Quantification and prediction of the concentration of different dilutions of Lambda Cyhalothrin through colorimetry and neural networks

The Lambda Cyhalothrin is an insecticide of broad spectrum used in agriculture, to reduce the loss in crops, due to the attack of some pests. This compound has the presence of the radicals chlorine, fluorine and cyano, which can cause serious effects on human health when are ingested. Because of this, exist the need of develop non - destructive methods, capable of determining the concentration of the pesticide in farming, for eradicate the presence of this substance on the fruit used as food. To achieve this, commercial Lambda Cyhalothrin and distilled water were used, to obtain the recommended dilutions for the treatment of various pests in agriculture. The samples were analyzed through colorimetry, obtaining the characteristic color spaces for the pesticide, with a correlation of 0.92 for the parameters "a" and "b", and 0.98 for the parameter "L". The Cab chroma and Hue angle were determined in 9.72 and 275° respectively for the pure compound. in the dilution, the value of Hue angle decreases until 220°. Through neural networks in Matlab, the relationship between the reflection spectrum of the dilutions with the concentration thereof was established. Estimating a prediction in the accuracy higher than 0.98 in the coefficient of determination

Predicción de radiación solar mediante deep belief network

The continued development of computational tools offers the possibility to execute processes with the ability to carry out activities more efficiently, exactness and precision. Between these tools there is the neural architecture, Deep Belief Network (DBN), designed to collaborate in the development of prediction technics to find information that allows to study the behavior of the natural phenomena, such as the solar insolation. This paper presents the obtained results when using the DBN architecture for solar insolation prediction, simulated through the programming tool Visual Studio C#, showing the deep level that this architecture has, how it affects the number of layers and neurons per layer in the training and the results to predict the desired values in 2014, with errors close to 2% and faster to training, respect to errors obtained through conventional methods for neural training, which are about 5% and take long periods of training.El desarrollo continuo de las herramientas computacionales ofrece la posibilidad de realizar procesos con la capacidad de llevar a cabo actividades con mayor eficiencia, exactitud y precisión. Entre estas herramientas se  encuentra la arquitectura neuronal, Deep Belief Network (DBN), diseñada con el propósito de colaborar en el desarrollo de técnicas de predicción para hallar información que permita estudiar el comportamiento de los fenómenos naturales, como lo es la radiación solar. En el presente trabajo se presentan los resultados obtenidos al manejar la arquitectura DBN para predicción de radiación solar, la cual se simula mediante la herramienta de programación Visual Studio C#, indicando el nivel de profundidad que posee esta arquitectura, como afecta la cantidad de capas y de neuronas en el entrenamiento y los resultados obtenidos para poder predecir los valores deseados en el 2014, con errores cercanos al 2 % y mayor rapidez para el entrenamiento, respecto a errores  obtenidos por métodos convencionales de entrenamiento neuronal, que se encuentran por el 5% y que a su vez llevan largos periodos de entrenamiento

Revisión de las Tecnologías y Aplicaciones del Habla Sub-vocal

This paper presents a review of the main applicative and methodological approaches that have been developed in recent years for sub-vocal speech or silent language. The sub-vocal speech can be defined as the identification and characterization of bioelectric signals that control the vocal tract, when is not produced sound production by the caller. The first section makes a deep review of methods for detecting silent language. In the second part are evaluated the technologies implemented in recent years, followed by a review of the main applications of this type of speech and finally present a broad comparison between jobs that have been developed in industry and academic applications.Este trabajo presenta una revisión de estado de las principales temáticas aplicativas y metodológicas del habla sub-vocal que se han venido desarrollando en los últimos años. La primera sección hace una honda revisión de los métodos de detección del lenguaje silencioso. En la segunda parte se evalúan las tecnologías implementadas en los últimos años, seguido de un análisis en las principales aplicaciones de este tipo de lenguaje y finalmente presentado una amplia comparación entre los trabajos que se han hecho en industria y academia utilizando este tipo de desarrollos

A modeling proposal and design of a control architecture of hybrid systems applied on manufacturing industrial processes

