• Journal of applied mathematics & informatics
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• 제16권1_2호
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• pp.307-321
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• 2004

In this paper, the unsteady Stokes problem is considered and also the pressure-correction method for the problem is described. At a fixed time level, we reduce the problem to two symmetric positive definite problems which depend on a time step parameter. Linear systems that arise from the problems are large, sparse, symmetric, positive definite and ill-conditioned as the time step tends to zero. Preconditioned problems based on an additive Schwarz method for solving the symmetric positive definite problems are derived and preconditioners are defined implicitly. It will be shown that the rate of convergence is independent of the mesh parameters as well as the time step size.

• 전기학회논문지
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• 제66권7호
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• pp.1078-1082
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• 2017

This paper presents an L1 optimization based filtering technique which effectively eliminates the optical misalignment effects encountered in the squint guidance mode with strapdown seekers. We formulated a series of L1 optimization problems in order to separate the bias and the gradient components from the measured data, and solved them via the alternating direction method of multipliers (ADMM) and sparse matrix decomposition techniques. The proposed technique was able to rapidly detect arbitrary discontinuities and gradient changes from the measured signals, and was shown to effectively cancel the undesirable effects coming from the seeker misalignment angles. The technique was implemented on embedded flight computers and the real-time operational performance was verified via the hardware-in-the-loop simulation (HILS) tests in parallel with the automatic target recognition algorithms and the intra-red synthetic target images.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• 한국음향학회지
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• 제36권4호
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• pp.285-291
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• 2017

두 개 수신기에 들어오는 신호 간의 시간 지연 값을 추정하기 위한 방법들이 연구되고 있다. 그중에서 채널 추정 기법을 기반으로 한 방법의 경우는 두 수신기의 입력 신호간의 상대적인 지연을 채널의 임펄스 응답처럼 추정하는 방법이다. 이 경우에는 해당 채널의 특성이 희소 채널의 특성을 가지고 있다. 기존의 방법들은 채널의 희소성을 이용하지 못하고 있는 방법이 대부분이다. 본 논문에서는 채널의 희소성을 이용하기 위하여 희소 신호 최적화 방법의 하나인 BPD(Basis Pursuit Denoising) 최적화 기법을 사용한 시간 지연 추정 방법을 제안한다. 제안한 방법을 기존의 일반 상호 상관(Generalized Cross Correlation, GCC) 방법과 적응 소유치 분해법 및 희소 신호 추정법의 일종인 RZA-LMS(Reweighted Zero-Attracting Least Mean Square)들과 비교하여, 백색 가우시안 신호원과 유색 신호원 및 해양 포유류 신호원에 대해서 비교 실험을 하였다. 그 결과 갑자기 추정성능이 열화되는 문턱 현상이 늦게 나타나거나 훨씬 줄어드는 것을 보였다.

• 대한전자공학회논문지SP
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• 제37권6호
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• pp.48-59
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• 2000

안테나 센서 어레이를 이용하여 수신되는 전파의 도래각을 추정하는 방식으로서 MUSIC(multiple signal classification)과 같은 고유분해(eigendecomposition)를 기반으로 한 방식은 백색잡음 환경하에서는 고분해능의 우수한 성능을 보이지만 유색잡음이 존재하는 환경에서는 성능이 크게 저하된다. 본 논문에서는 주기성을 가진 신호에 잡음이 더해진 선호를 웨이브렛 영역으로 변환하여 신호와 잡음을 분리하는 방법을 사용하여 유색잡음이 있는 환경에서 도래각 추정 문제를 접근하였다. 배경잡음만 있는 경우 센서 어레이 출력을 이산 웨이브렛 분해를 하여 얻은 멀티스케일 성분들의 공분산 행렬은 밴드화된 행렬로 근사화 할 수 있는데 비하여 협대역 신호는 멀티스케일 성분간의 상관성은 급속히 감소하는 현상을 보이지 않고 공분산 행렬에서는 신호성분이 전체 행렬에 분포한다. 어레이 출력의 공분산 행렬을 웨이브렛 영역으로 변환하여 유색잡음에 해당하는 특정 밴드를 삭제하고 MUSIC과 같은 기존의 공간 스펙트럼 추정방식을 적용하여 도래각을 추정 한 다음 그 결과로 부터 신호성분을 합성하여 삭제한 밴드를 채우는 과정을 반복하여 정확한 도래각을 얻는 방안을 제안하였다. 제안된 알고리즘의 성능을 여러 가지 형태의 상관함수 특성을 가진 유색잡음 환경에서 모의실험을 통하여 기존 방식과 비교 분석하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권4호
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• pp.248-257
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• 2002

군집화는 객체들의 특성을 분석하여 유사한 성질을 갖고 있는 객체들을 동일한 집단으로 분류하는 방법이다. 전자 상거래 자료처럼 차원 수가 많고 누락 값이 많은 자료의 경우 입력 자료의 차원축약, 잡음제거를 목적으로 SVD를 사용하여 군집화를 수행하는 것이 효과적이지만, SVD를 통해 변환된 자료는 원래의 속성 정보를 상실하기 때문에 군집 결과분석에서 원본 속성의 가치 해석이 어렵다. 따라서 본 연구는 군집화 수행 후 엔트로피 가중치 및 SVD를 이용하여 군집의 중요한 속성을 발견하기 위한 군집 특징 선택 기법 ENTROPY-SVD를 제안한다. ENTROPY-SVD는 자료의 속성들과 유사객체 군과의 묵시적인 은닉 구조를 활용하기 위하여 SVD를 이용하고 유사객체 군에 포함된 응집도가 높은 속성들을 발견하기 위하여 엔트로피 가중치를 사용한다. 또한 ENTROPY-SVD를 적용한 모델 기반의 협력적 여과기법의 추천 시스템 CFS-CF를 제안하고 그 효용성 및 효과를 평가한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2702-2719
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• 2013

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

• Bulletin of the Korean Chemical Society
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• 제29권10호
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• pp.2017-2022
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• 2008

Of the experiments to shorten NMR measuring time by sparse sampling, non-uniform sampling (NUS) is advantageous. NUS miminizes systematic errors which arise due to the lack of samplings by randomization. In this study, I report the real-time acquisition of 3D NMR data using NUS and maximum-entropy (MaxEnt) data processing. The real-time acquisition combined with NUS can reduce NMR measuring time much more. Compared with multidimensional decomposition (MDD) method, which was originally suggested by Jaravine and Orekhov (JACS 2006, 13421-13426), MaxEnt is faster at least several times and more suitable for the realtime acquisition. The designed sampling schedule of current study makes all the spectra during acquisition have the comparable resulting resolutions by MaxEnt. Therefore, one can judge the quality of spectra easily by examining the intensities of peaks. I report two cases of 3D experiments as examples with the simulated subdataset from experimental data. In both cases, the spectra having good qualitie for data analysis could be obtained only with 3% of original data. Its corresponding NMR measuring time was 8 minutes for 3D HNCO of ubiquitin.