• 대한전자공학회논문지SP
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• 제43권2호
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• pp.82-86
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• 2006

패턴인식 시스템은 일반적으로 데이터의 전처리, 특징 추출, 학습단계의 과정을 거쳐서 개발되어 진다. 그중에서도 특징 추출 과정은 다차원 공간을 가진 입력 데이터의 복잡도를 줄여서 다음 단계인 학습단계에서 계산 복잡도와 인식률을 향상시키는 역할을 한다. 패턴인식에서 특징 추출 기법으로써 principal component analysis, factor analysis, linear discriminant analysis 같은 방법들이 널리 사용되어져 왔다. 이 논문에서는 singular value decomposition (SVD) 방법이 패턴인식 시스템의 특징 추출과정에 유용하게 사용될 수 있음을 보인다. 특징 추출단계에서 SVD 기법의 유용성을 검증하기 위하여 원격탐사 응용에 적용하였는데, 실험결과는 널리 쓰이는 PCA에 비해 약 25%의 인식률의 향상을 가져온다는 것을 알 수 있다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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• pp.1162-1165
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• 2001

Operational Modal Analysis also known as Output Only Modal Analysis has in the recent years been used for extracting modal parameters of civil engineering structures and is now becoming popular for mechanical structures. The advantage of the method is that no artificial excitation need to be applied to the structure or force signals to be measured. All the parameter estimation is based upon the response signals, thereby minimising the work of preparation for the test. This test case is a controlled lab set-up enabling different parameter estimation methods techniques to be used and compared to the Operational Modal Analysis. For Operational Modal Analysis two different estimation techniques are used： a non-parametric technique based on Frequency Domain Decomposition (FDD), and a parametric technique working on the raw data in time domain, a data driven Stochastic Subspace Identification (SS!) algorithm. These are compared to other methods such as traditional Modal Analysis.

• Nuclear Engineering and Technology
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• 제51권4호
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• pp.968-976
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• 2019

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

• 한국전산구조공학회논문집
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• 제23권2호
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• pp.153-163
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• 2010

Krylov 부공간 모델차수축소법은 초기 유한요소모델과 축소모델의 전달함수의 계수인 모멘트를 일치시키는 방법을 이용하는 축소기법으로 이미 대형 유한요소모델의 주파수응답함수의 효율적인 계산에 많이 사용되고 있는 방법 중의 하나이다. 본 논문에서는 Krylov 부공간 축소기법을 이용한 관심 주파수영역에 대한 주파수응답 해석과 이를 통하여 계산된 주파수응답의 여러 가지 설계변수에 대한 설계민감도 해석 방법을 제안하였다. 일반적으로 기계시스템의 주파수응답을 고려한 최적설계를 위해서는 설계변수에 대한 관심 주파수영역에서의 주파수응답 및 그의 민감도 정보가 요구되므로, 고려하는 유한요소모델이 대형일 경우에는 관심 주파수영역에서의 반복적인 해석으로 인한 계산비용의 문제가 심각하게 대두된다. 본 논문에서는 축소모델을 이용하여 주파수응답과 주파수응답의 설계민감도 해석을 수행하여 계산의 효율성을 극대화하였다. 계산상 시스템행렬의 민감도 계산에는 시간측면과 구현의 용이성 측면에서 장점이 있는 준해석적 방법을 이용하였다. 수치 예제를 통하여 축소기법을 이용한 주파수응답의 설계민감도 해석 결과를 초기 유한요소모델의 민감도 결과와 비교하여 우수한 정확성 및 효율성을 확인하였다. 본 논문에서 제안된 방법을 주파수응답을 고려하는 최적설계에 이용하는 경우, 결과의 정확성 및 계산비용 측면에서 매우 효과적인 방법이 될 수 있을 것으로 판단된다.

• Smart Structures and Systems
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• 제10권2호
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• pp.131-154
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• 2012

In this paper, it is aimed to determine the seismic behaviour of highway bridges by nondestructive testing using ambient vibration measurements. Eynel Highway Bridge which has arch type structural system with a total length of 216 m and located in the Ayvaclk county of Samsun, Turkey is selected as an application. The bridge connects the villages which are separated with Suat U$\breve{g}$urlu Dam Lake. A three dimensional finite element model is first established for a highway bridge using project drawings and an analytical modal analysis is then performed to generate natural frequencies and mode shapes in the three orthogonal directions. The ambient vibration measurements are carried out on the bridge deck under natural excitation such as traffic, human walking and wind loads using Operational Modal Analysis. Sensitive seismic accelerometers are used to collect signals obtained from the experimental tests. To obtain experimental dynamic characteristics, two output-only system identification techniques are employed namely, Enhanced Frequency Domain Decomposition technique in the frequency domain and Stochastic Subspace Identification technique in time domain. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of boundary conditions to reduce the differences between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. After finite element model updating, maximum differences between the natural frequencies are reduced averagely from 23% to 3%. The updated finite element model reflects the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring. Analytical model of the bridge before and after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behaviour. It can be seen from the analysis results that displacements increase by the height of bridge columns and along to middle point of the deck and main arches. Bending moments have an increasing trend along to first and last 50 m and have a decreasing trend long to the middle of the main arches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• 응용통계연구
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• 제30권1호
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• pp.83-93
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• 2017

k (${\geq}2$) 그룹의 p-차원 데이터의 시각화에서 가장 전형적인 방법은 Fisher의 정준판별분석(canonical discriminant analysis; CDA)이다. CDA는 마할라노비스 공간에서 k개 그룹 중심을 근사하게 통과하는 저차원 부공간에 관측점들을 사영한다. 본 논문은 척도화 유클리드 공간에서 다그룹 다차원 데이터를 시각화하는 방법을 제안하는데, 저차원 부공간의 제1축(또는 제1축과 제2축)은 그룹 중심들의 최대변별(maximum discrimination)에서 찾고 부공간의 제2축(또는 제3축)은 관측개체들의 최대산포(maximum dispersion)에서 찾는다. 이러한 혼종방법(hybrid method)은 2-그룹 다차원 자료의 시각화에서 특히 유용하다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• Communications for Statistical Applications and Methods
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• 제18권3호
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• pp.267-275
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• 2011

According to recent studies, Bayesian information criteria(BIC) is proposed to determine the structural dimension of the central subspace through sliced inverse regression(SIR) with high-dimensional predictors. The BIC may be useful in K-means clustering inverse regression(KIR) with high-dimensional predictors. However, the direct application of the BIC to KIR may be problematic, because the slicing scheme in SIR is not the same as that of KIR. In this paper, we present empirical penalty term studies of BIC in KIR to identify the most appropriate one. Numerical studies and real data analysis are presented.