud, y con los de dos estimadores no parametricos: limite producto y Bayesiano, resultando un comportamiento superior por parte de uno de nuestros estimadores. Finalmente nemos comprobado mediante simulacion que nuestros estimadores son robustos frente a la supuesta distribuci6n de censura, y que uno de los intervalos de confianza propuestos es valido con muestras pequenas. Este estudio ha servido tambien para confirmar el mejor comportamiento de uno de nuestros estimadores. SETTING OUT AND SUMMARY OF THE THESIS When we study the lifetime of components it's necessary to take into account the elements that don't fail during the experiment, or those that fail by reasons which are desirable to exclude from consideration. The model of random censorship is very usefull for analysing these data. In this model the time to failure and the time censor are random variables. We obtain two Bayes estimators of the reliability function of an exponential distribution based on randomly censored data. We have calculated the asymptotic expansion of the mean, variance and mean square error of both estimators, when the censor's distribution is exponential. We have obtained also the asymptotic distribution of the estimators for the more general case of censor's Weibull distribution. Two large-sample confidence bands have been proposed for each estimator. The results have been compared with those of the maximum likelihood estimator, and with those of two non parametric estimators: Product-limit and Bayesian. One of our estimators has the best behaviour. Finally we have shown by simulation, that our estimators are robust against the assumed censor's distribution, and that one of our intervals does well in small sample situation.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Dynamic chanel model LMS updating for RSS-based localization

Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes.

Stochastic homogenization of the laser intensity to improve the irradiation uniformity of capsules directly driven by thousands laser beams

Illumination uniformity of a spherical capsule directly driven by laser beams has been assessed numerically. Laser facilities characterized by ND = 12, 20, 24, 32, 48 and 60 directions of irradiation with associated a single laser beam or a bundle of NB laser beams have been considered. The laser beam intensity profile is assumed super-Gaussian and the calculations take into account beam imperfections as power imbalance and pointing errors. The optimum laser intensity profile, which minimizes the root-mean-square deviation of the capsule illumination, depends on the values of the beam imperfections. Assuming that the NB beams are statistically independents is found that they provide a stochastic homogenization of the laser intensity associated to the whole bundle, reducing the errors associated to the whole bundle by the factor  , which in turn improves the illumination uniformity of the capsule. Moreover, it is found that the uniformity of the irradiation is almost the same for all facilities and only depends on the total number of laser beams Ntot = ND × NB.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Study of the Influence of Reference System in Surface Finishing Parameters Evaluation.

In the present paper the influence of the reference system with regard to the characterization of the surface finishing is analyzed. The effect of the reference system’s choice on the most representative surface finishing parameters (e.g. roughness average Ra and root mean square values Rq) is studied. The study can also be applied to their equivalent parameters in waviness and primary profiles. Based on ISO and ASME standards, three different types of regression lines (central, mean and orthogonal) are theoretically and experimentally analyzed, identifying the validity and applicability fields of each one depending on profile’s geometry. El presente trabajo realiza un estudio de la influencia que supone la elección del sistema de referencia en la determinación los valores de los parámetros más relevantes empleados en la caracterización del acabado superficial tales como la rugosidad media aritmética Ra o la rugosidad media cuadrática Rq y sus equivalentes en los perfiles de ondulación y completo. Partiendo de la definición establecida por las normas ISO y ASME, se analizan tres tipos de líneas de regresión cuadrática (línea central, línea media y línea ortogonal), delimitando los campos de validez y de aplicación de cada una de ellas en función de la geometría del perfil. Para ello se plantean diversos tipos de perfiles y se desarrolla un estudio teórico y experimental de los mismos.

Video Prioritization for Unequal Error Protection

We analyze the effect of packet losses in video sequences and propose a lightweight Unequal Error Protection strategy which, by choosing which packet is discarded, reduces strongly the Mean Square Error of the received sequence

Estudio de la influencia del sistema de referencia en la evaluación de parámetros de acabado superficial

In the present paper the influence of the reference system with regard to the characterization of the surface finishing is analyzed. The effect of the reference system’s choice on the most representative surface finishing parameters (e.g. roughness average Ra and root mean square values Rq) is studied. The study can also be applied to their equivalent parameters in waviness and primary profiles. Based on ISO and ASME standards, three different types of regression lines (central, mean and orthogonal) are theoretically and experimentally analyzed, identifying the validity and applicability fields of each one depending on profile’s geometry

CSM-IXIM: A New Maize Simulation Model for DSSAT Version 4.5

Th e CERES-Maize model is the most widely used maize (Zea mays L.) model and is a recognized reference for comparing new developments in maize growth, development, and yield simulation. Th e objective of this study was to present and evaluate CSMIXIM, a new maize simulation model for DSSAT version 4.5. Code from CSM-CERES-Maize, the modular version of the model, was modifi ed to include a number of model improvements. Model enhancements included the simulation of leaf area, C assimilation and partitioning, ear growth, kernel number, grain yield, and plant N acquisition and distribution. Th e addition of two genetic coeffi cients to simulate per-leaf foliar surface produced 32% smaller root mean square error (RMSE) values estimating leaf area index than did CSM-CERES. Grain yield and total shoot biomass were correctly simulated by both models. Carbon partitioning, however, showed diff erences. Th e CSM-IXIM model simulated leaf mass more accurately, reducing the CSM-CERES error by 44%, but overestimated stem mass, especially aft er stress, resulting in similar average RMSE values as CSM-CERES. Excessive N uptake aft er fertilization events as simulated by CSM-CERES was also corrected, reducing the error by 16%. Th e accuracy of N distribution to stems was improved by 68%. Th ese improvements in CSM-IXIM provided a stable basis for more precise simulation of maize canopy growth and yield and a framework for continuing future model developments

Aplicaciones de interés forestal de las imágenes obtenidas con el sensor Multi-angle Imaging SpectroRadiometer (MISR)

Las aplicaciones de la teledetección al seguimiento de lo que ocurre en la superficie terrestre se han ido multiplicando y afinando con el lanzamiento de nuevos sensores por parte de las diferentes agencias espaciales. La necesidad de tener información actualizada cada poco tiempo y espacialmente homogénea, ha provocado el desarrollo de nuevos programas como el Earth Observing System (EOS) de la National Aeronautics and Space Administration (NASA). Uno de los sensores que incorpora el buque insignia de ese programa, el satélite TERRA, es el Multi-angle Imaging SpectroRadiometer (MISR), diseñado para capturar información multiangular de la superficie terrestre. Ya desde los años 1970, se conocía que la reflectancia de las diversas ocupaciones y usos del suelo variaba en función del ángulo de observación y de iluminación, es decir, que eran anisotrópicas. Tal variación estaba además relacionada con la estructura tridimensional de tales ocupaciones, por lo que se podía aprovechar tal relación para obtener información de esa estructura, más allá de la que pudiera proporcionar la información meramente espectral. El sensor MISR incorpora 9 cámaras a diferentes ángulos para capturar 9 imágenes casi simultáneas del mismo punto, lo que permite estimar con relativa fiabilidad la respuesta anisotrópica de la superficie terrestre. Varios trabajos han demostrado que se pueden estimar variables relacionadas con la estructura de la vegetación con la información que proporciona MISR. En esta Tesis se ha realizado una primera aplicación a la Península Ibérica, para comprobar su utilidad a la hora de estimar variables de interés forestal. En un primer paso se ha analizado la variabilidad temporal que se produce en los datos, debido a los cambios en la geometría de captación, es decir, debido a la posición relativa de sensores y fuente de iluminación, que en este caso es el Sol. Se ha comprobado cómo la anisotropía es mayor desde finales de otoño hasta principios de primavera debido a que la posición del Sol es más cercana al plano de los sensores. También se ha comprobado que los valores máximo y mínimo se van desplazando temporalmente entre el centro y el extremo angular. En la caracterización multiangular de ocupaciones del suelo de CORINE Land Cover que se ha realizado, se puede observar cómo la forma predominante en las imágenes con el Sol más alto es convexa con un máximo en la cámara más cercana a la fuente de iluminación. Sin embargo, cuando el Sol se encuentra mucho más bajo, ese máximo es muy externo. Por otra parte, los datos obtenidos en verano son mucho más variables para cada ocupación que los de noviembre, posiblemente debido al aumento proporcional de las zonas en sombra. Para comprobar si la información multiangular tiene algún efecto en la obtención de imágenes clasificadas según ocupación y usos del suelo, se han realizado una serie de clasificaciones variando la información utilizada, desde sólo multiespectral, a multiangular y multiespectral. Los resultados muestran que, mientras para las clasificaciones más genéricas la información multiangular proporciona los peores resultados, a medida que se amplían el número de clases a obtener tal información mejora a lo obtenido únicamente con información multiespectral. Por otra parte, se ha realizado una estimación de variables cuantitativas como la fracción de cabida cubierta (Fcc) y la altura de la vegetación a partir de información proporcionada por MISR a diferentes resoluciones. En el valle de Alcudia (Ciudad Real) se ha estimado la fracción de cabida cubierta del arbolado para un píxel de 275 m utilizando redes neuronales. Los resultados muestran que utilizar información multiespectral y multiangular puede mejorar casi un 20% las estimaciones realizadas sólo con datos multiespectrales. Además, las relaciones obtenidas llegan al 0,7 de R con errores inferiores a un 10% en Fcc, siendo éstos mucho mejores que los obtenidos con el producto elaborado a partir de datos multiespectrales del sensor Moderate Resolution Imaging Spectroradiometer (MODIS), también a bordo de Terra, para la misma variable. Por último, se ha estimado la fracción de cabida cubierta y la altura efectiva de la vegetación para 700.000 ha de la provincia de Murcia, con una resolución de 1.100 m. Los resultados muestran la relación existente entre los datos espectrales y los multiangulares, obteniéndose coeficientes de Spearman del orden de 0,8 en el caso de la fracción de cabida cubierta de la vegetación, y de 0,4 en el caso de la altura efectiva. Las estimaciones de ambas variables con redes neuronales y diversas combinaciones de datos, arrojan resultados con R superiores a 0,85 para el caso del grado de cubierta vegetal, y 0,6 para la altura efectiva. Los parámetros multiangulares proporcionados en los productos elaborados con MISR a 1.100 m, no obtienen buenos resultados por sí mismos pero producen cierta mejora al incorporarlos a la información espectral. Los errores cuadráticos medios obtenidos son inferiores a 0,016 para la Fcc de la vegetación en tanto por uno, y 0,7 m para la altura efectiva de la misma. Regresiones geográficamente ponderadas muestran además que localmente se pueden obtener mejores resultados aún mejores, especialmente cuando hay una mayor variabilidad espacial de las variables estimadas. En resumen, la utilización de los datos proporcionados por MISR ofrece una prometedora vía de mejora de resultados en la media-baja resolución, tanto para la clasificación de imágenes como para la obtención de variables cuantitativas de la estructura de la vegetación. ABSTRACT Applications of remote sensing for monitoring what is happening on the land surface have been multiplied and refined with the launch of new sensors by different Space Agencies. The need of having up to date and spatially homogeneous data, has led to the development of new programs such as the Earth Observing System (EOS) of the National Aeronautics and Space Administration (NASA). One of the sensors incorporating the flagship of that program, the TERRA satellite, is Multi-angle Imaging Spectroradiometer (MISR), designed to capture the multi-angle information of the Earth's surface. Since the 1970s, it was known that the reflectance of various land covers and land uses varied depending on the viewing and ilumination angles, so they are anisotropic. Such variation was also related to the three dimensional structure of such covers, so that one could take advantage of such a relationship to obtain information from that structure, beyond which spectral information could provide. The MISR sensor incorporates 9 cameras at different angles to capture 9 almost simultaneous images of the same point, allowing relatively reliable estimates of the anisotropic response of the Earth's surface. Several studies have shown that we can estimate variables related to the vegetation structure with the information provided by this sensor, so this thesis has made an initial application to the Iberian Peninsula, to check their usefulness in estimating forest variables of interest. In a first step we analyzed the temporal variability that occurs in the data, due to the changes in the acquisition geometry, i.e. the relative position of sensor and light source, which in this case is the Sun. It has been found that the anisotropy is greater from late fall through early spring due to the Sun's position closer to the plane of the sensors. It was also found that the maximum and minimum values are displaced temporarily between the center and the ends. In characterizing CORINE Land Covers that has been done, one could see how the predominant form in the images with the highest sun is convex with a maximum in the camera closer to the light source. However, when the sun is much lower, the maximum is external. Moreover, the data obtained for each land cover are much more variable in summer that in November, possibly due to the proportional increase in shadow areas. To check whether the information has any effect on multi-angle imaging classification of land cover and land use, a series of classifications have been produced changing the data used, from only multispectrally, to multi-angle and multispectral. The results show that while for the most generic classifications multi-angle information is the worst, as there are extended the number of classes to obtain such information it improves the results. On the other hand, an estimate was made of quantitative variables such as canopy cover and vegetation height using information provided by MISR at different resolutions. In the valley of Alcudia (Ciudad Real), we estimated the canopy cover of trees for a pixel of 275 m by using neural networks. The results showed that using multispectral and multiangle information can improve by almost 20% the estimates that only used multispectral data. Furthermore, the relationships obtained reached an R coefficient of 0.7 with errors below 10% in canopy cover, which is much better result than the one obtained using data from the Moderate Resolution Imaging Spectroradiometer (MODIS), also onboard Terra, for the same variable. Finally we estimated the canopy cover and the effective height of the vegetation for 700,000 hectares in the province of Murcia, with a spatial resolution of 1,100 m. The results show a relationship between the spectral and the multi-angle data, and provide estimates of the canopy cover with a Spearman’s coefficient of 0.8 in the case of the vegetation canopy cover, and 0.4 in the case of the effective height. The estimates of both variables using neural networks and various combinations of data, yield results with an R coefficient greater than 0.85 for the case of the canopy cover, and 0.6 for the effective height. Multi-angle parameters provided in the products made from MISR at 1,100 m pixel size, did not produce good results from themselves but improved the results when included to the spectral information. The mean square errors were less than 0.016 for the canopy cover, and 0.7 m for the effective height. Geographically weighted regressions also showed that locally we can have even better results, especially when there is high spatial variability of estimated variables. In summary, the use of the data provided by MISR offers a promising way of improving remote sensing performance in the low-medium spatial resolution, both for image classification and for the estimation of quantitative variables of the vegetation structure.

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

Effect of the Reference Line on Main Roughness Parameters

In the present paper the influence of the reference system with regard to the characterization of the surface finishing is analyzed. The effect of the reference system’s choice on the most representative surface finishing parameters (e.g. roughness average Ra and root mean square values Rq) is studied. The study can also be applied to their equivalent parameters in waviness and primary profiles. Based on ISO and ASME standards, three different types of regression lines (center, mean and orthogonal) are theoretically and experimentally analyzed, identifying the validity and applicability fields of each one depending on profile’s geometry.

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.