• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.461-467
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• 2011

Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.11-20
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• 2015

Typically welded bellows are checked with human eye and microscope, and then go through leakage test of gas. The proposed system alternates these heuristic techniques using support vector machines. Image procedures in the proposed method can cover the irregularity problem induced from human being. To get easy observation through microscope, 3D display system is also exploited. Experimental results from this automatic measurement show the welding detection is done within one tenth of permitted error range.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.112-120
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• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.749-756
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• 2012

An approach widely used for small area estimation is based on linear mixed models. However, when the functional form of the relationship between the response and the input variables is not linear, it may lead to biased estimators of the small area parameters. In this paper we propose M-quantile kernel regression for small area mean estimation allowing nonlinearities in the relationship between the response and the input variables. Numerical studies are presented that show the sample properties of the proposed estimation method.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.314-317
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• 2003

기존의 침입탐지 시스템에서는 오용탐지모델이 널리 사용되고 있다. 이 모델은 낮은 오판율(False Alarm rates)을 가지고 있으나, 새로운 공격에 대해 전문가시스템(Expert Systems)에 의한 규칙추가를 필요로 한다. 그리고 그 규칙과 완전히 일치되는 시그너처만 공격으로 탐지하므로 변형된 공격을 탐지하지 못한다는 문제점을 가지고 있다 본 논문에서는 이러한 문제점을 보완하기 위해 주성분분석(Principle Component Analysis; 이하 PCA)과 서포트 벡터 머신(Support Vector Machines; 이하 SVM)을 이용한 침입탐지 시스템을 제안한다. 네트워크 상의 패킷은 PCA를 이용하여 결정된 주성분 공간에서 해석되고, 정상적인 흐름과 비정상적인 흐름에 대한 패킷이미지패턴으로 정규화 된다. 이러한 두 가지 클래스에 대한 SVM 분류기를 구현한다. 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.68-73
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• 2021

In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.192-192
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• 2020

취약성 분석의 결과로 폭설에 의한 기후노출은 현재에는 강원권이 가장 취약한 것으로 나타났다. 그러나 미래에는 강원권, 충청권, 호남권을 연결하는 축으로 취약지역이 확대될 것으로 전망된다. 본 연구에서는 다양한 머신러닝 기법을 이용하여 대설피해 예측을 실시하였다. 머신러닝 기법으로는 로지스틱회귀모형, 서포트벡터 머신, 의사결정트리 모형을 적용하였다. 종속변수로 대설피해액 자료를 이용하였고, 독립변수로 기상관측자료, 사회·경제적 요소를 사용하였다. 결과적으로 기존에 사용했던 다중회귀모형과 머신러닝 기법으로 예측한 예측력을 비교 및 분석하였고, 예측력이 가장 높은 머신러닝 기법을 제시하였다. 본 연구에서 대설피해 예측을 위해 사용된 예측력이 가장 높은 기법을 활용하여 대설피해를 예측한다면, 미래에 전국적으로 확대될 대설피해에 대해 효과적으로 대비할 수 있을 것으로 기대된다.