• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.459-461
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• 2006

저궤도 인공위성 전력계 시스템의 설계 및 해석을 위한 태양 전지 전력조절기의 대신호 안정도해석을 수행한다. 태양전지 전력조절기에서 제어가능한 모든 방법에 따른 태양전지에서 바라본 태양전지 전력조절기의 부하특성을 분류하고, 상태공간해석을 이용하여 태양전지 시스템의 대신호적 안정도를 해석한다. 또한, 본 논문에서는 태양전지 전력조절기의 부하특성을 정전력부하에서 정저항부하로 변환하여 대신호적인 안정도를 확보하는 비선형변환을 제안한다. 제안된 변환기법을 통해 최대전력점 추적제어나 배터리 충전제어 및 전류분배제어가 가능한 병렬 모듈 태양전지 레귤레이터에 적합한 단일 전류 제어기를 구성한다. 제안된 대신호 해석과 저항제어를 검증하기위해, 200W급 태양전지와 100W급 태양전지 전력조절기 두 모듈을 병렬로 구성하여 실험하였다.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.593-601
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• 2022

We call "fruad" the cases that are not frequently occurring but cause significant losses. Fraud detection is commonly encountered in various applications, including wafer production in the semiconductor industry. It is not trivial to directly extend the standard binary classification methods to the fraud detection context because the misclassification cost is much higher than the normal class. In this article, we propose the functional fraud detection support vector machine (F²DSVM) that extends the fraud detection support vector machine (FDSVM) to handle functional covariates. The proposed method seeks a classifier for a function predictor that achieves optimal performance while achieving the desired sensitivity level. F²DSVM, like the conventional SVM, has piece-wise linear solution paths, allowing us to develop an efficient algorithm to recover entire solution paths, resulting in significantly improved computational efficiency. Finally, we apply the proposed F²DSVM to the defective wafer detection problem and assess its potential applicability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2968-2974
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• 2015

Method for finding the attractors is the important object to investigate in the linear/additive CA because it is a primary interest in applications like pattern recognition, pattern classification, design of associative memory and query processing etc. But the research has been so far mostly concentrated around linear/additive CA and it is not enough to modelize the complex real life problem. So nonlinear CA is demanded to devise effective models of the problem and solutions around CA model. In this paper we introduce CLT as an upgraded version of RMT and provide the process for finding the attractors and nonreachable states effectively through the CLT.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.93-98
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• 2006

This paper deals with the estimation of unknown actuator faults for linear dynamic systems with sensor noise. The presented method based on the integral observer permits to achieve good convergence and exact estimation of unknown faults. The validity of proposed method is established by using the simulation results which compare to the existing methods.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.37-45
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• 2013

Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.61-70
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• 2004

It is difficult to find an appropriate web site because exponentially growing web contains millions of web documents. Personalization of web search can be realized by recommending proper web sites using user profile but more efficient method is needed for estimating preference because user's evaluation on web contents presents many aspects of his characteristics. As user profile has a property of non-linearity, estimation by classifier is needed and combination of classifiers is necessary to anticipate diverse properties. Structure adaptive self-organizing map (SASOM) that is suitable for Pattern classification and visualization is an enhanced model of SOM and might be useful for web mining. Fuzzy integral is a combination method using classifiers' relevance that is defined subjectively. In this paper, estimation of user profile is conducted by using ensemble of SASOM's teamed independently based on fuzzy integral and evaluated by Syskill ＆ Webert UCI benchmark data. Experimental results show that the proposed method performs better than previous naive Bayes classifier as well as voting of SASOM's.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1998.04a
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• pp.5-5
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• 1998

가스터빈 엔진은 민간 항공분야와 방위 무기의 발달과 더불어 성능이 향상되어 왔고, 그 역할도 증대하였다. 성능 향상과 역할 증대에 따라 처리하여야 할 일의 양과 그 속도가 증가하게 되면서 엔진 제어 난이도도 증가하고 제어기법도 향상되고 있다. 이에, 전자공학의 발달에 힘입어 전자식 엔진 제어기가 엔진제어의 임무를 수행하게 되었고, 근래에는 기체의 무게감소와 신뢰성 향상이라는 이중 이익을 위해 FADEC(Full Authority Digital Engine Controller)엔진 제어기까지 등장, 사용되고 있다. 가스터빈 엔진의 제어는 일반적으로 비선형 시스템에 관한 모델링 단계와 성능 해석결과를 이용한 보상기 설계 및 제어 단계의 3부분으로 크게 분류된다. FADEC이란 개념이 정착되기 이전에는 통상적인 제어 법칙인 PID(Proportional Integral Derivative) 방법이 사용되었으나, 시스템의 복잡화와 다변화에 의하여 modern control 개념이 고려된 새로운 제어 방법이 사용되기 시작하였다. 본 논문에서는 엔진 제어에 실제적으로 이용할 수 있는 제안된 제어 법칙을 이용하여 실제 엔진 모델에 적용하여 시뮬레이션 함으로써 새로운 제어 법칙이 엔진 제어에 적용 가능함을 보이고자 한다.

• Journal of Korean Society of Steel Construction
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• v.21 no.6
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• pp.563-574
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• 2009

This paper briefly describes the fundamental strategies--path-tracing, pin-pointing, and path-switching--in the computational elastic bifurcation theory of geometrically non-linear single-load-parameter conservative elastic spatial structures. The stability points in the non-linear elasticity may be classified into limit points and bifurcation points. For the limit points, the path tracing scheme that successively computes the regular equilibrium points on the equilibrium path, and the pinpointing scheme that precisely locates the singular equilibrium points were sufficient for the computational stability analysis. For the bifurcation points, however, a specific procedure for path-switching was also necessary to detect the branching paths to be traced in the post-buckling region. After the introduction, a general theory of elastic stability based on the energy concept was given. Then path tracing, an indirect method of detecting multiple bifurcation points, and path switching strategies were described. Next, some numerical examples of bifurcation analysis were carried out for a trussed stardome, and a pin-supported plane circular arch was described. Finally, concluding remarks were given.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.380-387
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• 2010

For the fault diagnosis of three-phase induction motors, we propose a diagnosis algorithm based on mutual information and linear discriminant analysis (LDA). The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by mutual information As the next step, feature extraction is performed by LDA, and then diagnosis is evaluated by k-NN classifier. The results to verify the usability of the proposed algorithm showed better performance than various conventional methods.