An expert system for diagnose of the heart valve diseases

In this paper, an expert diagnosis system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with the feature extraction from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Wavelet transforms and short time Fourier transform methods are used to feature extract from the Doppler signals on the time–frequency domain. Wavelet entropy method is applied to these features. The back-propagation neural network is used to classify the extracted features. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective to detect Doppler heart sounds. The correct classification rate was about 94% for normal subjects and 95.9% for abnormal subjects.We want to thank, the Cardiology Department of the Firat Medicine Center, Elazig, Turkey for providing the DHS signals to us. This work was supported by Firat University Research Fund. (Project No: 527)

A New Hybrid Model Based on an Intelligent Optimization Algorithm and a Data Denoising Method to Make Wind Speed Predication

To mitigate the increase of anxiety resulting from the depletion of fossil fuels and destruction of the ecosystem, wind power, as the most common renewable energy, is a flourishing industry. Thus, accurate wind speed forecasting is critical for the efficient function of wind farms. However, affected by complicated influence factors in meteorology and volatile physical property, wind speed forecasting is difficult and challenging. Based on previous research efforts, an intelligent hybrid model was proposed in this paper in an attempt to tackle this difficult task. First, wavelet transform was utilized to extract the main components of the original wind speed data while eliminating noise. To make better use of the back-propagation artificial neural network, the initial parameters of the network are substituted with optimized ones, which are achieved by using the artificial fish swarm algorithm (AFSA), and the final combination model is employed to conduct wind speed forecasting. A series of data are collected from four different observation sites to test the validity of the proposed model. Through comprehensive comparison with the traditional models, the experiment results clearly indicate that the proposed hybrid model outperforms the traditional single models

Estrategias de análisis y exploración de datos como soporte a la operación y supervisión deprocesos químicos

