• 전자공학회논문지B
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• 제30B권9호
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• pp.84-93
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• 1993

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for the shape distortions in handwritten characters. In this paper, we review these nonlinear shape normalization methods from the two points of view : feature projection and feature density equalization. The former makes feature projection histogram by projecting a certain feature at each point of input image into horizontal-or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. A systematic comparison of these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity and measure of variation. Then, we present the result of quantitative evaluation of each method based on these criteria for a large variety of handwritten Hangul syllables.

• 한국철도학회논문집
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• 제2권1호
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• pp.38-46
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• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objective in this study is fractal dimension and attractor quadrant feature. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor feature extraction in this study can enhance the precision rate of ultrasonic evaluation for defect signals of rail weld zone such as side hole and crack.

• 지능정보연구
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• 제7권1호
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• pp.47-61
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• 2001

사례기반 추론과 같은 사후학습 기법은 인공신경망이나 의사결정나무와 같은 사전학습 기법에 비해서 여러 장점을 가지고 있다. 하지만, 사후학습 기법은 사례 표현에 관련성이 적은 속성이 포함된 경우에는 성능이 저하되는 단점을 가지고 있다. 이러한 단점을 극복하기 위해서, 속성 가중치 부여 방법들이 연구되었다. 기존의 속성 가중치 부여 방법들은 대부분 전역적으로 속성 가중치를 부여하는 것이었다. 본 연구에서는 새로운 지역적 속성 가중치 부여 방법인 CBDFW를 제안한다. CBDFW 기법은 무작위로 생성된 속성 가중치들의 분류 성공 여부를 저장하고 있다가, 새로운 사례가 주어졌을 때에 성공적인 분류 결과를 보인 가중치들을 검색하여 동적으로 새로운 가중치들을 생성해낸다. 신용평가 데이터로 CBDFW의 성능을 실험한 결과, 기존의 연구들에서 제시된 분류 적중률보다 우수한 성능을 보였다.

• Journal of Welding and Joining
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• 제18권5호
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• pp.77-83
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• 2000

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objective in this study is fractal dimension and attractor quadrant feature. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These difference in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor analysis and DAC in this study can enhance the precision rate of ultrasonic evaluation for defect signals of rail weld zone such as side hole and crack.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.214-217
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• 2000

Many researches have been performed for human recognition and coding schemes recently. For this situation, we propose an automatic facial feature extraction algorithm. There are two main steps: the face region evaluation from original background image such as office, and the facial feature extraction from the evaluated face region. In the face evaluation, Genetic Algorithm is adopted to search face region in background easily such as office and household in the first step, and Template Matching Method is used to extract the facial feature in the second step. We can extract facial feature more fast and exact by using over the proposed Algorithm.

• 반도체디스플레이기술학회지
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• 제14권4호
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• pp.56-60
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• 2015

Mobile devices like smartphones and tablets are becoming increasingly capable in terms of processing power. Although they are already used in computer vision, no comparable measurement experiments of the popular feature extraction algorithm have been made yet. That is, local feature descriptors are widely used in many computer vision applications, and recently various methods have been proposed. While there are many evaluations have focused on various aspects of local features, matching accuracy, however there are no comparisons considering on speed trade-offs of recent descriptors such as ORB, FAST and BRISK. In this paper, we try to provide a performance evaluation of feature descriptors, and compare their matching precision and speed in KD-Tree setup with efficient computation of Hamming distance. The experimental results show that the recently proposed real valued descriptors such as ORB and FAST outperform state-of-the-art descriptors such SIFT and SURF in both, speed-up efficiency and precision/recall.

• 한국멀티미디어학회논문지
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• 제7권2호
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• pp.156-163
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• 2004

본 논문에서는 적응적 얼굴영역 검출과 얼굴 특징자 평가함수를 사용한 실시간 얼굴인식 알고리즘을 제안하였다. 제안한 알고리즘은 명암도 정보와 타원마스킹 기법뿐만 아니라 인종별 얼굴피부색을 사용하여 정확한 얼굴영역을 적응적으로 검출 가능하다. 또한 제안한 알고리즘은 얼굴 특징자 및 얼굴특징자간 기하학적 평가함수를 사용하여 얼굴 인식 효율을 개선하였다. 제안한 알고리즘은 생체인증 및 보안 시스템 분야에 사용 가능하다. 실험에서는 제안한 방법의 우수성을 입증하기 위해 실 영상을 사용하였으며 실험 결과 기존의 방법보다 얼굴 영역 검출뿐만 아니라 얼굴인식 성능을 개선하였다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권3호
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• pp.153-163
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• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• 한국철도학회:학술대회논문집
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• 한국철도학회 1998년도 추계학술대회 논문집
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• pp.87-95
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• 1998

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of welding defects. For this purpose, analysis objective in this study is fractal dimension and attractor Quadrant feature. Trajectory changes in the attractor indicated that even the same type of defects carried substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange. Such differences in characteristics of weld defects enables the evaluation of unique features of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hale) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor feature extraction in this study can enhance the precision rate of ultrasonic evalaution for defect signals of rail weld zone such as side hole and crack.

• 전력전자학회논문지
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• 제27권4호
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.