Clasificación de eventos sísmicos empleando procesos gaussianos

La clasificación de señales sísmicas es de crucial importancia para el descubrimiento de posibles interacciones entre movimientos telúricos volcánicos y procesos volcánicos per se. En este artículo, se presenta la aplicación de procesos gaussianos para la clasificación de eventos sísmicos registrados en el volcán Nevado del Ruíz. Las señales se caracterizan usando los coeficientes de un modelo autoregresivo, empleado para estimar la densidad espectral de potencia. La función de distribución predictiva para la clasificación se aproxima mediante el método de Laplace. El desempeño obtenido es mayor que el de una red neuronal artificial, clasificador utilizado tradicionalmente para resolver esta tarea.Seismic signals classification is important by itself in order to discover factual interactions between volcanic earthquakes and volcanic processes. In this paper, it is presented the application of Gaussian processes for seismic events classification registered at Nevado del Ruiz volcano. Feature extraction is accomplished using the coefficients of an autoregressive model, employed for the estimation of the power spectral density. The predictive distribution for classification is approximated using the Laplace method. Obtained performance is higher than the one obtained with an artificial neural network, the state of the art classifier for this kind of task

Evaluación de algoritmos de detección de complejos QRS mediante las curvas de funcionamiento ROC, DET y EPC

Se presenta una metodología para la selección de modelos utilizados en detección de eventos, empleando las curvas de funcionamiento característica de operación del receptor (ROC - Receiver Operating Characteristic), compensación del error de detección (DET - Detection Error Trade-off) y curvas de desempeño esperado (EPC - Expected Performance Curve), las cuales asumen un criterio de mínimo error para evaluar modelos. Las curvas se evalúan sobre algoritmos de detección de complejos QRS en electrocardiografía utilizando la base de datos de arritmias del MIT [8]. Los resultados obtenidos muestran que la mejor curva para representar el comportamiento de los métodos de detección es la curva EPC debido a que utiliza pruebas sobre conjuntos de entrenamiento y validación. Igualmente se obtiene que el mejor detector de complejos QRS es el basado en la amplitud y la primera derivada AF3.A methodology to select models used in detection is shown; it uses the performance curves named ROC, DET and EPC. These curves employ a criterion to evaluate the model based in obtaining a minimum error. Curves are applied over QRS complex detection algorithms using MIT Arrhythmia Database. Results show that the best curve for representing the behavior of the detection algorithms is the EPC curve, due to it uses training and test set. Equally, we obtained that the best QRS complex detector is AF3

Reconocimiento de expresiones faciales utilizando análisis de componentes principales Kernel (KPCA)

Este artículo presenta una metodología para el reconocimiento de expresiones faciales con análisis de componentes principales kernel, la base de datos utilizada es la Carnegie Mellon University como herramienta de prueba. El método utiliza una función kernel que mapea los datos del espacio característico original a uno de mayor dimensionalidad, de esta forma un problema de origen no lineal se traslada a uno lineal y puede resolverse linealmente, además los métodos basados en kernel pueden reducir el número de parámetros usados para la clasificación, este método es comparado con el análisis de componentes principales y es puesto a discusión donde los porcentajes de acierto encontrados con la base de datos son mayor al 90%.This paper presents a methodology on the recognition of facial expressions with kernel principal component analysis using the Carnegie Mellon University database as a testing tool. This method uses a kernel function to map data from the original feature space to a higher dimensional space, through which a nonlinear problem is translated into a linear one and is to be solved in a linear way, besides a kernel based method can reduce the number of parameters used by the clasiffier, this method compares with principal component analysis and discussed where the percentages of sucess found with the database is greater than 90%

Regresión Bayesiana lineal para calibrar los parámetros de un modelo de horno de arco

