• 제목/요약/키워드: Sparse matrix

검색결과 253건 처리시간 0.027초

HYBRID REORDERING STRATEGIES FOR ILU PRECONDITIONING OF INDEFINITE SPARSE MATRICES

  • Lee Eun-Joo;Zgang Jun
    • Journal of applied mathematics & informatics
    • /
    • 제22권1_2호
    • /
    • pp.307-316
    • /
    • 2006
  • Incomplete LU factorization preconditioning techniques often have difficulty on indefinite sparse matrices. We present hybrid reordering strategies to deal with such matrices, which include new diagonal reorderings that are in conjunction with a symmetric nondecreasing degree algorithm. We first use the diagonal reorderings to efficiently search for entries of single element rows and columns and/or the maximum absolute value to be placed on the diagonal for computing a nonsymmetric permutation. To augment the effectiveness of the diagonal reorderings, a nondecreasing degree algorithm is applied to reduce the amount of fill-in during the ILU factorization. With the reordered matrices, we achieve a noticeable improvement in enhancing the stability of incomplete LU factorizations. Consequently, we reduce the convergence cost of the preconditioned Krylov subspace methods on solving the reordered indefinite matrices.

낮은 계수 행렬의 Compressed Sensing 복원 기법 (Compressed Sensing of Low-Rank Matrices: A Brief Survey on Efficient Algorithms)

  • 이기륭;예종철
    • 대한전자공학회논문지SP
    • /
    • 제46권5호
    • /
    • pp.15-24
    • /
    • 2009
  • Compressed sensing은 소수의 선형 관측으로부터 sparse 신호를 복원하는 문제를 언급하고 있다. 최근 벡터 경우에서의 성공적인 연구 결과가 행렬의 경우로 확장되었다. Low-rank 행렬의 compressed sensing은 ill-posed inverse problem을 low-rank 정보를 이용하여 해결한다. 본 문제는 rank 최소화 혹은 low-rank 근사의 형태로 나타내질 수 있다. 본 논문에서는 최근 제안된 여러 가지 효율적인 알고리즘에 대한 survey를 제공한다.

Analysis Land-use Changes of the Suomo Basin Based on Remote Sensing Images

  • Chen, Junfeng
    • 대한원격탐사학회:학술대회논문집
    • /
    • 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
    • /
    • pp.702-707
    • /
    • 2002
  • Three periods of land-use maps of the Suomo Basin were drawn from topographic maps (1970a) and Landsat TM/ETM images (1986a and 1999a). The area of each kind of land use was calculated from the three maps. From 1970 to 1999, the area of forestland decreased 17%, the area of sparse forestland increased 8%, and the area of grassland increased 10%. The transferring trend of the land-use is that forestland turned into sparse forestland and brush land, and the brush land degenerated into grassland based on the transferring matrixes from 1970 to 1986, and from 1986 to 1999. According to the local government record and statistical data, forest cover rate had been increasing from 1970 to 1998, but the amount of growing stock had been declining. From 1957 to 1998, the amount of growing stock declined from 423m$^3$/ha to 177m$^3$/ha.

  • PDF

Vehicle Image Recognition Using Deep Convolution Neural Network and Compressed Dictionary Learning

  • Zhou, Yanyan
    • 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%.

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO-OFDM systems

  • Singh, Davinder;Sarin, Rakesh Kumar
    • ETRI Journal
    • /
    • 제41권3호
    • /
    • pp.298-307
    • /
    • 2019
  • This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

랜덤워크 기법을 위한 GPU 기반 희소행렬 벡터 곱셈 방안에 대한 성능 평가 (GPU-based Sparse Matrix-Vector Multiplication Schemes for Random Walk with Restart: A Performance Study)

  • 유재서;배홍균;강석원;유용승;박영준;김상욱
    • 한국정보처리학회:학술대회논문집
    • /
    • 한국정보처리학회 2020년도 추계학술발표대회
    • /
    • pp.96-97
    • /
    • 2020
  • 랜덤워크 기반 노드 랭킹 방식 중 하나인 RWR(Random Walk with Restart) 기법은 희소행렬 벡터 곱셈 연산과 벡터 간의 합 연산을 반복적으로 수행하며, RWR 의 수행 시간은 희소행렬 벡터 곱셈 연산 방법에 큰 영향을 받는다. 본 논문에서는 CSR5(Compressed Sparse Row 5) 기반 희소행렬 벡터 곱셈 방식과 CSR-vector 기반 희소행렬 곱셈 방식을 채택한 GPU 기반 RWR 기법 간의 비교 실험을 수행한다. 실험을 통해 데이터 셋의 특징에 따른 RWR 의 성능 차이를 분석하고, 적합한 희소행렬 벡터 곱셈 방안 선택에 관한 가이드라인을 제안한다.

