• 대한수학회지
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• 제59권1호
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• pp.35-51
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• 2022

We give secure parameter suggestions to use sparse secret vectors in LWE based encryption schemes. This should replace existing security parameters, because homomorphic encryption (HE) schemes use quite different variables from the existing parameters. In particular, HE schemes using sparse secrets should be supported by experimental analysis, here we summarize existing attacks to be considered and security levels for each attacks. Based on the analysis and experiments, we compute optimal scaling factors for CKKS.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2023년도 제35회 한글 및 한국어 정보처리 학술대회
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• pp.694-697
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• 2023

본 논문은 문맥 이해에서 발생할 수 있는 문제점을 개선하기 위해 Sparse Attention 모델을 적용하였다. 실험 결과, 이 방법은 문맥 손실률을 상당히 줄이며 자연어 처리에 유용하다는 것을 확인하였다. 본 연구는 기계 학습과 자연어 처리분야에서 더 나은 문맥 이해를 위한 새로운 방향을 제시하며, 향후 다양한 모델과 방법론을 탐구하여 문맥 이해를 더욱 향상시킬 계획이다.

• 전력전자학회논문지
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• 제18권3호
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• pp.217-224
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• 2013

This paper presents a diagnostic method for a sparse matrix converter that detects faults in any single switch or a pair of switches. The sparse matrix converter is functionally equivalent to the standard matrix converter but has a reduced number of switches. The proposed diagnostic method is based in the measurement of input and output currents. The currents have respective characteristic according to the location of faulty switches. This method not only detects the switches of open-circuit fault but identifies the location of the faulty switching devices without complicated calculations. The simulation and experimental results verify that, based on the proposed method, the fault of sparse matrix converter can be easily and fast detected.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제20권3호
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• pp.225-242
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• 2016

The phase-type representation is strongly connected with the positive realization in positive system. We attempt to transform phase-type representation into sparse mono-cyclic positive representation with as low order as possible. Because equivalent positive representations of a given phase-type distribution are non-unique, it is important to find a simple sparse positive representation with lower order that leads to more effective use in applications. A Hypo-Feedback-Coxian Block (HFCB) representation is a good candidate for a simple sparse representation. Our objective is to find an HFCB representation with possibly lower order, including all the eigenvalues of the original generator. We introduce an efficient nonlinear optimization method for computing an HFCB representation from a given phase-type representation. We discuss numerical problems encountered when finding efficiently a stable solution of the nonlinear constrained optimization problem. Numerical simulations are performed to show the effectiveness of the proposed algorithm.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.173-180
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• 2018

This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2748-2766
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• 2016

Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• 한국컴퓨터산업학회논문지
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• 제5권9호
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• pp.1079-1088
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• 2004

일반적으로 코드 모션 알고리즘은 계산적 최적화와 레지스터 과부하와 연관되는 실행시간 최적화를 수행 한다. 본 논문은 계산적 최적화와 수명적 최적화에 더하여 코드의 크기를 고려하는 희소 코드 모션 알고리즘을 제안한다. 희소 코드 모션 알고리즘에서 BCM 알고리즘은 계산적으로 최적 코드 모션을 수행하고, LCM 알고리즘은 레지스터 과부하를 감소시킨다. 희소 코드 모션 알고리즘은 블필요한 코드 모션을 억제시키기 때문에 계산적으로나 수명적으로 최적인 알고리즘이다. 희소 코드 모션 알고리즘은 성능평가를 통하여 기존의 연구보다 프로그램의 능률 및 실행시간을 향상시켰다.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.360-369
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• 2017

In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

• 한국통신학회논문지
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• 제40권6호
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• pp.1024-1031
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• 2015

압축 센싱은 신호가 성긴 (Sparse) 특성을 지니며 선형 측정된 값들이 Incoherent 할 때, 나이퀴스트율 이하로 표본화된 신호를 원본 신호로 정확하게 복구할 수 있는 새로운 신호 획득 이론이다. 본 논문에서는 원본 신호의 Sparse한 정도에 따라 성능이 변화하는 압축 센싱을 이용한 효율적인 신호 검출 및 추정 기법을 제안하며, 이론적 분석과 함께 모의 실험 결과를 보여준다.