• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.241-252
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• 2000

In this paper, a translation, rotation and scale invariant system for the recognition of closed 2-D images using the bispectrum of a contour sequence and a weighted fuzzy classifier is derived and compared with the recognition process using one of the competitive neural algorithm, called a LVQ( Loaming Vector Quantization). The bispectrum based on third order cumulants is applied to the contour sequences of an image to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to the represent two-dimensional planar images and are fed into a weighted fuzzy classifier. The experimental processes with eight different shapes of aircraft images are presented to illustrate a relatively high performance of the proposed recognition system.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.1
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• pp.89-95
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• 2011

In this study, We modeled the deck house of the container ship like the representative bluff body and made the model ship. By using the PIV technique, the exhaust gas anti-reflux effect of the deck house backward according to open and close of the Sunken Deck and installation of the deflector in deck house side were measured in circulating water channel. The experiment system consists of hi-speed camera, laser, image board, host computer. The mean velocity vector and time mean axial velocity were found in deck house backward and the results were compared each case.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.136-140
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• 2015

In this paper, a comparative studies on the STAP and SFAP were performed, which are known as representative anti-jamming technology for adaptive array antenna. As a method of estimating the weighting vector for simulation, MMSE(Minimum Mean Square Error) algorithm was commonly used and the analyses of the simulation performance in various jamming environments were presented. Especially, performance comparison between STAP and SFAP according to the jamming power J/S(Jamming to Signal Power Ratio), performance comparison in the ratio of jamming bandwidth to signal bandwidth, and performance comparison of BER between STAP and SFAP were presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8A
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• pp.752-759
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• 2006

In this paper, the performances of representative high resolution direction finding algorithms are evaluated and compared in wideband mobile environments. The angular spread phenomenon in mobile environments is first investigated and then a vector channel model for wideband OFDMA signals is derived. A direction finding system architecture for OFDMA smart antenna systems is proposed and finally the performances of high resolution direction finding algorithms are evaluated in wideband mobile environments by taking the WiBro system as a target system.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.94-101
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• 2002

In this paper, we propose a motion estimation algorithm for the discontinuity blocks. The algorithm uses sub-SAD value (i.e. the sum of absolute difference for a quarter of a block) to identify the discontinuity region, and produces additional motion vectors for these sub-blocks if necessary. We show with experimental results that, in comparison with some conventional motion estimation algorithms, the proposed algorithm achieves quality enhancement for the sequences with discontinuity blocks, and also shows the same computational quantity as to normal algorithms for sequences with less discontinuity.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.73-74
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• 2017

SVM(Support Vector Machine) 알고리즘은 대표적인 기계 학습 분류 알고리즘으로 감정 분석, 제스처 인식 등 다양한 분야의 문제를 해결하기 위해 사용되고 있다. SVM 알고리즘은 분리경계면(Hyper-Plane) 또는 분리경계면 집합 중 지지벡터(Support Vector)라 불리는 특정한 점들로 이루어진 두 그룹 간의 거리 차이(Margin)를 최대로 하는 분리경계면을 이용하여 데이터를 분류하는 알고리즘이다. 높은 정확도를 제공하지만 처리 속도가 느리며 학습을 위해 대량의 데이터 및 메모리가 필요하기 때문에 자원이 제한적인 IoT 환경에서 사용이 어렵다. 본 논문에서는 자원이 제한된 IoT 노드를 기반으로 효율적으로 데이터를 학습하기 위해 K-means 알고리즘을 이용하여 SVM 알고리즘의 저전력화 방안을 연구한다.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1134-1141
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• 2005

With ever more popularity of video web-publishing, popular content is being compressed, reformatted and modified, resulting in excessive content duplication. Such overlapped data can cause problem of search speed and rate of searching. However, duplicated data on other site can provide alternatives while specific site cause problem. This paper proposes the efficient method, for retrieving. similar video data in large database. In this research we have used the method to compare summarized video image instead of the raw video data, and detected similar videos through clustering in that dimension feature vector through PCA(principle component analysis). We show that our proposed method is efficient and accurate through our experiment.

• Journal of Korea Water Resources Association
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• v.30 no.1
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• pp.63-73
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• 1997

Finite element model is established for the simulation of groundwater flow due to water curtain around underground oil storage Choleski decomposition method. The symmetric global conductance matrix is solved by vector storage Choleski decomposition method. The model is verified through comparison with the results of electric analogy. For the application of this model to real site, the finite element meshes are constructed according to representative vertical cross and longitudinal sections. In cross-sectional analysis, potential and flow distributions are compared based on the cavern pressure and horizontal water curtain. For longitudinal section, effects between nearly located caverns with or without vertical water curtain are analyzed. These results prove that the established model can be used as a tool for flow analysis around underground caverns.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.53-56
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• 2005

NTSS(New Three-Step Search Algorithm)는 대표적인 Fast BMA(Block Matching Algorithm)인 TSS(Three-Step Search Algorithm)에 중앙 편향적(Center-Biased) 특성을 고려하여 향상시킨 방법이다. 그러나 NTSS는 움직임이 작은 영상인 경우에는 TSS보다 개선된 성능을 보여주지만, 움직임이 큰 영상에 대해서는 TSS와 큰 차이가 없으며 탐색영역이 커질수록 오히려 성능이 떨어지는 단점이 있다. 본 논문에서는 움직임 벡터의 특성에 맞는 탐색영역을 적용시킴으로써 탐색영역의 증가로 발생되는 NTSS의 단점을 보완하여 움직임이 큰 영상에 대해서도 향상된 성능을 갖는 방법을 제안한다. 제안된 방법을 적용 하였을 때 움직임이 작은 영상에서는 기존의 방법과 동일한 결과를 얻었으며 움직임이 큰 영상에서는 최고 0.5dB이상 성능이 개선되었다.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.747-754
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• 2002

In this paper, We propose a document thematic words extraction by using principal component analysis(PCA) which is one of the multivariate statistical methods. The proposed PCA model understands the flow of words in the document by using an eigenvalue and an eigenvector, and extracts thematic words. The proposed model is estimated by applying to document summarization. Experimental results using newspaper articles show that the proposed model is superior to the model using either word frequency or information retrieval thesaurus. We expect that the Proposed model can be applied to information retrieval , information extraction and document summarization.