• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제5권1호
• /
• pp.85-100
• /
• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. Finally, due to the blocking effect, it will be effective for ill-conditioned problems. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditioner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to 1024 x 1024, and for an ill-conditioned matrix from the shell problem from the Harwell-Boeing collection. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications. The results show that Multi-Color Block SOR and ILU(0) with Multi-Color ordering give the best performances for the finite difference matrices and for the shell problem only the Multi-Color Block SOR converges.

• Journal of Information Processing Systems
• /
• 제17권2호
• /
• pp.411-425
• /
• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Communications for Statistical Applications and Methods
• /
• 제21권4호
• /
• pp.349-361
• /
• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• Journal of the Korean Statistical Society
• /
• 제33권3호
• /
• pp.313-321
• /
• 2004

We consider the problem of testing cell probabilities in sparse multinomial data. Aerts et al. (2000) presented T=${{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2$ as a test statistic with the local least square polynomial estimator ${{p}_{i}}^{*}$, and derived its asymptotic distribution. The local least square estimator may produce negative estimates for cell probabilities. The local maximum likelihood polynomial estimator ${{\hat{p}}_{i}}$, however, guarantees positive estimates for cell probabilities and has the same asymptotic performance as the local least square estimator (Baek and Park, 2003). When there are cell probabilities with relatively much different sizes, the same contribution of the difference between the estimator and the hypothetical probability at each cell in their test statistic would not be proper to measure the total goodness-of-fit. We consider a Pearson type of goodness-of-fit test statistic, $T_1={{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2/p_{i}$ instead, and show it follows an asymptotic normal distribution. Also we investigate the asymptotic normality of $T_2={{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2/p_{i}$ where the minimum expected cell frequency is very small.

• 응용통계연구
• /
• 제29권1호
• /
• pp.27-39
• /
• 2016

본 논문은 다차원의 시계열 자료 분석에서 효율적인 희박벡터자기회귀모형에서의 모수 추정에 대해서 연구한다. 희박벡터자기회귀모형은 영에 가까운 계수를 정확이 영으로 둠으로써 희박성을 확보한다. 따라서 변수 선택과 모수 추정을 한꺼번에 할 수 있는 lasso를 이용한 방법론을 희박벡터자기회귀모형의 추정에 쓸 수 있다. 하지만 Davis 등(2015)에서는 모의실험을 통해 일반적인 lasso의 경우 영이아닌 계수를 참값보다 훨씬 더 많이 찾아 희박성에 약점이 있음을 보고하였다. 이에 따라 본 연구는 희박벡터자기회귀모형에 adaptive lasso를 이용하면 일반 lasso보다 희박성을 비롯한 전반적인 모수의 추정이 매우 유의하게 개선됨을 보인다. 또한 adaptive lasso에서 쓰이는 튜닝 모수들에 대한 선택도 아울러 논의한다.

• 방송공학회논문지
• /
• 제10권2호
• /
• pp.166-173
• /
• 2005

본 논문에서는 직교 주파수 분할 다중 접속 (orthogonal frequency division multiplexing, OFDM) 시스템에서 MP (matching pursuit) 알고리듬을 이용하는 이동 채널 추정법을 제안한다. OFDM 시스템에서 기존의 채널추정 알고리듬으로 쓰이는 LS (least square) 알고리듬은 잡음의 영향으로 채널 추정 오류의 가능성을 가지고 있다. 본 논문에서는 MP 알고리듬을 이용하여 스파스(sparse)형태의 채널을 추정함으로써 다중경로 신호가 없다고 가정되는 시간 구간에서 발생될 수 있는 잡음에 의한 영향을 줄인다. 그리고 연속으로 전송되는 파일럿 정보를 이용하여 변화하는 채널을 추정한다. 64QAM,그리고 이동 다중 경로 페이딩 채널에 대해서 심볼 오율을 측정하여 제안된 알고리듬과 LS알고리듬의 성능을 비교한다.

