• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• Journal of Biosystems Engineering
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• v.36 no.6
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• pp.461-466
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• 2011

The purpose of this study is basically for the use of simulations to enhance productivity. In this paper, the optimal number of allocation in a small and medium industry which produces eleutherococcus senticosus sap, is performed using simulations. The simulation model was developed under considerations of production layout, process & operation, process time, total work time, work in process (WIP), utilization, failure rate, and operation efficient as inputs, and was validated with careful comparisons between real behaviors and outputs of the production line. Therefore, we can evaluate effects and changes in productivity when some strategies and/or crucial factors are changed. Although too many workers and machines could decrease productivity, the eleutherococcus senticosus sap production line in this paper has been maintained many machines. To solve this problem, we determined the optimal number of workers and machines that could not cause any interrupt in productions using simulations. This simulation model considers diverse input variables which could influence productivity, and it is very useful not only for the production line of Eleutherococcus Senticosus Sap, but also for other production lines with various purposes, especially, in the small and medium industries.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.367-381
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• 2011

Reducing waste for the efficiency of production is becoming more important because of the rapidly changing market circumstances and the rising material and oil prices. The dispatching also has to consider the characteristic of production circumstance for the efficiency. The production circumstance has the non-identical parallel machines with rework rate since machines have different capabilities and deterioration levels in the real manufacturing field. This paper proposes a dispatching method, FTLR (Flow Time Loss Index with Rework Rate) for production efficiency. The goal of FTLR is to minimize flow time based on such production environments. FTLR predicts the flow time with rework rate. After assessing dominant position of expected flow time per each machine, FTLR performs dispatching to minimize flow time. Experiments compare various dispatch methods for evaluating FTLR with mean flow time, mean tardiness and max tardiness in queue.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.84-92
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• 2016

Since the mankind started its space mission, the number of artificial space objects has been increasing exponentially. It contains not just the space machines which are in use but the machines which are out of order. Meantime, those dead machines are being a serious danger, a real threat to human's lives and property because of it could re-enter into the earth's atmosphere and fall to the ground causing mega-disaster. As the number of space activities gets growing so far, the re-entry of the space objects will be a lot more happened in the future. Therefore, not just natural space object like asteroids but the artificial space object like artificial satellite and space station can cause the disaster by falling to the ground. To protect our nation and our property, the government has set up the space disaster management center in Korea astronomy and Space science Institute. In this study, we surveyed public's recognition of the space object's re-entry situation and analyzed it to contribute building national space disaster management policy.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.703-705
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• 2004

Conventional support vector machines (SVMs) find optimal hyperplanes that have maximal margins by treating all data equivalently. In the real world, however, the data within a data set may differ in degree of uncertainty or importance due to noise, inaccuracies or missing values in the data. Hence, if all data are treated as equivalent, without considering such differences, the optimal hyperplanes identified are likely to be less optimal. In this paper, to more accurately identify the optimal hyperplane in a given uncertain data set, we propose a membership-induced distance from a hyperplane using membership values, and formulate three kinds of membership-induced SVMs.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.110-115
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• 2014

The reconfigurability of FPGA devices allows designers to evaluate, test and validate a new control algorithm; a new component or prototypes without damaged the real system with the so-called hardware co-simulation. The present paper uses the Xilinx System Generator (XSG) environment to establish and validate a new nonlinear estimator for the rotor time constant inverse that will be exploited to improve the indirect rotor field control of induction motor.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.9-12
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• 2001

Learning vector quantization (LVQ) is a supervised learning technique that uses class information to move the vector quantizer slightly, so as to improve the quality of the classifier decision regions. In this paper we propose a selection method of initial codebook vectors for a teaming vector quantization (LVQ3) using support vector machines (SVM). The method is experimented with artificial and real design data sets and compared with conventional methods of the condensed nearest neighbor (CNN) and its modifications (mCNN). From the experiments, it is discovered that the proposed method produces higher performance than the conventional ones and then it could be used efficiently for designing nonparametric classifiers.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.69-75
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• 2000

At the stage of initial design, prototypes are needed for engineering and aesthetic purposes. In order to get a fast and non-expensive prototype, designers prefer rapid prototyping(RP) to any other means. In driving a 3D CAD model into rapid prototyping, sectioning the model is essential and there are two negotiation-needed targets, enhancing accuracy while taking less build-time, which makes adaptive slicing taken into account. In spite of the advantages of adaptive slicing, it is not yet applied to real RP machines because of the limits of hardwares. In this thesis, a new slicing algorithm which (1)uses several values of thickness available in a RP machine. (2)determines total number of layers to make the prototype within the intended time and (3)arranges the layers using orthogonal arrays to minimize the volume error caused by the difference between a given CAD model and a fabricated model is presented. And the algorithm is expected to have possibility of assisting RP machines to take the advantages of adaptive slicing.