• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.1
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• pp.27-33
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• 1975

The required minimum size of character diode matrix of Korean letters is estimated from the topological complexity of letter structure. The OR aombination of three letter boards (diode matrice) gives all possible Hangout whole letters in proper traditional combined form with minimum required discernibility. Two forms of first consonants (centre located ones for horizontal vowels and leftward displaced ones for vertical and composed vowels) are switched by only 1 bit of the vowel code. The vowel pattern length is modified by again the last four bits of the code. A new 15bit sized inner code is proposed which permits considerably small sized decoding mechanism.

• Smart Structures and Systems
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• v.15 no.1
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• pp.81-97
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• 2015

Recently, a new output-only modal identification method based on time-frequency independent component analysis (ICA) has been developed by the authors and shown to be useful for even highly-damped structures. In many cases, it is of interest to identify the complex modes of structures with non-proportional damping. This study extends the time-frequency ICA based method to a complex ICA formulation for output-only modal identification of non-proportionally-damped structures. The connection is established between complex ICA model and the complex-valued modal expansion with sparse time-frequency representation, thereby blindly separating the measured structural responses into the complex mode matrix and complex-valued modal responses. Numerical simulation on a non-proportionally-damped system, laboratory experiment on a highly-damped three-story frame, and a real-world highly-damped base-isolated structure identification example demonstrate the capability of the time-frequency complex ICA method for identification of structures with complex modes in a straightforward and efficient manner.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1239-1246
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• 1999

This paper presents a new algorithm to construct the modular network model for SSR analysis by simply applying KCL to each node and KVL to all branches connected to the node sequentially. This method has advantages that the model can be derived directly from the system data for transient stability study and turbine/generator shaft model, the resulted model in the form of augmented state matrix is very sparse, and thus efficient SSR study of a large scale system becomes possible. The proposed algorithm is verified with the IEEE First and Second Benchmark models.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.10
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• pp.410-422
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• 1984

This paper presents a new practical method for optimal active and reactive power control for the economic operation in electrical power system, and the programs are developed for digital computer solution. The major features and techniques of this paper are as follows: 1) The method is presented for finding the equivalent active power balance equation applying the sparse Jacobian matrix of power flow equation instead of using B constant as active power balance equation considering transmission loss, and thus for determining directly optimal active power allocation berween generator unitw satisfying the equality and inequality constraints. 2) The method is proposed for solving directly the optimum economim dispatch problem without using gradient method and penalty function for both active and reactive power control. As a result, the computing time are reduced and convergence characteristic is remarkably improved. 3) Unlike most of conventional methods which adopt the transmission loss as a objective function for reactive power control, the total fuel cost of themal power plant is adopted as objective function for both active and reactive power control. consequently, more reasonable and economic profit can be achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1412-1417
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• 2004

Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.75-83
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• 2007

Orthogonal frequency division multiplexing (OFDM) systems with multiple transmit antennas can exploit space-time block coding on each subchannel for reliable data transmission. Spacetime coded OFDM systems, however, are very sensitive to time variant channels because the channels need to be static over multiple OFDM symbol periods. In this paper, we propose to mitigate the channel variations in the frequency domain using a linear filter in the frequency domain that exploits the sparse structure of the system matrix in the frequency domain. Our approach has reduced complexity compared with alternative approaches based on time domain block-linear filters. Simulation results demonstrate that our proposed frequency domain block-linear filter reduces computational complexity by more than a factor of ten at the cost of small performance degradation, compared with a time domain block-linear filter.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.999-1000
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• 2006

The LDPC Code is focusing a powerful FEC(Forward Error Correction) codes for 4G Mobile Communication system. LDPC codes are used minimizing channel errors by modeling AWGN Channel as VDSL system. The performance of LDPC code is better than that of turbo code in long code word on iterative decoding algorithm. LDPC code are encoded by sparse parity check matrix. there are decoding algorithms for a LDPC code, Bit Flipping, Message passing, Sum-Product. Because LDPC Codes use low density parity bit, mathematical complexity is low and relating processing time becomes shorten.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.130-132
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• 2003

계산 과학을 사용하는 응용 분야는 공학, 물리, 화학, 생명 과학에서 경제학까지 다양하다. 계산 과학에 사용되는 많은 알고리즘들은 행렬 연산을 포함하고 있으며 이 행렬은 크기가 크고 대부분의 원소가 0값을 갖는 희소 행렬일 경우가 많다. 본 논문에서는 희소 행렬의 연산 중, 희소 행렬 A와 밀집 벡터 x, y에 대하여 ylongleftarrowy＋Ax와 ylongleftarrowy＋$A^{T}$ Ax 의 두 가지 연산에 대한 계산 속도 개선 방법으로서 레지스터 재사용을 높이는 레지스터 블록화와 캐쉬 미스를 줄이기 위한 캐쉬 최적화 방법을 제안하며 또한 희소 행렬의 특성과 target 컴퓨터의 구조에 따라 정해지는 레지스터 블록 크기를 결정하는 방법을 설명한다. Preliminary결과로 이 방법을 Pentium III system상에서 실험한 결과를 보이는데 ylongleftarrowy＋Ax 의 연산에 대하여는 2.5 배, ylongleftarrowy＋$A^{T}$ Ax 의 연산에 대하여는 3.5 배까지의 성능 개선을 이룰 수 있다.