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

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

• Geomechanics and Engineering
• /
• 제34권3호
• /
• pp.267-284
• /
• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• 정보처리학회논문지B
• /
• 제13B권4호
• /
• pp.439-448
• /
• 2006

Kohonen SOM(Self-Organizing Map)이나 MLP(Multi-Layer Perceptron), SVM(Support Vector Machine)과 같은 기존의 인식 및 클러스터링 알고리즘들은 새로운 입력 패턴에 대한 적응성이 떨어지고 학습 패턴 자체의 복잡도에 대한 학습률의 의존도가 크게 나타나는 등 여러 가지 단점이 있다. 이러한 학습 알고리즘의 단점은 문제의 학습 패턴자체의 특성을 잃지 않고 문제의 복잡도를 낮출 수 있다면 보완할 수 있다. 패턴 자체의 특성을 유지하며 복잡도를 낮추는 방법론은 여러 가지가 있으며, 본 논문에서는 커널 공간 해석 기법을 접근 방법으로 한다. 본 논문에서 제안하는 kSOM(kernel based SOM)은 원 공간의 데이터가 갖는 복잡도를 무한대에 가까운 초 고차원의 공간으로 대응시킴으로써 데이터의 분포가 원 공간의 분포에 비해 상대적으로 성긴(spase) 구조적 특정을 지니게 하여 클러스터링 및 인식률의 상승을 보장하는 메커니즘 을 제안한다. 클러스터링 및 인식률의 산출은 본 논문에서 제안한 새로운 유사성 탐색 및 갱신 기법에 근거하여 수행한다. CEDAR DB를 이용한 필기체 문자 클러스터링 및 인식 실험을 통해 기존의 SOM과 본 논문에서 제안한 kSOM과 성능을 비교한다.

• 한국컴퓨터정보학회논문지
• /
• 제18권12호
• /
• pp.11-19
• /
• 2013

본 논문에서는 유도 전동기의 기계적 결함을 진단하기 위해 진동신호와 질감 분석을 이용한 알고리즘을 제안한다. 영상화된 결함 신호가 갖는 무늬, 색상 대비의 특징을 분석하고, 그레이레벨 동시발생행렬(Gray-Level Co-occurrence Model, GLCM)을통해 세 가지 질감특징을추출한다. 추출된 세 가지질감 특징을 RBF(Radial Basis Function) 커널 함수를 사용하는 다중레벨 서포터 벡터 머신(Multi-Level Support Vector Machine, MLSVM)의 입력으로 사용하여 결함 유형을 분류한다. 결함 유형을 분류하는 최적의 MLSVM을 위한 RBF 커널 함수의 매개변수를 찾기 위해 매개변수 값을 0.3부터 1.0으로 바꿔가며 분류성능을 평가한 결과, 결함 유형별로 0.3에서 0.6사이의 매개변수 값에서 100%에 가까운 분류 정확성을 보였다. 또한 15dB, 20dB의 잡음이 첨가된 진동신호를 이용한 실험에서도 평균 98%이상의 높은 분류 정확성을 보였다.

• 한국건축시공학회지
• /
• 제11권3호
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제32권12호
• /
• pp.1260-1270
• /
• 2005

최근 기계학습 분야에서 커널머신을 이용한 대표적 학습기로 Support Vector Machines (SVM)이 주목 받고 있다. SVM은 통계적 학습이론에 기반하여 뛰어난 일반화 성능을 보여주며, 다양한 패턴인식 문제에 적용되고 있다. 그러나. SVM은 이진 분류기이므로 일반적인 다중 클래스 문제에 곧바로 적용할 수 없다. SVM을 다중 클래스 문제의 하나인 얼굴인식에 도입하기 위한 방법으로는, One-Per-Class와 All-Pairs가 대표적이다. 상기 두 방법은 다중 클래스 문제를 여러 개의 이진 클래스 문제로 분할하고, 이들을 다시 종합하여 최종 결정을 내리는 출력코딩이라는 일반적인 방법에 속한다. 본 논문에서는 이진 분류기인 SVM의 다중 클래스 분류기 확장 방안으로 출력코딩 방법론을 설명한다. 또한 출력코딩 방법론의 대표적인 이론적 기반인 ECOC(Ewor-Correcting Output Codes)를 근간으로 하는 새로운 출력코딩 방법들을 제안하고, 얼굴인식 실험을 통해 SVM을 기반 분류기로 사용할 경우의, 출력코딩 방법의 특성을 비교$\cdot$분석한다.

• 제어로봇시스템학회논문지
• /
• 제19권4호
• /
• pp.364-371
• /
• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권3호
• /
• pp.941-953
• /
• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권4호
• /
• pp.1140-1152
• /
• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.