• Structural Engineering and Mechanics
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• 제57권3호
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• pp.529-541
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• 2016

Under the interaction between dam body, dam foundation and external environment, the dam structural behavior presents the time-varying nonlinear characteristics. According to the prototypical observations, the correct identification on above nonlinear characteristics is very important for dam safety control. It is difficult to implement the description, analysis and diagnosis for dam structural behavior by use of any linear method. Based on the rescaled range analysis approach, the algorithm is proposed to identify and extract the fractal feature on observed dam structural behavior. The displacement behavior of one actual dam is taken as an example. The fractal long-range correlation for observed displacement behavior is analyzed and revealed. The feasibility and validity of the proposed method is verified. It is indicated that the mechanism evidence can be provided for the prediction and diagnosis of dam structural behavior by using the fractal identification method. The proposed approach has a high potential for other similar applications.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.817-824
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• 2010

Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

• 경영정보학연구
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• 제25권1호
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• pp.47-73
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• 2023

본 연구는 인공지능 기법 중 다중레이블 속성선택 방법을 적용하여 복잡한 경영환경에서 의사결정의 효과성을 증대시키는 방안을 설명한다. 인공지능 기반의 의사결정 시스템은 의사결정자의 선택과 판단을 돕거나, 대신하는 중요한 역할을 한다. 더욱이 최근 인공지능을 중심으로 한 비즈니스 의사결정은 기업의 성장 동력으로 평가받는데, 이를 위해서는 효과적인 의사결정 방법이 수반되어야 한다. 이에 본 연구는 의미 있는 속성값을 선별하는 CFS-BR(이진연관성 접근 기반의 상관관계 속성선택 모델)을 제안하여, 효과적인 의사결정을 지원하는 것을 돕는다. 예시데이터와 실증데이터의 분석 결과, CFS-BR은 유의미한 속성을 최상우선선별 알고리즘 기반으로 최상의 조합을 선별하므로 효율적 의사결정을 지원할 수 있고, 기존의 다중 레이블 속성선택 방법과 비교하였을 때 정확도가 높은 것으로 보아 효과적인 의사결정을 증대시키는 데 유용하다.

• Smart Structures and Systems
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• 제30권6호
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• 대한전자공학회논문지SP
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• 제48권4호
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• pp.76-85
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• 2011

본 논문에서는 Gabor 특징과 웨이브렛 영역의 BDIP와 BVLC 특징을 이용한 질감 특징 기반 언어 인식 방법을 제안한다. 제안된 방법에서는 먼저 시험 영상에 Gabor 변환과 웨이브렛 변환을 적용한다. 웨이브렛 영역의 상세 대역에는 Donoho의 연역치화를 적용하여 잡음을 제거한다. 이어서 Gabor 영상에는 크기 연산자를 적용하고 웨이브렛 부대역에는 BDIP와 BVLC 연산자를 적용한다. 그런 다음 Gabor 크기 영상과 BDIP, BVLC 부대역에 대하여 통계치를 계산하여 그 결과들을 벡터화하고 융합하여 특징 벡터로 사용한다. 분류 단계에서는 얼굴 인식에 주로 사용되는 WPCA를 분류기로 하여 시험 특징 벡터와 가장 유사한 학습 특징 벡터를 찾는다. 실험 결과 제안된 방법은 실험 문서 영상 DB에 대하여 비교적 낮은 특징 벡터 차원으로 매우 우수한 언어 인식 성능을 보여준다.

• Journal of Welding and Joining
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• 제16권6호
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• pp.86-96
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• 1998

This study proposes the analysis method of time series ultrasonic signal using the chaotic feature extraction for degradation extent evaluation. Features extracted from time series data using the chaotic time series signal analyze quantitatively degradation extent. For this purpose, analysis objective in this study is fractal dimension, lyapunov exponent, strange attractor on hyperspace. The lyapunov exponent is a measure of the rate at which nearby trajectories in phase space diverge. Chaotic trajectories have at least one positive lyapunov exponent. The fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal correlation) dimensions, lyapunov exponents, energy variation showed values of 2.217∼2.411, 0.097∼ 0.146, 1.601∼1.476 voltage according to degardation extent. The proposed chaotic feature extraction in this study can enhances precision ate of degradation extent evaluation from degradation extent results of the degraded materials (SA508 CL.3)

• 음성과학
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• 제12권2호
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• pp.171-182
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• 2005

An efficient GMM (Gaussian mixture modeling) method based on LPCA (local principal component analysis) with VQ (vector quantization) for speaker identification is proposed. To reduce the dimension and correlation of the feature vector, this paper proposes a speaker identification method based on principal component analysis. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs PCA in each region. Finally, the GMM for the speaker is obtained from the transformed feature vectors in each region. Compared to the conventional GMM method with diagonal covariance matrix, the proposed method requires less storage and complexity while maintaining the same performance requires less storage and shows faster results.

• Journal of Oral Medicine and Pain
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• 제9권1호
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• pp.157-167
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• 1984

A human bite-mark shows special feature according to the suspect's dentition. The teeth which most frequently give useful bite-marks are six upper and lower anteriors, while the premolar teeth somtimes give marks it is often difficult to distinguish. The author tried to classify and to analize the anterior dentition which makes the bite-mark directly by means of 672 maxillary and 691 mandibular stone model taken from Korean adult aging from 17 to 40 years old. The results were as follows : 1. There was no particular correlation between the presence of six normal-shaped and correctly positioned upper and lower anteriors and the presence of rotation of teeth. 2. Inter central incisor, inter lateral incisor, inter canine width and angles of adjecent teeth were not identical eath other in studied models. 3. The results of this analysis supported the statement that any bite-mark had no same feature.

• 정보보호학회논문지
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• 제24권5호
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• pp.1013-1022
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• 2014

상관관계 전력 분석(Correlation Power Analysis, CPA)은 암호장비에서 알고리즘이 수행될 때 누설되는 전력 소비 신호와 알고리즘의 중간 계산 값의 상관도를 이용하여 비밀키를 추출하는 부채널 공격 방법이다. CPA는 누설된 전력 소비의 시간적인 동기 또는 잡음에 의해 공격 성능이 영향을 받는다. 최근 전력 분석의 성능 향상을 위해 다양한 신호 처리 기술이 연구되어지고 있으며, 그 중 주성분 분석 기반의 신호 압축 기술이 제안되었다. 주성분 분석 기반의 신호 압축은 주성분 선택 방법에 따라 분석 성능에 영향을 주기 때문에 주성분 선택은 중요한 문제이다. 본 논문에서는 CPA의 성능 향상을 위해 전력 소비와의 상관도가 높은 주성분을 선택하는 주성분 선택 기법을 제안한다. 또한 각 주성분이 갖는 특징이 다르다는 점을 이용한 주성분 기반 CPA 분석 기법을 제안하고, 기존 방법과 제안하는 방법의 실험적인 분석을 통해 공격 성능이 향상됨을 보인다.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.