Comparative Study on modeling Efficiency Between Support Vector Machines (SVMs) model and Parallel OBF-NN model

This project is about the comparative study between model efficiency between support vector machine (SVM) and parallel OBF-NN model. To demonstrate the concept, basic support vector regression (SVR) model is developed as nonlinear model identification. Best parameter and option for SVR model is selected in order to construct optimum model performance. The study is developed using selected case study, which is using van de vusse reactor datasets. The data consist of input and output than applicable to perform simulation as training and validation data. Lastly, an OBF-SVR model is developed that use OBF model as linear part and SVR model as nonlinear part align in parallel. The performance of each developed model is tested in their performance in validation to approach real system value. The developed OBF-SVR model is compared with OBF-NN model and the deviation between each model is investigated

Artificial Intelligence Machine Learning in Marine Hydrodynamics

Artificial Intelligence (AI) Support Vector Machine (SVM) learning algorithms have enjoyed rapid growth in recent years with applications in a wide range of disciplines often with impressive results. The present paper introduces this machine learning technology to the field of marine hydrodynamics for the study of complex potential and viscous flow problems. Examples considered include the forecasting of the seastate elevations and vessel responses using their past time records as "explanatory variables" or "features" and the development of a nonlinear model for the roll restoring, added moment of inertia and viscous damping using the vessel response kinematics from free decay tests as "features". A key innovation of AI-SVM kernel algorithms is that the nonlinear dependence of the dependent variable on the "features" is embedded into the SVM kernel and its selection plays a key role in the performance of the algorithms. The kernel selection is discussed and its relation to the physics of the marine hydrodynamic flows considered in the present paper is addressed.United States. Office of Naval Research (Grant N00014-17-1-2985

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints

Kernel methods in machine learning

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.Comment: Published in at http://dx.doi.org/10.1214/009053607000000677 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Global Nonlinear Kernel Prediction for Large Dataset with a Particle Swarm Optimized Interval Support Vector Regression

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data

Modeling and Optimization of the Microwave PCB Interconnects Using Macromodel Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

Comparative Study on modeling Efficiency Between Support Vector Machines (SVMs) model and Parallel OBF-NN model

This project is about the comparative study between model efficiency between support vector machine (SVM) and parallel OBF-NN model. To demonstrate the concept, basic support vector regression (SVR) model is developed as nonlinear model identification. Best parameter and option for SVR model is selected in order to construct optimum model performance. The study is developed using selected case study, which is using van de vusse reactor datasets. The data consist of input and output than applicable to perform simulation as training and validation data. Lastly, an OBF-SVR model is developed that use OBF model as linear part and SVR model as nonlinear part align in parallel. The performance of each developed model is tested in their performance in validation to approach real system value. The developed OBF-SVR model is compared with OBF-NN model and the deviation between each model is investigated

Signal processing techniques for agro-industrial machinery monitoring

En los últimos tiempos, las técnicas de procesado de señal han ido ganando importancia dentro de numerosas aplicaciones industriales. Estos enfoques orientados al procesado de señal están abriendo nuevas perspectivas en muchas áreas del ámbito agro-industrial, destacando entre ellas la monitorización de maquinaria. El principal objetivo de esta tesis es el diseño, implementación y evaluación de esquemas de procesado de señal específicos que permitan la monitorización de equipamiento agro-industrial en tres sentidos: mantenimiento predictivo, seguimiento de vehículos y equipos de medida. Las técnicas propuestas en esta tesis contribuyen al estado del arte, expandiendo o extendiendo técnicas existentes, e incluso proponiendo esquemas completamente novedosos. La metodología seguida a lo largo de esta tesis, con objeto de alcanzar los objetivos marcados, se puede dividir en cinco etapas: revisión del estado del arte, formulación de hipótesis, desarrollo y evaluación, análisis de resultados y publicación de resultados. En esta tesis se han abordado tres problemas agro-industriales diferentes: mantenimiento predictivo de una cosechadora agrícola, seguimiento cinemático de un vehículo y monitorización del flujo a través de cada una de las boquillas en un pulverizador agrícola. Tres características principales de los métodos propuestos destacan sobre el resto. La primera es que todos los métodos satisfacen los objetivos con una precisión suficiente. La segunda característica es que todos los métodos propuestos conducen a sistemas que son asequibles y baratos. La última característica es la optimización de los métodos, que conduce a menores necesidades computacionales en comparación con otros enfoques existentes. Esta última propiedad hace que estos métodos puedan emplearse en aplicaciones con requisitos de tiempo real. Los resultados obtenidos en esta tesis ofrecen muestras de la capacidad de monitorizar maquinaria agro-industrial ofrecida por los métodos de procesado de señal. Hay dos conclusiones principales que se puede extraer de estos resultados. La primera es que las técnicas de procesado de señal pueden obtener información útil relativa a los problemas agro-industriales abordados. La segunda conclusión es que las soluciones propuestas tienden a proporcionar mayor precisión, mejor relación efectividad-coste y son más fáciles de desplegar, en comparación con otras alternativas existentes.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO 2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir.;The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (\u3e2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO 2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities.;Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model\u27s strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction.;Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN\u27s input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values.;Base on the well log analysis, four reservoir subunits within a marine-dominated estuarine depositional system are defined: barrier sand, central bay shale, tidal channels and fluvial channel subunits. A 3-D geological model, which is used to estimate theoretical CO2 sequestration capacity, is constructed with the integration of core data, wireline log data and geological background knowledge. Depending on the proposed 3-D geological model, the best regions for coupled CCUS-EOR are located in southern portions of the field, and the estimated CO2 theoretical storage capacity for Jacksonburg-Stringtown oil field vary between 24 to 383 million metric tons. The estimation results of CO2 sequestration and EOR potential indicate that the Jacksonburg-Stringtown oilfield has significant potential for CO2 storage and value-added EOR