• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.209-212
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• 2006

In this study, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. Effect of altitude variation on the Defect Diagnostics algorithm has been included and evaluated. Separate learning Algorithm(SLA) suggested with ANN to loam the performance data selectively after classifying the position of defects by SVM improves the classification speed and accuracy.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.211-213
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• 2006

In this study, plasma etching process was modeled by using support vector machine (SVM). The data used in modeling were collected from the etching of silica thin films in inductively coupled plasma. For training and testing neural network, 9 and 6 experiments were used respectively. The performance of SVM was evaluated as a function of kernel type and function type. For the kernel type, Epsilon-SVR and Nu-SVR were included. For the function type, linear, polynomial, and radial basis function (RBF) were included. The performance of SVM was optimized first in terms of kernel type, then as a function of function type. Five film characteristics were modeled by using SVM and the optimized models were compared to statistical regression models. The comparison revealed that statistical regression models yielded better predictions than SVM.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.36-39
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• 2006

This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurement data. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of PMU for monitoring power system dynamics. The methodology for fast monitoring of the system is initiated locally which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper briefly describes the mathematical background of SVM, and explains the procedure for fast classification of voltage stability using the SVM algorithm. To illustrate the effectiveness of the classifier, this paper includes numerical examples with a 11-bus test system.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.19-43
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• 2011

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.67-74
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• 2003

Median regression analysis has robustness properties which make it an attractive alternative to regression based on the mean. Support vector machine (SVM) is used widely in real-world regression tasks. In this paper, we propose a new SV median regression based on check function. And we illustrate how this proposed SVM performs and compare this with the SVM based on absolute deviation loss function.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.655-662
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• 2012

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.216-221
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• 2010

In this paper, we propose speaker verification method using Support Vector Machine (SVM) kernel with Gaussian Mixture Model (GMM)-supervector based on the Mahalanobis distance. The proposed GMM-supervector SVM kernel method is combined GMM with SVM. The GMM-supervectors are generated by GMM parameters of speaker and other speaker utterances. A speaker verification threshold of GMM-supervectors is decided by SVM kernel based on Mahalanobis distance to improve speaker verification accuracy. The experimental results for text-independent speaker verification using 20 speakers demonstrates the performance of the proposed method compared to GMM, SVM, GMM-supervector SVM kernel based on Kullback-Leibler (KL) divergence, and GMM-supervector SVM kernel based on Bhattacharyya distance.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.118-126
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• 2006

There is a greater importance for underground work designed and built in the urban areas when it comes to considering the cost-effectiveness and the period of construction commensurate with an increasing trend of skyscrapers. At this stage of underground work, it's extremely necessary to choose a proper earth retaining method. Therefore, the study has suggested the rational retaining wall method by developing the support vector machine(SVM) model as a tool to choose a proper retaining wall method applied at the stage of selecting the earth retaining method. In order to develop the SVM model, the binary SVM classifier is expanded into a multi-class classifier. and to present the feasibility of our SVM model, we considered 129 projects. Applying the 'SVM Model' developed in the study to the designing and developing stages of the earth retaining work will contribute to the successful outcomes by decreasing any changes of design from implementing the earth retaining.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.43-46
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• 2003

In this paper, a novel rotor flux estimation method of an induction motor using support vector machine(SVM) is presented. Two veil-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. The theory of the SVM algorithm is based on statistical teaming theory. Training of SVH leads to a quadratic programming(QP) problem. The proposed SVM rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of Proposed algorithm are throughly verified through numerical simulation.