• 품질경영학회지
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• 제38권3호
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• pp.354-362
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• 2010

This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.199-200
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-Gateway System)에서 사용되는 수 천 개 이상의 센서에서 데이터 전송을 받는 각 게이트웨이의 데이터 처리 속도를 향상시키고 작업 오류를 줄이기 위하여 SVM(Support Vector Machine) 알고리즘을 사용한 센서 데이터 할당 기법을 제안한다. 각 센서의 반복 측정 간격과 중요도에 따라 작업부하(Workload)를 구하고, 이를 순차 반복 비교를 통해 Sub-task 값을 구한다. 이렇게 구해진 Sub-task값을 기준으로 각 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• 대한안전경영과학회지
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• 제7권1호
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• pp.199-210
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• 2005

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

• Journal of Power Electronics
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• 제11권6호
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• pp.846-855
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• 2011

This paper proposes a control mode switching scheme between vector control and constant V/f control for induction machine (IM) drives for maximum torque utilization in a higher speed region. For the constant V/f scheme, a smooth transition method from the linear range of PWM up to the six-step mode is applied, by which the machine flux and torque can be kept constant in a high-speed range. Also, a careful consideration of the initial phase angle of the voltage in the transient state of the control mode change between the vector control and V/f schemes is described. The validity of the proposed strategy is verified by the experiment result for a 3-kW induction motor drives.

• 교육녹색환경연구
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• 제11권3호
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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.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.441-450
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• 2008

In this paper we propose a new method for solving multiclass problem with least squares support vector machine(LS-SVM) regression. This method implements one-against-all scheme which is as accurate as any other approach. We also propose cross validation(CV) method to select effectively the optimal values of hyper-parameters which affect the performance of the proposed multiclass method. Experimental results are then presented which indicate the performance of the proposed multiclass method.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.235-246
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• 2024

In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.

• Structural Engineering and Mechanics
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• 제83권1호
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• pp.79-92
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• 2022

In this paper, the impact of a vernacular pozzolanic kaolinite mine on concrete alkali-silica reaction and strength has been evaluated. For making the samples, kaolinite powder with various levels has been used in the quality specification test of aggregates based on the ASTM C1260 standard in order to investigate the effect of kaolinite particles on reducing the reaction of the mortar bars. The compressive strength, X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) experiments have been performed on concrete specimens. The obtained results show that addition of kaolinite powder to concrete will cause a pozzolanic reaction and decrease the permeability of concrete samples comparing to the reference concrete specimen. Further, various machine learning methods have been used to predict ASR-induced expansion per different amounts of kaolinite. In the process of modeling methods, optimal method is considered to have the lowest mean square error (MSE) simultaneous to having the highest correlation coefficient (R). Therefore, to evaluate the efficiency of the proposed model, the results of the support vector machine (SVM) method were compared with the decision tree method, regression analysis and neural network algorithm. The results of comparison of forecasting tools showed that support vector machines have outperformed the results of other methods. Therefore, the support vector machine method can be mentioned as an effective approach to predict ASR-induced expansion.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.405-407
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• 2009

SVM(Support Vector Machine) is a classification method which is recently watched in mechanical learning system. Vapnik, Osuna, Platt etc. had suggested methodology in order to solve needed QP(Quadratic Programming) to realize SVM so that have extended application field. SVM find hyperplane which classify into 2 class by converting from input space converter vector to characteristic space vector using Kernel Function. This is very systematic and theoretical more than neural network which is experiential study method. Although SVM has superior generalization characteristic, it depends on Kernel Function. There are three category in the Kernel Function as Polynomial Kernel, RBF(Radial Basis Function) Kernel, Sigmoid Kernel. This paper has analyzed performance of SVM against kernel using virtual data.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.79-86
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• 2006

영상 식별 기술은 대용량의 멀티미디어 데이터베이스 환경 하에서 고속의 검색을 위해서 필수적이다. 본 논문은 이러한 고속 검색을 위하여 GA(Genetic Algorithm)과 SVM(Support Vector Machine)을 결합한 모델을 제안한다. 특징벡터로는 색상 정보와 질감 정보를 사용하였다. 이렇게 추출된 특징벡터의 집합을 제안한 모델을 통해 최적의 유효 특징벡터의 집합를 찾아 영상을 식별하여 정확도를 높였다. 성능평가는 색상, 질감. 색상과 질감의 연합 특징벡터를 각각 사용한 성능 비교. SYM과 제안된 알고리즘과의 성능을 비교하였다. 실험 결과 색상과 질감을 연합한 특징벡터를 사용한 것이 단일 특징벡터를 사용한 것 보다 좋은 결과를 보였으며 하이브리드 기법을 이용한 제안된 알고리즘이 SVM알고리즘만을 이용한 것 보다 좋은 결과를 보였다.