• 한국전자파학회논문지
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• 제19권3호
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• pp.352-357
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• 2008

페이딩 채널에서 공간-시간 트렐리스 부호화된 OFDM 시스템을 제안하고, 시간 영역에서 제안된 principal ratio combining(PRC)와 일반화된 PRC(GPRC)의 수식을 이론적으로 주파수 영역에서 새롭게 분석한다. 시간 영역에서와 마찬가지로 GPRC 기법은 ML 및 PRC 기법을 분할하여 성능과 수신기 복잡도 간의 유연한 tradeoff를 활용할 수 있다. M진 PSK 변조 방식을 이용하는 컴퓨터 시뮬레이션을 통해 주파수 플랫 및 주파수 선택적 페이딩에서 성능을 검증한다. 또한, 세 가지 수신 기법간의 수신기 복잡도와 시뮬레이션 시간을 비교 검토한다.

• 천문학회지
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• 제40권3호
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• pp.61-65
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• 2007

The 2-1 and 5-4 transitions of SiO have been observed toward the Sgr B2 region, including the Principal Cloud(the GMC containing Sgr B2(M)) and its surroundings. The morphology and velocity structure of the SiO emission show a close resemblance with the HNCO Ring feature, identified by Minh & Irvine(2006), of about 10 pc in diameter, which may be expanding and colliding with the Principal Cloud. Three SiO clumps have been found around the Ring, with total column densities $N_{SiO}{\sim}1{\times}10^{14}cm^{-2}$ at the peak positions of these clumps. The fractional SiO abundance relative to $H_2$ has been estimated to be ${\sim}(0.5-1){\times}10^{-9}$, which is about two orders of magnitude larger than the quiet dense cloud values. Our SiO observational result supports the existence of an expanding ring, which may be triggering active star formations in the Principal Cloud.

• 한국기계가공학회지
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• 제8권1호
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• pp.64-70
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• 2009

This study is to analyze the impact of automotive body with computer simulation. The total deformation, equivalent strain and strain and principal stress are analyzed respectively in case of front, rear and side impacts. The maximum total deformation of side impact is more than 6 times as large as that of rear impact. The maximum equivalent strain or stress of side impact is more than 4 times as large as that of rear impact. These deformation, strain and stress of front impact are a little more than those of rear impact. The maximum principal stress of side impact is more than 4.5 times as large as that of rear impact. This stress of front impact is a little more than that of rear impact.

• 한국정밀공학회지
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• 제32권5호
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• pp.463-470
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• 2015

Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• 대한산업공학회지
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• 제40권3호
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• pp.275-282
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• 2014

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.298-303
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• 2000

A procedure to predict the fatigue life of rubber components based on the signed principal strain method was proposed. A tension-compression rubber specimen with Jang-gu shape was designed and principal strain distribution was obtained by using the nonlinear finite element analysis. Finite element analysis and fatigue test of strut rubber mount were conducted to evaluate the fatigue life prediction procedure proposed. A procedure was employed to predict the fatigue life of strut rubber mount. Predicted fatigue lives have a good agreement with tested lives within a factor of 3.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2001년도 추계학술발표대회 논문집 제20권 2호
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• pp.85-88
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• 2001

Line spectrum pair(LSP) 계수는 양자화 오류에 강하고. 선형 릴간에 효율적이며, 필터의 안정성 판정이 용이하므로 LPC를 대신하여 음성 부호화에 널리 사용되고 있다. 일반적으로 LSP 계수간에는 일정한 상관관계가 나타나고, 이 특성을 이용하면 LSP 계수의 부호량을 줄일 수 있는 가능성이 있나. 본 논문에서는 LSP 계수를 압축하기 위해 principal component analysis(PCA)를 사용한 방법을 제안한다. 제안된 방법에서는 LSP 계수를 Karhunen-Loeve(KL) 변환해 에너지가 집중되는 고유치(eigenvalue)와 고유벡터(eigenvector)를 찾고 값을 양자화 한다. 성능 평가를 위해 2.4kbps MELP(mixed excitation linear prediction)와 8kbps QCELP(qualcumn code excited linear prediction) 음성 부호화기를 사용해 결과 값을 비교했고, 압축률이 증가하는 것을 확인했다.

• 대한기계학회논문집A
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• 제28권3호
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• pp.304-310
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• 2004

In this paper, cracking threshold for nanoindentation is analyzed by using 3D finited-element method. The analysis by maximum principal stress criterion can obtain the reliable results for determining to crack initiation location and load. Because the ratio of maximum principal stress to indentation depth for Victors indentation is smaller than flat-plane-column indentation and cracking for Victors indentation occurs from the inner part of specimen difficult to measure crack length, the nanoindentation facture test for flat-plane-column indentation is more effective.