En esta tesis se presenta un conjunto de metodologías que intentan facilitar la tarea de explotación de la información contenida en los datos históricos de proceso y como reaprovecharlos de modo de producir un impacto positivo en la operación del proceso.Se comienza por atacar el problema de asegurar la calidad de los datos. Se hace una revisión de los métodos de filtración univariable, en especial los basados en técnicas wavelets, ya que estos últimos se han mostrado en la literatura particularmente ventajosos para el filtrado de datos. Se establece, mediante experimentos, cuales son las funciones wavelets más apropiadas para el filtrado de diversos patrones de señales. Luego, se propone una mejora a los métodos actuales de filtración con wavelets por añadir un paso previo de estimación del nivel de descomposición que afecta a la aplicación de las wavelets. Lo anterior ayuda a una mayor autonomía en la aplicación en línea de estos métodos, a la vez que aseguran precisión en la estimación de los filtrados resultantes. Adicionalmente, se propone una estrategia que combina varias wavelets para intentar dar respuesta en aplicaciones en-línea a la pregunta de cual wavelets utilizar.El problema de la calidad de los datos también se estudia a través del enfoque de Reconciliación de Datos (RD). Se intenta contribuir al desarrollo de estrategias para casos dinámicos y lineales, uno de los retos actuales de la RD. La propuesta desarrollada combina un paso inicial de extracción de tendencias mediante filtrado basado en wavelets con la posterior reconciliación de las tendencias con una técnica RD basada en la representación polinómica del modelo del proceso. Las propuestas se muestran mejor que las estrategias actuales, en términos de precisión de los estimados obtenidos. Adicionalmente, se propone una primera extensión del método para procesos altamente no lineales, obteniéndose resultados satisfactorios.En el área de supervisión se presenta un análisis comparativo de diversas estrategias de monitorización basada en Análisis de Componentes Principales (ACP) y para el caso de procesos afectados frecuentemente por perturbaciones de lenta aparición. Se propone una variante basada en filtrado wavelets con ACP que logra obtener respuestas competitivas con los métodos actuales para la detección de este tipo de perturbaciones pero que, adicionalmente, reduce drásticamente la generación de alarmas falsas.En otro bloque de trabajos de supervisión se presenta el análisis de estrategias que combina ACP con técnicas de Clustering, para la supervisión de procesos multioperacionales. En una primera parte se presenta una comparación de diversas combinaciones ACP-clustering. Esta comparación permite establecer cual de ellas brinda un mejor manejo de aspectos como la identificación de clusters de formas diversas ó el tratamiento de outliers. En la comparación se añaden leves extensiones a algunas de las técnicas existentes que conducen a un mejor manejo de todos los aspectos mencionados anteriormente. Adicionalmente, se establecen alternativas de cómo usar las técnicas para casos en que se tiene poco ó ningún conocimiento previo de los grupos de operación. A continuación, se propone una integración TEM-clustering con modelos ACP multigrupos para la supervisión de procesos multi-operacionales. A diferencia de las estrategias existentes en la literatura, se introduce el tratamiento de las transiciones durante la etapa de diseño del sistema de monitorización, como soporte al operador durante un cambio de operaciones y/o para ayudarle a reducir rápidamente el espejo de posibles causas de anormalidades tras la ocurrencia de un fallo. Finalmente, las estrategias anteriores se adaptan al análisis de procesos afectados por decaimiento en la operación. La estrategia resultante se muestra potencialmente útil tanto para profundizar en el conocimiento del proceso como para asistir en su supervisión, planificación y mantenimiento.This thesis presents a set of new methodologies that tries to exploit the information embedded in process historical data and effectively support process analysis and supervision tasks.The data rectification problem was considered in first place. The adequacy of some type of wavelet for univariate filtering of different signal patterns was studied. Then, a strategy to determine the best decomposition level was proposed and consequently, an initial step to improve current wavelet filtering approaches was found. The obtained results expand the applicability and reliability of existing filtering schemes with wavelets for on-line applications without losing of accuracy on signal estimation. Additionally, an alternative strategy was proposed to solve the problem of which wavelet to choose. This last strategy consist on a weighted combination of different wavelets functions with only one output. The data rectification problem was also studied through a Data Reconciliation (DR) approach. The focus was set on DR developments for Dynamics and Linear Systems. The proposed strategy consists on first applying a trend extraction step, to identify measured process variables trends and then reconciling these trends to make them consistent with the dynamic process model studied. For the trend extraction step, filtering using wavelets was adopted. To reconcile the estimated variables trends, a extended polynomial approach was used. The comparison with existing RD approaches shows promising results in terms of accuracy and computing efficiency. Further extensions that contemplate nonlinear cases were also introduced, showing also satisfactory results.Process Supervision problems were considered in second place. Primarily, Principal Components Analysis (PCA) based monitoring strategies for treatment of processes frequently affected by slowly appearing disturbances or small relative shifts were compared. This comparison included some new proposals combining wavelets filtering approaches and PCA. One of the proposed approaches was capable of handling the detection of small disturbances as good as other existing approaches, but dramatically reducing the problem of false alarms generation.Multioperational process supervision strategies were also considered and studied. First, a comparison of different strategies from literature was considered. The aim was to determine the strategy that produced better results in front of issues like identification of clusters with different forms or its performance facing outliers. The considered strategies are based on the combination of PCA with clustering techniques (PCA-clustering). Not only existing approaches were studied but also some extensions of them were also considered. Finally it was shown how new modified strategies lead to improve handling of all the considered issues. In addition, cluster number estimation problem was studied and some successfully strategies were proposed to perform it.Finally, the integration of the above PCA-clustering strategies with multigroup PCA for supervising of multioperational process was proposed and evaluated. The aim was to allow good process supervision capabilities for handling operating changes situations and to facilitate fault diagnosis tasks together with additional capabilities like data transitions treatment. Additionally, an extension of the above-integrated strategy for analysis and supervision of process with decaying performance was evaluated. The resulting strategy was shown as potentially useful to extract useful knowledge from data and to support supervising, planning and maintenance process tasks