Orientador: João Maurício RosárioTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Com o avanço da tecnologia têm sido incrementados a complexidade dos sistemas de produção industrial. Essa complexidade está marcada pela interação das variáveis que respondem a uma dinâmica baseada em eventos discretos e as variáveis que são descritas em relação ao tempo. Isto levou para uma mudança no paradigma dos sistemas automáticos de controle industrial, onde tradicionalmente essas dinâmicas são analisadas de maneira independente, para propor uma estratégia de modelagem e controle na que levem em consideração a interação das duas dinâmicas constituindo um sistema de dinâmica hibrida. Neste trabalho se desenvolve uma proposta de modelagem e controle para sistemas que respondem a uma dinâmica de arquitetura híbrida. A proposta esta baseada no recente formalismo desenvolvido como método de integração para Equações diferenciais ordinárias, denominado sistema de estado quantificado (QSS), que foi utilizado na discretização das variáveis de estado que descrevem a dinâmica continua de um sistema. Além disso, foi utilizado o formalismo de especificação de eventos discretos (DEVS), com o proposito de representar a dinâmica regida pela ocorrência de eventos e a interação com a dinâmica regida em relação ao tempo em um único modelo, permitindo projetar estratégias de controle. Com o proposito de validar a proposta, foram realizados dois estudos de caso aplicados em um processo de manufatura industrial, levando em consideração sua arquitetura dinâmica híbrida permitindo a implementação em sistemas embarcados utilizando o conceito da prototipagem rápidaAbstract: With the advancement of technology has been enhanced complexity of industrial production systems. This complexity is characterized by the interaction of variables that respond to a dynamic and discrete event based on the variables that are described in relation to time. This led to a paradigm shift in industrial automatic control systems, where traditionally these dynamics are analyzed independently, to propose a strategy for the modeling and control that take into account the dynamic interaction of the two forming a hybrid dynamic system. In this work a proposed modeling and control systems that respond to a dynamic hybrid architecture. The proposal is based on the formalism recently developed as a method of integration for ordinary differential equations, called quantified state system (QSS), which was used in the discretization of state variables that describe the dynamics of a system continues. In addition, we used the formalism of discrete event specification (DEVS) with the purpose of representing the dynamics governed by the occurrence of events and interaction with the dynamics governed over time in a single model, allowing to design control strategies. In order to validate the proposal, were carried out two case studies used in a process of industrial manufacturing, taking into account its dynamic hybrid architecture allowing the implementation in embedded systems using the concept of rapid prototypingDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Prediction of reproductive system affectation in Sprague Dawley rats by food intake exposed with fenthion, using Naïve Bayes classifier and genetic algorithms

Improper application of pesticides in agricultural crops and indirect effects caused by exposure to them through consumption of contaminated crops, nowadays represent a serious risk to public health harmony. It is vital then, to know the degree of toxicity of each of these chemicals in order to properly regulate its application and sensitize the population at risk. Therefore, this paper shows the results of an algorithm with the ability to predict the effects on the reproductive system in Sprague Dawley rats, caused by the intake of food exposed with Fenthion. The original data were processed using the Naïve Bayes classifier, then optimized using genetic algorithms. It is concluded that the prediction algorithm does the job properly, processing qualitative information with relatively low computational cost, which allows its easy portability to different development platforms

