• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.129.1-129.1
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• 2012

서포트 벡터 머신(Support Vector Machine, SVM)과 인공신경망 모형(Neural Network, NN)을 사용하여 태양 양성자 현상(Solar proton event, SPE)의 플럭스 세기를 예측해 보았다. 이번 연구에서는 1976년부터 2011년까지 10MeV이상의 에너지를 가진 입자가 10개 cm-1 sec-1 ster -1 이상 입사할 경우를 태양 양성자 현상으로 정의한 NOAA의 태양 고에너지 입자 리스트와 GOE위성의 X-ray 플레어 데이터를 사용하였다. 여기에서 C, M, X 등급의 플레어와 관련있는 178개 이벤트를 모델의 훈련을 위한 데이터(training data) 89개와 예측을 위한 데이터(prediction data) 89개로 구분하였다. 플러스 세기의 예측을 위하여, 우리는 로그 플레어 세기, 플레어 발생위치, Rise time(플레어 시작시간부터 최대값까지의 시간)을 모델 입력인자로 사용하였다. 그 결과 예측된 로그 플럭스 세기와 관측된 로그 플럭스 세기 사이의 상관계수는 SVM과 NN에서 각각 0.32와 0.39의 값을 얻었다. 또한 두 값 사이의 평균 제곱근 오차(Root mean square error)는 SVM에서 1.17, NN에서는 0.82로 나왔다. 예측된 플럭스 세기와 관측된 플럭스 세기의 차이를 계산해 본 결과, 오차 범위가 1이하인 경우가 SVM에서는 약 68%이고 NN에서는 약 80%의 분포를 보였다. 이러한 결과로부터 우리는 NN모델이 SVM모델보다 플럭스 세기를 잘 예측하는 것을 알 수 있었다.

• Journal of IKEEE
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• v.24 no.4
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• pp.1022-1027
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• 2020

In this paper, SVM-based speaker classification is experimented with GMM-supervector. To create a speaker cluster, conventional speaker change detection is performed with the KL distance using the SNR-based weighting function. SVM-based speaker classification consists of two steps. In the first step, SVM-based classification between UBM and speaker models is performed, speaker information is indexed in each cluster, and then grouped by speaker. In the second step, the SVM-based classification between UBM and speaker models is performed by inputting the speaker cluster group. Linear and RBF are applied as kernel functions for SVM-based classification. As a result, in the first step, the case of applying the linear kernel showed better performance than RBF with 148 speaker clusters, MDR 0, FAR 47.3, and ER 50.7. The second step experiment result also showed the best performance with 109 speaker clusters, MDR 1.3, FAR 28.4, and ER 32.1 when the linear kernel was applied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.21-28
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• 2012

Predication of protein interaction sites for monomer structures can reduce the search space for protein docking and has been regarded as very significant for predicting unknown functions of proteins from their interacting proteins whose functions are known. In the other hand, the prediction of interaction sites has been limited in crystallizing weakly interacting complexes which are transient and do not form the complexes stable enough for obtaining experimental structures by crystallization or even NMR for the most important protein-protein interactions. This work reports the calculation of 3D surface patches of complex structures and their properties and a machine learning approach to build a predictive model for the 3D surface patches in interaction and non-interaction sites using support vector machine. To overcome classification problems for class imbalanced data, we employed an under-sampling technique. 9 properties of the patches were calculated from amino acid compositions and secondary structure elements. With 10 fold cross validation, the predictive model built from SVM achieved an accuracy of 92.7% for classification of 3D patches in interaction and non-interaction sites from 147 complexes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.181-183
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• 2003

최근 대용량의 데이터베이스로부터 유용한 정보를 발견하고 데이터간에 존재하는 연관성을 탐색하고 분석하는 데이터 마이닝에 관한 많은 연구들이 진행되고 있다. 다양한 생체 신호를 분석하기 위하여 데이터 마이닝 기법을 이용할 수 있다. 본 논문에서는 심전도 신호의 패턴을 분류하기 위하여 신경망 기법을 적용하였다. 최근 패턴분류에 있어서 각광을 받고 있는 SVM 모델은 학습과정에서 얻어진 확률분포를 이용하여 의사결정함수를 추정한 후 이 함수에 따라 새로운 데이터를 이원분류 하는 것으로 분류 문제에 있어서 일반화 기능이 매우 높다. 기존에 많이 이용되던 BP 모델과 비교평가 하였다.

• Annual Conference of KIPS
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• 2002.04a
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• pp.577-580
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• 2002

인터넷상의 정보의 급증에 따라 필요한 정보를 발견하고 관련된 정보를 조직화하기가 더욱 어려워지고 있으며 정보 접근의 부하를 줄이기 위한 효율적인 문서 분류의 중요성 및 필요성이 증가하고 있다. 본 논문에서는 디렉토리 내의 학습 문서 집합을 기반으로 구성된 디렉토리 내의 대표 용어 집합으로 구성된 모델을 학습 및 분류하기 위해 SVM을 사용한다. 문서분류를 위해 정보통신 웹 디렉토리 내의 문서로부터 추출된 용어 집합을 기반으로 학습을 수행한 후 문서 분류를 수행한다. 또한 TFiDF를 기반으로 특징을 표현하기 위해 벡터공간 모델을 사용하였고 이를 기반으로 성능 평가를 수행한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.227-230
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• 2007

본 논문에서는 로봇의 목표물 추적을 위하여 SVM을 이용한 12각형 기반의 Q-learning 알고리즘을 제안한다. 제안한 알고리즘의 유효성을 보이기 위해 본 논문에서는 두 대의 로봇과 장애물 그리고 하나의 목표물로 정하고, 각각의 로봇이 숨겨진 목표물을 찾아내는 실험을 가정하여 무작위, DBAM과 AMAB의 융합 모델, 마지막으로는 본 논문에서 제안한 SVM과 12각형 기반의 Q-learning 알고리즘을 이용하여 실험을 수행하고, 이 3가지 방법을 비교하여 본 논문의 유효성을 검증하였다.