• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.

• 한국정보통신학회논문지
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• 제25권4호
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• pp.565-571
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• 2021

본 논문은 레이더 시스템에 사용되는 상용칩의 주파수 선택적 IQ 불일치를 보상하는 기법을 제안하고, 성능 열화로 인하여 고성능 레이더 시스템에 적용이 어려웠던 상용칩의 사용이, 제안된 기법을 통하여 가능함을 성능 분석을 통하여 보였다. IQ 불일치 보상 성능의 극대화를 위하여 본 논문에서는 특잇값 분해를 통한 차원 축소 기법을 제안하고, 제안된 차원 축소 기법에 기반한 IQ 불일치 복소 보상 여파기의 설계를 위한 최적화 모델을 제안하였다. 제안된 보상 기법의 우수성을 입증하기 위하여 실제 상용칩에 기반한 IQ 불일치 측정 및 보상 시스템을 FPGA로 구현하였으며, 개발된 시스템을 통하여 논문에서 제안하는 방법의 성능을 검증하였다. 성능 검증 결과, 기존 방법과 비교하여 본 논문에서 제안하는 방법이 큰 복잡도 증가 없이 기존 방법의 성능을 뛰어넘는 우수한 성능을 보임을 확인하였다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.921-926
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• 2007

A dynamic system under random disturbance is considered in the study. In order to control the system efficiently, proper reduction of system dimension is indispensible in design stage. The reduction method using component cost analysis in conjunction with stochastic analysis is proposed for the control of a system. System response is obtained in terms of dynamic moment equation via Fokker-Plank-Kolmogorov(F-P-K) equation. The dynamic moment response of the system under random disturbance are reduced by using of deterministic version of component cost analysis. The reduced system via proposed "stochastic component cost analysis" is successfully implemented for dynamic response and shows remarkable control performance effectively utilizing "stochastic controller" in physical time domain.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.877-878
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• 2008

We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.

• 동력기계공학회지
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• 제19권3호
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• pp.55-62
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• 2015

This paper discusses a new model for investigating the micro-mechanical behavior of beam-like structures composed of various elastic moduli and complex geometries varying through the cross-sectional directions and also periodically-repeated along the axial directions. The original three-dimensional problem is first formulated in an unified and compact intrinsic form using the concept of decomposition of the rotation tensor. Taking advantage of two smallness of the cross-sectional dimension-to-length parameter and the micro-to-macro heterogeneity and performing homogenization along dimensional reduction simultaneously, the variational asymptotic method is used to rigorously construct an effective zeroth-order beam model, which is similar a generalized Timoshenko one (the first-order shear deformation model) capable of capturing the transverse shear deformations, but still carries out the zeroth-order approximation which can maximize simplicity and promote efficiency. Two examples available in literature are used to demonstrate the consistence and efficiency of this new model, especially for the structures, in which the effects of transverse shear deformations are significant.

• Journal of Information Technology Applications and Management
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• 제16권1호
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• pp.97-113
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• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2485-2492
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• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.

• 대한전자공학회논문지SP
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• 제44권2호
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• pp.55-62
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• 2007

본 논문에서는 카메라 자동 교정을 통한 3차원 재구성 과정에서 생기는 오차로 인해 포함되는 잡음을 특성에 따라 효과적으로 제거하여 정교한 3차원 데이터를 얻기 위한 방법을 제안한다. 기존의 잡음 평활화 과정은 잡음 때문에 면적이 큰 메쉬는 3차원으로 재구성하는데 문제점이 존재한다. 제안한 알고리즘은 메쉬의 면적이 중요하기 때문에 취득된 3차원 데이터는 불필요한 삼각형 메쉬들을 사전에 제거하는 전처리 과정이 필요하다. 본 연구는 3차원 메쉬의 면적 정보를 이용하여 잡음의 특성을 분석하고, 그 특성에 따라 피크 잡음과 가우스 잡음을 분리하여 효과적으로 잡음을 제거한다. 본 알고리즘의 성능은 재구성 데이터에 대한 정량적인 비교 분석을 통해 기존의 메쉬 평활화 방법보다 더 정교한 3차원 데이터를 얻음을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• 응용통계연구
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• 제8권1호
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• pp.61-74
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• 1995

오늘날에는 컴퓨터를 이용한 다양한 그래프기법의 개발로 자료로부터 정보를 직접적으로 얻는 것이 용이하다. 특히 최근에 발표된 R-코드(Cook과 Weisberg, 1994)는 다양한 2차원, 3차원 플롯 뿐만 아니라 축의 회전과 여러가지 모형에 대한 적합성을 제시하므로 보다 쉽게 자료에 적합한 모형을 시각적으로 분석할 수 있게 하였다. 그러나 그래프는 3차원 이상의 공간을 표현할 수 없기 때문에 하나의 반응변수와 세개이상의 설명변수 사이의 관계를 직접적으로 표현하는 것이 불가능하다. 이와 관련하여 Li(1991, 1992)에 의하여 제시된 SIR, pHd 방법과 Cook과 Weisberg(1991)에 의하여 제시된 SAVE는 설명변수들의 선형결합을 이용하여 효과적으로 설명변수들의 차원을 줄이는 방법을 제시하였다. 본 연구에서는 Li에 의하여 제시된 pHd 방법을 반응변수가 2개이상인 다변량 반응변수 모형에 적용하는 방법을 연구하였다. pHd 방법의 적용에는 많은 계산과정이 요구되는데, 이러한 계산과 다양한 플롯은 R-코드를 이용하였다.