• 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.

• 한국산학기술학회논문지
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• 제11권4호
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• pp.1342-1347
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• 2010

무선 센서 네트워크의 다양한 연구 분야 중에서 철조망에서의 표적의 침입 탐지 및 식별에 관한 연구는 산업시설, 보안지역, 교도소, 군사지역, 공항 등 다양한 분야에서 사용된다. 현재 철조망 감시는 대부분 유선 센서 노드를 통한 유선 센서 네트워크 환경에서 이루어지고 있다. 기존의 유선 센서 네트워크는 100bps 이상의 높은 데이터 전송률을 통해 수신되는 높은 샘플링 신호를 이용하여 고속 푸리에 변환에 의한 신호의 주파수 분석 기법을 사용해 왔다. 하지만, 유선 센서 네트워크의 높은 데이터 전송률과 비교하여 무선 센서 네트워크의 센서 노드는 유선 센서 네트워크에 비해 매우 낮은 데이터 전송률을 가진다. 따라서 무선 센서 네트워크에서 수신되는 신호의 샘플링이 매우 낮고, 유선 센서 네트워크에서 사용된 고속 푸리에 변환에 의한 신호의 주파수 분석에 따른 주파수별 특징 추출을 할 수 없다. 따라서 본 논문에서는 철조망 감시를 위한 높은 데이터 전송률을 보장하는 유선 센서 네트워크에 비해 제한된 통신자원과 센서 노드의 낮은 데이터 전송률로 인해 수신되는 한정적인 신호의 정보를 이용한 무선 센서 네트워크에서 철조망의 표적 침입 탐지 및 식별을 위한 특징 추출 알고리즘을 제안한다.

• 한국음향학회지
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• 제3권1호
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• pp.25-35
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• 1984

This paper analysis the Dynamic Time Warping algorithms for time normalization of speech pattern and discusses the Dynamic Programming algorithm for spoken Korean isolated digits recognition. In the DP matching, feature vectors of the reference and test pattern are consisted of first three formant frequencies extracted by power spectrum density estimation algorithm of the ARMA model. The major differences in the various DTW algorithms include the global path constrains, the local continuity constraints on the path, and the distance weighting/normalization used to give the overall minimum distance. The performance criterias to evaluate these DP algorithms are memory requirement, speed of implementation, and recognition accuracy.

• 아태비즈니스연구
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• 제13권2호
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• pp.23-37
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• 2022

Purpose - The purpose of this paper is to analyze the dynamic factors of the daily FX implied volatility based on the fractional integration methods focusing on long memory feature and structural changes. Design/methodology/approach - This paper uses the daily FX implied volatility data of the EUR-USD and the JPY-USD exchange rates. For the fractional integration analysis, this paper first applies the basic ARFIMA-FIGARCH model and the Local Whittle method to explore the long memory feature in the implied volatility series. Then, this paper employs the Adaptive-ARFIMA-Adaptive-FIGARCH model with a flexible Fourier form to allow for the structural changes with the long memory feature in the implied volatility series. Findings - This paper finds statistical evidence of the long memory feature in the first two moments of the implied volatility series. And, this paper shows that the structural changes appear to be an important factor and that neglecting the structural changes may lead to an upward bias in the long memory feature of the implied volatility series. Research implications or Originality - The implied volatility has widely been believed to be the market's best forecast regarding the future volatility in FX markets, and modeling the evolution of the implied volatility is quite important as it has clear implications for the behavior of the exchange rates in FX markets. The Adaptive-ARFIMA-Adaptive-FIGARCH model could be an excellent description for the FX implied volatility series

• Journal of Information Processing Systems
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• 제17권3호
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Earthquakes and Structures
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• 제20권4호
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• pp.405-415
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• 2021

Following the dynamic property analysis and elaboration, linear response spectrum analysis (RSA) and response history analysis (RHA) were conducted on a representative hyperbolic cooling towers (HCT) in present study. The seismic responses in tower shell were illustrated in detail, including the internal force amplitude, modal contribution, influence from damping ratio, comparison of results got from RSA and RHA and especially the latitude distributions of internal forces. The results show that the eigenmodes could be classified in a new method into four types according to their mode shapes and only the lateral bending modes and vertical stretching modes are meaningful for horizontal and vertical earthquake correspondingly. The bending modes and seismic deformation display the same feature which is global lateral bending accompanied by minute circular flow displacement of section. This feature also decides the latitude distributions of internal forces as sine or cosine. Moreover, the following method is also proposed for approximate estimation of internal force amplitudes without time-consuming response history analysis: getting the response spectrums of the selected ground accelerations and then comparing values of response spectrums at the natural period of first lateral bending mode because it is always prime dominant for horizontal seismic responses.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1778-1785
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• 2010

This paper proposes a new video-based method of analyzing human gait which is a highly variable dynamic process. It captures a human gait of varying directions as a trajectory in the phase space. The proposed method includes two options of a stochastic process model and a self-organizing feature map as the tool of feature space representation and analysis. Test results show that the model is highly intuitive and we believe it can contribute to our understanding of human activity as well as gait behavior.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.410-414
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• 1996

A visual servoing algorithm is proposed for a robot with a camera in hand. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a, commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• 전기학회논문지
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• 제59권12호
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• 한국정보통신학회논문지
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• 제13권8호
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• pp.1693-1698
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• 2009

본 논문에서는 서명에서 생성되는 속도 성분(방향 정보, 거리 정보)에서 방향 정보만을 특징 정보로 서명DB에서 저장하고 거리(속력) 정보는 조정상수(w)를 이용함으로써 오류율에 영향을 주지 않으면서 처리속도를 개선하고, 특징 정보 크기도 줄일 수 있었다. 이를 위해 통계적 검정 T-test를 이용하여 확인하였다.