• 한국전산구조공학회논문집
• /
• 제30권2호
• /
• pp.127-135
• /
• 2017

멀티코어 CPU와 BLAS, LAPACK을 구현한 최적 수치라이브러리, 직접 희소 솔버의 대중화 등 PC나 워크스테이션 수준에서도 대규모 유한요소 모델을 해석할 수 있도록 컴퓨팅 환경이 급속도로 변화되었다. 이 논문에서는 멀티코어 CPU를 갖는 공유 메모리 구조에 대한 병렬 유한요소 프로그램 설계시 고려사항으로 (1) 최적화된 수치라이브러리의 사용, (2) 최신 직접 희소 솔버의 사용, (3) OpenMP를 이용한 병렬 요소 강성 행렬의 계산, (4) 희소행렬 저장방식의 일종인 triplet을 이용한 어셈블 기법 등을 제시하였다. 또한 대규모 수치모델을 통해 많은 시간이 소요되는 작업을 기준으로 병렬화 효과를 검토하였다.

• Journal of the Korean Data and Information Science Society
• /
• 제14권2호
• /
• pp.303-311
• /
• 2003

We consider the problem of testing cell probabilities in sparse multinomial data. Aerts, et al.(2000) presented $T_1=\sum\limits_{i=1}^k(\hat{p}_i-p_i)^2$ as a test statistic with the local polynomial estimator $(\hat{p}_i$, and showed its asymptotic distribution. When there are cell probabilities with relatively much different sizes, the same contribution of the difference between the estimator and the hypothetical probability at each cell in their test statistic would not be proper to measure the total goodness-of-fit. We consider a Pearson type of goodness-of-fit test statistic, $T=\sum\limits_{i=1}^k(\hat{p}_i-p_i)^2/p_i$ instead, and show it follows an asymptotic normal distribution.

• 대한수학회보
• /
• 제55권6호
• /
• pp.1791-1809
• /
• 2018

Let T be the singular integral operator with nonsmooth kernel which was introduced by Duong and McIntosh, and $T_q(q{\in}(1,{\infty}))$ be the vector-valued operator defined by $T_qf(x)=({\sum}_{k=1}^{\infty}{\mid}T\;f_k(x){\mid}^q)^{1/q}$. In this paper, by proving certain weak type endpoint estimate of L log L type for the grand maximal operator of T, the author establishes some quantitative weighted bounds for $T_q$ and the corresponding vector-valued maximal singular integral operator.

• ALGAE
• /
• 제25권3호
• /
• pp.141-153
• /
• 2010

Seagrass epiphyte blooms potentially have important economic and ecological consequences in Tampa Bay, one of the Gulf of Mexico's largest estuaries. As part of a Tampa Bay pilot study to monitor the impact of environmental stresses, precise characterization of epiphyte diversity is required for efficient management of affected resources. Thus, epiphyte diversity may be used as a rational basis for assessment of ecosystem health. In May 2001, epiphytic species encompassing green, brown and red macroalgae were manually collected from dense and sparse seagrass beds of Thalassia testudinum and Syringodium filiforme. A total of 20 macroalgal epiphytes, 2 Chlorophyta, 2 Phaeophyta, and 16 Rhodophyta, were found on T. testudinum and S. filiforme seagrass at the four sampling sites (Bishop Harbor, Cockroach Bay, Feather Sound, and Mariposa Key). The Rhodophyta, represented by 16 species, dominated the numbers of species. Among them, the thin-crusted Hydrolithon farinosum was the most commonly found epiphyte on seagrass leaves. Species number, as well as species frequency of epiphytes, is higher at dense seagrass sites than sparse seagrass sites. Four attachment patterns of epiphytes can be classified according to cortex and rhizoid development: 1) creeping, 2) erect, 3) creeping & erect, and 4) erect & holding. The creeping type is characterized by an encrusting thallus without a rhizoid or holdfast base. Characteristics of the erect type include a filamentous thallus with or without a cortex, and a rhizoid or holdfast base. The creeping and erect type is characterized by a filamentous thallus with a cortex and rhizoid. A filamentous thallus with a cortex, holdfast base, and host holding branch is characteristics of the erect and holdfast attachment type. This study characterized each species found on the seagrass for epiphyte identification.