• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.461-471
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• 2017

In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.91-99
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• 2019

An innovative tone modeling framework based on deep neural networks in tone recognition was proposed in this paper. In the framework, both the prosodic features and the articulatory features were firstly extracted as the raw input data. Then, a 5-layer-deep deep belief network was presented to obtain high-level tone features. Finally, support vector machine was trained to recognize tones. The 863-data corpus had been applied in experiments, and the results show that the proposed method helped improve the recognition accuracy significantly for all tone patterns. Meanwhile, the average tone recognition rate reached 83.03%, which is 8.61% higher than that of the original method.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.163-168
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• 2011

블로그는 인터넷 공간에서 가장 손쉽게 정보 출간, 토론 참여, 커뮤니티 형성하는 수단이다. 그러나 최근에 광고를 유치하거나 페이지 순위를 올리기 위한 목적의 다양한 스팸 블로그가 범람하고 있다. 본 연구의 목적은 웹 환경에서 이러한 스팸 블로그(Splog)를 자동으로 판별하는 시스템을 개발하는 것이다. 먼저 블로그의 HTML을 제거한 후 품사를 부착하였다. 어휘/품사 쌍을 자질로 사용하였으며 카이제곱 통계량을 이용하여 유용한 자질을 선택하였다. 선택된 자질의 가중치를 벡터로 표현한 후, 지지벡터기계(Support Vector Machines)를 학습하여 자동으로 스팸 블로그를 판별하는 시스템을 제안하였으며, SPLOG 데이터 집합으로 실험한 결과 F1척도로 90.5%의 정확률을 얻었다.

• Structural Engineering and Mechanics
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• 제40권5호
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• pp.719-743
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• 2011

A hybrid damage monitoring scheme using parallel acceleration-impedance approaches is proposed to detect girder damage and support damage in steel plate-girder bridges which are under ambient train-induced excitations. The hybrid scheme consists of three phases: global and local damage monitoring in parallel manner, damage occurrence alarming and local damage identification, and detailed damage estimation. In the first phase, damage occurrence in a structure is globally monitored by changes in vibration features and, at the same moment, damage occurrence in local critical members is monitored by changes in impedance features. In the second phase, the occurrence of damage is alarmed and the type of damage is locally identified by recognizing patterns of vibration and impedance features. In the final phase, the location and severity of the locally identified damage are estimated by using modal strain energy-based damage index methods. The feasibility of the proposed scheme is evaluated on a steel plate-girder bridge model which was experimentally tested under model train-induced excitations. Acceleration responses and electro-mechanical impedance signatures were measured for several damage scenarios of girder damage and support damage.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.877-878
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• 2008

We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.