Fast Binary Block Inverse Jacket Transform

  • Lee Moon-Ho;Zhang Xiao-Dong;Pokhrel Subash Shree;Choe Chang-Hui;Hwang Gi-Yean
    • Journal of electromagnetic engineering and science
    • /
    • 제6권4호
    • /
    • pp.244-252
    • /
    • 2006
  • A block Jacket transform and. its block inverse Jacket transformn have recently been reported in the paper 'Fast block inverse Jacket transform'. But the multiplication of the block Jacket transform and the corresponding block inverse Jacket transform is not equal to the identity transform, which does not conform to the mathematical rule. In this paper, new binary block Jacket transforms and the corresponding binary block inverse Jacket transforms of orders $N=2^k,\;3^k\;and\;5^k$ for integer values k are proposed and the mathematical proofs are also presented. With the aid of the Kronecker product of the lower order Jacket matrix and the identity matrix, the fast algorithms for realizing these transforms are obtained. Due to the simple inverse, fast algorithm and prime based $P^k$ order of proposed binary block inverse Jacket transform, it can be applied in communications such as space time block code design, signal processing, LDPC coding and information theory. Application of circular permutation matrix(CPM) binary low density quasi block Jacket matrix is also introduced in this paper which is useful in coding theory.

저전력 무선 생체신호 모니터링을 위한 심전도/근전도/뇌전도의 압축센싱 연구 (Study on Compressed Sensing of ECG/EMG/EEG Signals for Low Power Wireless Biopotential Signal Monitoring)

  • 이욱준;신현철
    • 전자공학회논문지
    • /
    • 제52권3호
    • /
    • pp.89-95
    • /
    • 2015
  • 무선 헬스케어 서비스에서 생체신호 모니터링 시스템의 전력소모를 효과적으로 감소시킬 수 있는 압축센싱 기법을 다양한 생체신호에 적용하여 압축률을 비교하였다. 압축센싱 기법을 이용하여 일반적인 심전도, 근전도, 뇌전도 신호의 압축과 복원을 수행하였고, 이를 통해 복원된 신호와 원신호를 비교함으로써, 압축센싱의 유효성을 판단하였다. 유사랜덤 행렬을 사용하여 실제 생체신호를 압축하였으며, 압축된 신호는 Block Sparse Bayesian Learning(BSBL) 알고리즘을 사용하여 복원하였다. 가장 산제된 특성을 가지는 근전도 신호의 최대 압축률이 10배로 확인되어 가장 높았으며, 심전도 신호의 최대 압축률은 5배였다. 가장 산제된 특성이 작은 뇌전도 신호의 최대 압축률은 4배였다. 연구된 심전도, 근전도, 뇌전도 신호의 압축률은 향후 압축센싱을 적용한 무선 생체신호 모니터링 회로 및 시스템 개발시 유용한 기초자료로 활용될 수 있다.

멀티코어 CPU를 갖는 공유 메모리 구조의 대규모 병렬 유한요소 코드에 대한 설계 고려 사항 (Design Considerations on Large-scale Parallel Finite Element Code in Shared Memory Architecture with Multi-Core CPU)

  • 조정래;조근희
    • 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.

희소 행렬의 특성을 이용하여 효율적인 등화기 설계법이 적용된 WCDMA 무선 신호 분석기 구현 (Implementation of WCDMA Air Protocol Analyzer with An Effective Equalizer Design using Characteristic of Sparse Matrix)

  • 신창의;최승원
    • 디지털산업정보학회논문지
    • /
    • 제9권1호
    • /
    • pp.111-118
    • /
    • 2013
  • This paper presents implementation of Air protocol analyzer and physical layer design algorithm. The analyzer is a measurement system providing real-time analysis of wireless signals between User Equipment (UE) and Node-B. The implemented system proposed in this paper consists of Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The waveform of Wideband Code Division Multiple Access (WCDMA) has been selected for verification of the proposed system. We designed the analyzer using equalizer algorithm and rake-receiver algorithm. Among various algorithms of designing the equalizer, we have chosen Linear Minimum Mean Square Error (LMMSE) equalizer that uses the inverse of channel matrix. Since the LMMSE equalizer uses the inverse channel matrix, it suffers from a large amount of computational load, while it outperforms most conventional equalizers. In this paper, we introduce an efficient procedure of reducing the computational load required by LMMSE equalizer-based receiver.