• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4556-4572
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• 2015

Dynamic texture (DT) recognition is a challenging problem in numerous applications. In this study, we propose a new algorithm for DT recognition based on group sparsity structure in conjunction with chaotic feature vector. Bag-of-words model is used to represent each video as a histogram of the chaotic feature vector, which is proposed to capture self-similarity property of the pixel intensity series. The recognition problem is then cast to a group sparsity model, which can be efficiently optimized through alternating direction method of multiplier algorithm. Experimental results show that the proposed method exhibited the best performance among several well-known DT modeling techniques.

• 전자공학회논문지
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• 제51권4호
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• pp.173-180
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• 2014

압축센싱은 성긴(Sparse) 또는 압축가능한(Compressible) 신호에 대해 Nyquist rate 미만의 샘플링으로도 신호 복원이 가능하다는 것을 수학적으로 증명한 새로운 패러다임의 신호 획득 방법이다. 단순한 신호 획득 과정을 이용하면서도, 동시에 우수한 압축센싱 복원 영상을 얻기 위한 많은 연구들이 수행되고 있다. 그러나, 에너지 분포 및 인간 시각 시스템 등 컬러 영상에 대한 기본적인 특성을 복원 과정에 활용한 기존 압축센싱 관련 연구는 많이 부족하다. 이러한 문제를 해결하기 위해, 본 논문에서는 컬러영상의 압축센싱 복원을 위한 평활 그룹-희소성 기반 반복적 경성 임계 알고리즘을 제안한다. 제안하는 방법은 그룹-희소성에 기반한 경성 임계치 적용과 프레임 기반 필터의 사용을 통해 영상의 변환 영역에 대한 희소성을 증대시키는 동시에 화소 영역의 평활 정도를 복원 과정에 활용할 수 있도록 한다. 또한, 그룹-희소화 경성 임계 과정은 자연 영상의 에너지 분포 및 인간 시각시스템 특성에 따라 중요하다고 판단되는 RGB-그룹 계수들을 보전하도록 설계하였다. 실험 결과 객관적 화질 측면에서 제안방법이 대표적인 그룹-희소화 평활 복원 기법 보다 평균 PSNR이 최대 2.7dB 높은 것을 확인하였다.

• 전자공학회논문지
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• 제52권9호
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• pp.106-113
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• 2015

본 논문에서는 컬러 영상의 압축 센싱 복원 기술에 인지시각시스템의 특성을 접목해 복원 영상의 화질을 향상 시키는 방법을 연구하였다. 제안하는 그룹-희소성 최소화 기반 컬러 채널별 시각적 가중치 적용 방법은 영상의 성긴 특성뿐만 아니라 인지시각시스템의 특성을 반영할 수 있도록 설계되었다. 또한, 복원 영상에서의 잡음을 제거하기 위하여 설계한 경계보존 필터는 영상의 경계 부분에 대한 디테일을 보존함으로써, 복원 영상의 품질을 향상 시키는 역할을 한다. 실험 결과, 제안하는 방법이 최신의 그룹-희소성 최소화 기반 방법들보다 평균 0.56 ~ 4dB 더 높은 PSNR을 달성함으로써, 객관적 성능을 향상시킬 수 있음을 확인하였으며, 주관적 화질 또한 기존 방법들에 비해 뛰어나다는 것을 복원된 영상 간 비교를 통해 확인하였다.

• Genomics & Informatics
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• 제20권4호
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• Journal of Information Technology Applications and Management
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• 제20권3_spc호
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• 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.

• 한국경영과학회지
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• 제13권1호
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• pp.31-38
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• 1988

This paper presents a methodology for the development of a part classification and coding system suited to each individual company. When coding a group of parts for a specific company by a general purpose part classification & coding system like OPITZ system, it is frequently observed that we use only a small subset of total available code numbers. Such sparsity in the actual occurrences of code numbers implies that we can design a better system which uses digits of the system more parsimoniously. A 2-dimensional fuzzy ISODATA algorithm is developed to extract the important characteristics for the classification from the set of given parts. Based on the extracted characteristics nd the distances between fuzzy clustering cenetroids, a company-unique classification and coding system can be developed. An example case study for a medium sized machine shop is presented.

• 한국콘텐츠학회논문지
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• 제7권10호
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• pp.19-26
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• 2007

협력적 필터링은 아이템에 대한 선호도를 기반으로 이웃 선정 방법을 사용하므로 내용을 반영하지 못할뿐만 아니라 희박성 및 확장성 문제를 가지고 있다. 이러한 문제를 개선하기 위하여 아이템 기반 협력적 필터링이 실용화되었으나 아이템의 속성을 반영하지는 못한다. 본 논문에서는 기존의 개인화 추천 시스템의 문제점을 해결하기 위하여 FP-Tree를 이용한 연관 군집 방법을 제안하였다. 제안된 방법으로는 FP-Tree를 이용하여 후보집합의 발생없이 빈발항목을 구성하고 연관규칙을 생성한다. 생성된 연관 규칙의 신뢰도에 따라서 $\alpha-cut$을 사용하여 효율적인 연관 군집을 한다. 성능평가를 위해 MovieLens 데이터 집합에서 Gibbs Sampling, EM, K-means와 비교 평가하였다.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.