budy santosa

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Andreas Umland

National University of "Kyiv-Mohyla Academy"

Armando Marques-Guedes

UNL - New University of Lisbon

Gwen Robbins Schug

University of North Carolina at Greensboro

Rob S E A N Wilson

University of California, Santa Cruz

Iolanda Craifaleanu

Technical University Of Civil Engineering, Bucharest

Fabián Hoyos Patiño

Universidad Nacional de Colombia (National University of Colombia)

Richard Cloutier

Université du Québec à Rimouski

Nicola Lupo

LUISS Guido Carli

Carla Freccero

University of California, Santa Cruz

Juraj Marušiak

Slovak Academy of Sciences

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Support vector regression for determining the minimum zone sphericity

International Journal of Advanced Manufacturing Technology, 2008

Several methods have been investigated to determine the deviation of manufactured spherical parts... more Several methods have been investigated to determine the deviation of manufactured spherical parts from ideal geometry. One of the most popular is the least squares technique, which is still widely employed in coordinate measuring machines used by industries. The least squares algorithm is optimal under the assumption that the data set is very large and has the inherent disadvantage of overestimating the minimum tolerance zone, resulting sometimes in the rejection of good parts. In addition, it requires that the data be distributed normally. The support vector regression approach alleviates the necessity for these assumptions. While most fitting algorithms in practice today require that the sampled data accurately represent the surface being inspected, support vector regression provides a generalization over the surface. We describe how the concepts of support vector regression can be applied to the determination of tolerance zones of nonlinear surfaces; to demonstrate the unique potential of support vector machine algorithms in the area of coordinate metrology. In specific, we address part quality inspection of spherical geometries.

Estimation of RUSLE EI30 based on 10 min interval rainfall data and GIS-based development of rainfall erosivity maps for Hitotsuse basin in Kyushu Japan

... images. 31st International Symposium on Remote Sensing of Environment, June 20-24, 2005, Sain... more

Learning networks for tornado detection

International Journal of General Systems, 2006

In this paper, different types of learning networks, such as artificial neural networks (ANNs), B... more

A Hybrid Knowledge Based-Clustering Multi-Class SVM Approach for Genes Expression Analysis

This study utilizes Support Vector Machines (SVM) for multi-class classification of a real data s... more This study utilizes Support Vector Machines (SVM) for multi-class classification of a real data set with more than two classes. The data is a set of E. coli whole-genome gene expression profiles. The problem is how to classify these genes based on their behavior in response to changing pH of the growth medium and mutation of the acid tolerance response gene regulator GadX. In order to apply these techniques, first we have to label the genes. The labels indicate the response of genes to the experimental variables: 1-unchanged, 2-decreased expression level and 3-increased expression level. To label the genes, an unsupervised K-Means clustering technique is applied in a multi-level scheme. Multi-level K-Means clustering is itself an improvement over standard K-Means applications. SVM is used here in two ways. First, labels resulting from multi-level K-Means clustering are confirmed by SVM. To judge the performance of SVM, two other methods, K-nearest neighbor (KNN) and Linear Discriminant Analysis (LDA) are implemented. The Implementation of Multi-class SVM used one-against-one method and one-against-all method. The results show that SVM outperforms KNN and LDA. The advantage of SVM includes the generalization error and the computing time. Second, different from the first application, SVM is used to label the genes after it is trained by a set of training data obtained from K-Means clustering. This alternative SVM strategy offers an improvement over standard SVM applications.

Data mining techniques for improved WSR-88D rainfall estimation

Computers & Industrial Engineering, 2002

The main objective of this paper is to utilize data mining and an intelligent system, Artificial ... more The main objective of this paper is to utilize data mining and an intelligent system, Artificial Neural Networks (ANNs), to facilitate rainfall estimation. Ground truth rainfall data are necessary to apply intelligent systems techniques. A unique source of such data is the Oklahoma Mesonet. Recently, with the advent of a national network of advanced radars (i.e. WSR-88D), massive archived data sets have been created generating terabytes of data. Data mining can draw attention to meaningful structures in the archives of such radar data, particularly if guided by knowledge of how the atmosphere operates in rain producing systems.

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Support vector regression for determining the minimum zone sphericity

International Journal of Advanced Manufacturing Technology, 2008

Estimation of RUSLE EI30 based on 10 min interval rainfall data and GIS-based development of rainfall erosivity maps for Hitotsuse basin in Kyushu Japan

... images. 31st International Symposium on Remote Sensing of Environment, June 20-24, 2005, Sain... more

Learning networks for tornado detection

International Journal of General Systems, 2006

In this paper, different types of learning networks, such as artificial neural networks (ANNs), B... more

A Hybrid Knowledge Based-Clustering Multi-Class SVM Approach for Genes Expression Analysis

Data mining techniques for improved WSR-88D rainfall estimation

Computers & Industrial Engineering, 2002

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