• 한국지능시스템학회논문지
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• 제13권6호
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• pp.655-660
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• 2003

일반적으로 Support Vector Machine은 이진 분류 모형에 있어 우수한 성능을 보이지만 모델의 한계로 인하여 다중 패턴의 분류 문제에는 쉽게 적용하기가 어렵다. 본 논문에서는 이진 분류를 포함한 다중 레이블을 갖는 데이터의 정확한 패턴 분류를 위하여 Zhu가 제안한 Import Vector Machine에 커널 Bagging 전략을 적용하여 분류의 정확성을 향상시키기 위한 Import Vector Voting 모형을 제안한다. 이러한 Import Vector Voting 모형은 다수의 커널함수를 적용한 결과 중에서 가장 성능이 우수한 커널함수를 이용하여 최종 분류를 수행하기 위한 voting 전략으로 사용한다. 본 논문에서 제안하는 Import Vector Voting 모형은 이진 분류를 포함한 3개 이상의 다중 패턴 데이터에 대한 분류 문제에 있어 매우 정확한 분류 성능을 보임을 실험을 통해 입증한다.

• Journal of Power Electronics
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• 제17권5호
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.275-278
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• 2002

Vapnik이 제안한 Support Vector Machine은 두 개의 부류를 갖는 데이터에 대한 분류에는 매우 좋은 성능을 보인다는 점은 이미 잘 알려져 있다. 하지만 부류의 개수가 3개 이상인 다중 패턴을 갖는 데이터에 대한 분류에는 SVM을 적용하기가 쉽지 않다. Support Vector Machine의 이러한 문제점을 해결하기 위하여 Zhu는 3개 이상의 부류를 갖는 데이터의 패턴 분류를 위하여 Import Vector Machine을 제안하였다. 이 모형은 Support Vector Machine을 이용하여 해결하기 어려운 다중 패턴 분류를 가능케 한다. Import Vector Machine은 커널 로지스틱 기반의 함수만을 사용하지만 본 논문에서는 다수의 커널 함수를 적용하여 가장 성능이 우수한 커널 함수를 찾아내어 최종 분류를 수행하게되는 bagging 기법을 적용하였다 제안하는 방법이 기존의 방법에 비해, 더욱 정확한 분류를 수행함을 실험 결과를 통해 확인한다.

• 한국소음진동공학회논문집
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• 제14권12호
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• 한국통신학회논문지
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• 제34권12C호
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• pp.1177-1183
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• 2009

In this paper, support vector machines (SVM) are applied to multi-user detector (MUD) for direct sequence (DS)-CDMA system. This work shows an analytical performance of SVM based multi-user detector with some of kernel functions, such as linear, sigmoid, and Gaussian. The basic idea in SVM based training is to select the proper number of support vectors by maximizing the margin between two different classes. In simulation studies, the performance of SVM based MUD with different kernel functions is compared in terms of the number of selected support vectors, their corresponding decision boundary, and finally the bit error rate. It was found that controlling parameter, in SVM training have an effect, in some degree, to SVM based MUD with both sigmoid and Gaussian kernel. It is shown that SVM based MUD with Gaussian kernels outperforms those with other kernels.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2005년도 제25회 추계학술대회논문집
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• pp.158-161
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• 2005

본 연구에서는 Support Vector Machine (SVM)을 이용하여 가스 터빈 엔진의 결함 진단을 시도하였다. SVM은 벡터 공간에서 임의의 비선형 경계인 Hyperplane을 찾아 두 개의 집합을 분류하는 방법으로 수학적으로 최적의 해를 찾을 수 있다고 알려져 있다. 이러한 이진 분류용 SVM을 다층으로 결합하여 가스 터빈의 결함을 정량적으로 판단해 내는 방법을 제안하였으며 기존의 Multi Layer Perceptron(MLP)보다 빠르고 신뢰성 있는 진단 결과를 보여주었음을 확인하였다.

• 대한기계학회논문집A
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• 제34권11호
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• pp.1681-1689
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• 2010

Support Vector Machine(SVM)을 이용한 회전기계 진단 연구가 많이 수행되어 왔으나 결함 분류성능은 입력 특징과 더불어 다중 분류 방법, 이진분류기, 커널함수 등에 따라 다르다. SVM 을 이용한 대부분의 기존 연구들은 한번 입력 특징들을 선정하면 결함 분류시 동일한 특징데이터를 이용한다. 본 논문에서는 회전기계의 다양한 결함조건에서 측정한 진동신호로부터 추출한 통계적 특징들을 이용하여 각각의 결함을 분류하기 위한 최적 특징들을 선정한 후, 해당 결함상태를 분류하기 위한 SVM 학습과 분류에 각각 이용하였다. 실험자료를 이용한 검증 결과, 제안한 단계 분류 방법이 상대적으로 적은 학습시간으로 단일 다중 분류 방법과 유사한 분류 성능을 얻을 수 있었다.