• Smart Structures and Systems
• /
• 제25권3호
• /
• pp.369-384
• /
• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• 한국컴퓨터정보학회논문지
• /
• 제29권1호
• /
• pp.11-19
• /
• 2024

멀티에이전트는 전장 교전 상황, 무인 운송 차량 등 다양한 실제 협동 환경에 사용될 수 있다. 전장 교전 상황에서는 도메인 정보의 제한으로 즉각적인 보상(Dense Reward) 설계의 어려움이 있어 명백한 희소 보상(Sparse Reward)으로 학습되는 상황을 고려해야 한다. 본 논문에서는 전장 교전 상황에서의 아군 에이전트 간 협업 가능성을 확인하며, 희소 보상 환경인 Multi-Robot Warehouse Environment(RWARE)를 활용하여 유사한 문제와 평가 기준을 정의하고, 강화학습 라이브러리인 Ray RLlib의 QMIX 알고리즘을 사용하여 학습 환경을 구성한다. 정의한 문제에 대해 QMIX의 Agent Network를 개선하고 Random Network Distillation(RND)을 적용한다. 이를 통해 에이전트의 부분 관측값에 대한 패턴과 시간 특징을 추출하고, 에이전트의 내적 보상(Intrinsic Reward)을 통해 희소 보상 경험 획득 개선이 가능함을 실험을 통해 확인한다.

• 대한수학회논문집
• /
• 제11권4호
• /
• pp.1175-1185
• /
• 1996

We study the computation of sparse null bases of equilibrium matrices in the context of structural optimization and incompressible fluid flow. In our approach we emphasize the parallel computatin and examine the applications. New block decomposition and node ordering schemes are suggested, and numerical examples are considered.

• Journal of applied mathematics & informatics
• /
• 제9권2호
• /
• pp.519-529
• /
• 2002

We investigate implementation of the determinantal representation of generalized inverses for complex and rational matrices in the symbolic package MATHEMATICA. We also introduce an implementation which is applicable to sparse matrices.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권10호
• /
• pp.5006-5022
• /
• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• Smart Structures and Systems
• /
• 제16권4호
• /
• pp.607-621
• /
• 2015

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node's size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

• 대한토목학회논문집
• /
• 제11권4호
• /
• pp.143-150
• /
• 1991

이 논문은 대규모인 약 2,000점(미지수 약 4,000개)의 평면 측지망을 조정할 수 있는 프로그램을 개발하는데 목적이 있다. 데이터의 저장 및 관리에는 희박행렬(sparse matrix)의 기법이 사용되었으며, 관측방정식에는 RR(C)U (Row-Wise Representation Complete Unodered)방식, 정규방정식에는 RR(U)U(Row-Wise Representation Upper Unodered) 방식을 도입하고 해법에는 수정 Cholesky법을 적용하였다. PC 386에서 개발된 이 프로그램은 정밀 2차 기준점망인 테스트망에 적용되었으며, 2차원 배열을 사용한 Cholesky 분해법 및 직교분해법을 채용한 프로그램과의 상대적인 비교분석이 이루어졌다. 연구의 결과에서는 희박행렬의 기법이 기억용량의 면에서 뿐만 아니라 처리시간에 있어서도 극히 효과적인 기법임을 보여주고 있다.

• 한국전자파학회논문지
• /
• 제29권7호
• /
• pp.560-565
• /
• 2018

기존에 제안된 SPICE(Sparse Iterative Covariance-based Estimation) 알고리즘은 시간영역에서 구현되며 공분산 행렬에 sparse recovery 기법을 적용함으로써 표적 방위각을 추정한다. 본 논문은 기존의 시간영역에서 다루던 SPICE 알고리즘을 주파수 영역으로 확장함으로써 주파수 영역에서도 구현 가능함을 보여준다. 또한 기존의 주파수 영역 표적 방위각 추정 알고리즘의 성능에 비하여 제안된 알고리즘의 성능이 우수함을 보여준다.

• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2468-2478
• /
• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

• 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.