Solar Insolation Prediction through Deep Belief Network

El desarrollo continuo de las herramientas computacionales ofrece la posibilidad de realizar procesos con la capacidad de llevar a cabo actividades con mayor eficiencia, exactitud y precisión. Entre estas herramientas se  encuentra la arquitectura neuronal, Deep Belief Network (DBN), diseñada con el propósito de colaborar en el desarrollo de técnicas de predicción para hallar información que permita estudiar el comportamiento de los fenómenos naturales, como lo es la radiación solar. En el presente trabajo se presentan los resultados obtenidos al manejar la arquitectura DBN para predicción de radiación solar, la cual se simula mediante la herramienta de programación Visual Studio C#, indicando el nivel de profundidad que posee esta arquitectura, como afecta la cantidad de capas y de neuronas en el entrenamiento y los resultados obtenidos para poder predecir los valores deseados en el 2014, con errores cercanos al 2 % y mayor rapidez para el entrenamiento, respecto a errores  obtenidos por métodos convencionales de entrenamiento neuronal, que se encuentran por el 5% y que a su vez llevan largos periodos de entrenamiento.The continued development of computational tools offers the possibility to execute processes with the ability to carry out activities more efficiently, exactness and precision. Between these tools there is the neural architecture, Deep Belief Network (DBN), designed to collaborate in the development of prediction technics to find information that allows to study the behavior of the natural phenomena, such as the solar insolation. This paper presents the obtained results when using the DBN architecture for solar insolation prediction, simulated through the programming tool Visual Studio C#, showing the deep level that this architecture has, how it affects the number of layers and neurons per layer in the training and the results to predict the desired values in 2014, with errors close to 2% and faster to training, respect to errors obtained through conventional methods for neural training, which are about 5% and take long periods of training

Solar Insolation Prediction through Deep Belief Network

The continued development of computational tools offers the possibility to execute processes with the ability to carry out activities more efficiently, exactness and precision. Between these tools there is the neural architecture, Deep Belief Network (DBN), designed to collaborate in the development of prediction technics to find information that allows to study the behavior of the natural phenomena, such as the solar insolation. This paper presents the obtained results when using the DBN architecture for solar insolation prediction, simulated through the programming tool Visual Studio C#, showing the deep level that this architecture has, how it affects the number of layers and neurons per layer in the training and the results to predict the desired values in 2014, with errors close to 2% and faster to training, respect to errors obtained through conventional methods for neural training, which are about 5% and take long periods of training

Revisión de las Tecnologías y Aplicaciones del Habla Sub-vocal

This paper presents a review of the main applicative and methodological approaches that have been developed in recent years for sub-vocal speech or silent language. The sub-vocal speech can be defined as the identification and characterization of bioelectric signals that control the vocal tract, when is not produced sound production by the caller. The first section makes a deep review of methods for detecting silent language. In the second part are evaluated the technologies implemented in recent years, followed by a review of the main applications of this type of speech and finally present a broad comparison between jobs that have been developed in industry and academic applications.Este trabajo presenta una revisión de estado de las principales temáticas aplicativas y metodológicas del habla sub-vocal que se han venido desarrollando en los últimos años. La primera sección hace una honda revisión de los métodos de detección del lenguaje silencioso. En la segunda parte se evalúan las tecnologías implementadas en los últimos años, seguido de un análisis en las principales aplicaciones de este tipo de lenguaje y finalmente presentado una amplia comparación entre los trabajos que se han hecho en industria y academia utilizando este tipo de desarrollos

Technologies and Applications Review of Subvocal Speech

This paper presents a review of the main applicative and methodological approaches that have been developed in recent years for sub-vocal speech or silent language. The sub-vocal speech can be defined as the identification and characterization of bioelectric signals that control the vocal tract, when is not produced sound production by the caller. The first section makes a deep review of methods for detecting silent language. In the second part are evaluated the technologies implemented in recent years, followed by a review of the main applications of this type of speech and finally present a broad comparison between jobs that have been developed in industry and academic applications

Detección vehicular mediante técnicas de visión de máquina

El presente artículo esboza los resultados del diseño de un detector de objetos mediante clasificadores Haar, los cuales operan en función a descriptores rectangulares relacionados con la intensidad de una región en una imagen. Este clasificador se entrena para la detección de automóviles, con el objetivo de establecer la cantidad de flujo vehicular en una vía, soportados en la información proveniente de cámaras de videovigilancia. Se realiza el entrenamiento del clasificador obteniendo un porcentaje de aciertos en la detección del 92.9%, y se comparan los resultados frente a técnicas de visión de máquina como lo es el flujo óptico, presentando un desempeño superior en más del 30%. Los tiempos de procesamiento obtenidos son en promedio de 40 milisegundos