In this paper, the authors present the calibration of the parameter of arc furnace that considers the non-linearity and high variability of this type of load. Starting with a nonlinear differential equation that describes the voltage-current characteristic of the arc, an equivalent linear equation that simplifies the estimation of the original model parameters is proposed. Parameter estimation is then accomplished using Bayesian linear regression using measurements taken during the furnance's most critical operation point. Relationships between the estimated value for the parameters and their uncertainty, in terms of the number of observations included in the model calibration process, are shown. Results obtained through simulation with the estimated parameters are contrasted against real data. A flicker meter, which complies with the IEC standard 61000-4-15, is used for determining the instantaneous flicker level (IFL) due to fluctuations present in the real and simulated current waveforms. Finally, the harmonic content of the real and simulated current waveforms is compared.Este documento presenta la calibración de los parámetros de un modelo de horno de arco eléctrico, que tiene en cuenta la naturaleza no lineal y la impedancia variable que exhibe este tipo de carga. A partir de la ecuación diferencial no lineal que describe la característica estática voltaje-corriente del arco eléctrico, se establece una ecuación equivalente lineal que facilita el ajuste de las constantes del modelo, usando mediciones reales de voltaje y de corriente tomadas en la etapa más crítica de la operación del horno. Se muestra el procedimiento de ajuste de los parámetros del modelo usando regresión Bayesiana Lineal. Se presenta a través de gráficas, la relación entre los parámetros del modelo de la etapa determinista y el comportamiento de la varianza de las funciones de densidad de probabilidad Gaussianas a posterior con el número de datos usados para la calibración del modelo. La validación de los resultados obtenidos se realiza simulando el modelo con los parámetros estimados para luego comparar éstos con mediciones reales. Se ha utilizado un medidor de Flicker que cumple con el estándar CEI IEC 61000-4-15 para determinar la Sensación Instantánea de Flicker (IFL) de las fluctuaciones presentes en las formas de onda reales y simuladas de las corrientes del arco eléctrico. Adicionalmente, se presenta en una gráfica el contenido armónico real y simulado de las corrientes de fase generadas en el horno

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Construction of a system of flow and temperature instrumentation on a porcine farm in the municipality of Marsella, Risaralda

We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm.We present a local monitoring system of temperature and caudal in a pig farm. The method consists of designing an instrumentation and measurement system, this uses a wireless sensor network (WSN) based on the ZigBee standard. The WSN sends the gathered data to a server that stores the information in a database with the purpose of consulting (local queries) at any time the data that have been measured by the electronic devices. The preliminary results show that the data we can be used to infer behavior of the variables under study, besides the prototype is scalable, efficient, that makes it easily adaptable to any pig farm

Modelos de estimulación cerebral profunda para diferentes consideraciones anatómicas y eléctricas

Deep Brain Stimulation (DBS) is a clinical treatment for Parkinson disease symptoms. DBS consists in the implantation of a stimulation electrode into the Subthalamic nucleus (STN) for the excitation of specific regions inside the STN. The stimulation potential has a few parameters that should be adjusted in order to achieve the desired treatment effect. The adjust is performed by the neurologist in several sessions with the patients and is not an exact procedure. In recent years there have been several works on the construction of propagation models of DBS, including head geometries and medium properties in order to visualize the possible effects of DBS while the stimulation parameters are adjusted. This work presents the construction of propagation models using the Finite Element Method (FEM) for the solution of Laplace or Poisson equations that govern the propagation phenomena. By the construction of these models, the shape and magnitude of the electric propagation inside the objective structures can be obtained.La estimulación cerebral profunda (DBS) es una  terapia  quirúrgica  validada  para  el  tratamiento de los  síntomas  asociados  con  la  enfermedad  de  Parkinson. Consiste en la implantación de un electrodo de estimulación generalmente en la región del núcleo subtalámico (STN), con el cual se excitan regiones específicas a partir de un potencial eléctrico con ciertos parámetros específicos. El ajuste de los parámetros de estimulación es un proceso realizado por parte del neurólogo y puede tardar varios meses hasta alcanzar los resultados deseados. Es por esto que en años recientes se ha estudiado la construcción de modelos de propagación eléctrica de las estructuras objetivo de la DBS con el fin de visualizar los posibles resultados de la distribución de campo eléctrico y la activación del tejido cerebral que sirven como guía  para  el  ajuste  de  los parámetros  de  estimulación, optimizando el procedimiento de configuración. En este trabajo  se  presenta  la  comparación  de  modelos  de  simulación que incluyen la definición de geometrías complejas  representando  diferentes  estructuras  cerebrales con propiedades de diferentes tejidos, con los cuales se obtienen los patrones de propagación eléctrica cerebral por medio del método de elementos finitos (FEM) aplicado a la solución de las ecuaciones de Laplace y Poisson

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes