Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Geometrically-driven underground camera modeling and calibration with coplanarity constraints for Boom-type roadheader

The conventional calibration methods based on perspective camera model are not suitable for underground camera with two-layer glasses, which is specially designed for explosion-proof and dust removal in coal mine. The underground camera modeling and calibration algorithms are urgently needed to improve the precision and reliability of underground visual measurement system. This paper presents a novel geometrically-driven underground camera calibration algorithm for Boom-type roadheader. The underground camera model is established under coplanarity constraints, considering explicitly the impact of refraction triggered by the two-layer glasses and deriving the geometrical relationship of equivalent collinearity equations. On this basis, we perform parameters calibration based on a geometrically-driven calibration model, which is a 2D-2D correspondences between the image points and object coordinates of the plannar target. A hybrid LM-PSO algorithm is further proposed in terms of the dynamic combination of the Levenberg-Marqurdt (LM) and Particle Swarm Optimization (PSO), which optimize the underground camera calibration results by minimizing the error of the nonlinear underground camera model. The experiment results demonstrate that the pose errors caused by the two-layer glass refraction are well corrected by the proposed method. The accuracy of the cutting-head pose estimation has increased by 55.73%, meeting the requirements of underground excavations

An ant colony based model to optimize parameters in industrial vision

Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations

Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010) in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA), and using the radial basis function (SVM-RBF), were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI

A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

A novel intelligent fault diagnosis method based on deep learning and particle swarm optimization support vectors machine (PSO-SVM) is proposed. The method uses deep learning neural network (DNN) to extract fault features automatically, and then uses support vector machine to classify diagnose faults based on extracted features. DNN consists of a stack of denoising autoencoders. Through pre-training and fine-tuning of DNN, features of input parameters can be extracted automatically. This paper uses particle swarm optimization algorithm to select the best parameters for SVM. The extracted features from multiple hidden layers of DNN are used as the input of PSO-SVM. Experimental data is derived from the data of rolling bearing test platform of West University. The results demonstrate that deep learning can automatically extract fault feature, which removes the need for manual feature selection, various signal processing technologies and diagnosis experience, and improves the efficiency of fault feature extraction. Under the condition of small sample size, combining the features of the multiple hidden layers as the input into the PSO-SVM can significantly increase the accuracy of fault diagnosis

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature