Budi Santosa - Academia.edu (original) (raw)

Papers by Budi Santosa

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.

Computational Optimization and Applications, 2007

In this research, a robust optimization approach applied to multiclass support vector machines (S... more In this research, a robust optimization approach applied to multiclass support vector machines (SVMs) is investigated. Two new kernel based-methods are developed to address data with input uncertainty where each data point is inside a sphere of uncertainty. The models are called robust SVM and robust feasibility approach model (Robust-FA) respectively. The two models are compared in terms of robustness and generalization error. The models are compared to robust Minimax Probability Machine (MPM) in terms of generalization behavior for several data sets. It is shown that the Robust-SVM performs better than robust MPM.

Computers & Industrial Engineering, 2002

International Journal of Smart Engineering System Design, 2003

The main objective of this paper is to utilize standard Support Vector Regression, Least Squares ... more The main objective of this paper is to utilize standard Support Vector Regression, Least Squares Support Vector Regression, and compare these techniques to traditional regression and a rain rate formula that meteorologists use, to facilitate rainfall estimation and rainfall detection. ...

Computational Management Science, 2005

This paper utilizes Artificial Neural Networks (ANNs), standard Support Vector Regression (SVR), ... more This paper utilizes Artificial Neural Networks (ANNs), standard Support Vector Regression (SVR), Least-Squares Support Vector Regression (LS-SVR), linear regression (LR) and a rain rate (RR) formula that meteorologists use, to estimate rainfall. A unique source of ground truth rainfall data is the Oklahoma Mesonet. With the advent of the WSR-88D network of radars data mining is feasible for this study. The reflectivity measurements from the radar are used as inputs for the techniques tested. LS-SVR generalizes better than ANNs, linear regression and a rain rate formula in rainfall estimation and for rainfall detection, SVR has a better performance than the other techniques.