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IEEE/PES Transmission and Distribution Conference and Exposition (T and D)

作者： Jain, Rishabh Lukic, Srdjan Lubkeman, David North Carolina State Univ FREEDM Syst Ctr Raleigh NC 27695 USA

ISBN: (纸本)9781538655832

This paper presents a multi-agent system (MAS) approach for automated service restoration for futuristic distribution systems with distributed energy resources (with/without microgrid forming capabilities). The proposed algorithm provides an architecture for interchangeable agent roles which is more robust and reliable compared to other approaches with rigid agent roles. The restoration algorithm is greedy (loosely based on distributed Prim's algorithm) to form the largest possible online sections. This helps improving system stability and makes it less susceptible to changing load/demand in the system by aggregation. The performance is demonstrated for a 33-bus distribution system with and without DERs. It is seen that algorithm always results in maximum possible loads be connected.

关键词： FLISR Multi-agent Systems Microgrids Resiliency IIID IEEE 1547 greedy algorithm Service Restoration Mixed Integer Linear Programming Smart Grid Self healing

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Recursive Dictionary-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第4期34卷 456-464页

作者： Kong Fanqiang Guo Wenjun Shen Qiu Wang Dandan College of Astronautics Nanjing University of Aeronautics and Astronautics

The sparse unmixing problem of greedy algorithms still remains a great challenge at finding an optimal subset of endmembers for the observed data from the spectral library,due to the usually high correlation of the spectral *** such circumstances,a novel greedy algorithm for sparse unmixing of hyperspectral data is presented,termed the recursive dictionary-based simultaneous orthogonal matching pursuit(RD-SOMP).The algorithm adopts a block-processing strategy to divide the whole hyperspectral image into several *** each iteration of the block,the spectral library is projected into the orthogonal subspace and renormalized,which can reduce the correlation of the spectral *** RD-SOMP selects a new endmember with the maximum correlation between the current residual and the orthogonal subspace of the spectral *** endmembers picked in all the blocks are associated as the endmember sets of the whole hyperspectral ***,the abundances are estimated using the whole hyperspectral data with the obtained endmember *** can be proved that RD-SOMP can recover the optimal endmembers from the spectral library under certain *** results demonstrate that the RD-SOMP algorithm outperforms the other algorithms,with a better spectral unmixing accuracy.

关键词： hyperspectral unmixing greedy algorithm simultaneous sparse representation sparse unmixing

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Offline-Enhanced Reduced Basis Method Through Adaptive Construction of the Surrogate Training Set

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JOURNAL OF SCIENTIFIC COMPUTING 2017年第2-3期73卷 853-875页

作者： Jiang, Jiahua Chen, Yanlai Narayan, Akil Univ Massachusetts Dartmouth Dept Math 285 Old Westport Rd N Dartmouth MA 02747 USA Univ Utah Sci Comp & Imaging SCI Inst 72 S Campus Dr Salt Lake City UT 84112 USA Univ Utah Dept Math 72 S Campus Dr Salt Lake City UT 84112 USA

The reduced basis method (RBM) is a popular certified model reduction approach for solving parametrized partial differential equations. One critical stage of the offline portion of the algorithm is a greedy algorithm, requiring maximization of an error estimate over parameter space. In practice this maximization is usually performed by replacing the parameter domain continuum with a discrete "training" set. When the dimension of parameter space is large, it is necessary to significantly increase the size of this training set in order to effectively search parameter space. Large training sets diminish the attractiveness of RBM algorithms since this proportionally increases the cost of the offline phase. In this work we propose novel strategies for offline RBM algorithms that mitigate the computational difficulty of maximizing error estimates over a training set. The main idea is to identify a subset of the training set, a "surrogate training set" (STS), on which to perform greedy algorithms. The STS we construct is much smaller in size than the full training set, yet our examples suggest that it is accurate enough to induce the solution manifold of interest at the current offline RBM iteration. We propose two algorithms to construct the STS: our first algorithm, the successive maximization method, is inspired by inverse transform sampling for non-standard univariate probability distributions. The second constructs an STS by identifying pivots in the Cholesky decomposition of an approximate error correlation matrix. We demonstrate the algorithm through numerical experiments, showing that it is capable of accelerating offline RBM procedures without degrading accuracy, assuming that the solution manifold has rapidly decaying Kolmogorov width.

关键词： Reduced basis method Surrogate parameter domain Cholesky decomposition Adaptivity greedy algorithm

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Continuous basis compressive time-delay estimation in overlapped echoes

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IET SIGNAL PROCESSING 2017年第5期11卷 566-571页

作者： Fereidoony, Foad Chamaani, Somayyeh Mirtaheri, Syed Abdullah Sebt, Mohammad Ali KN Toosi Univ Technol Elect & Comp Engn Tehran Iran

High-accuracy time-delay estimation is basically noted in several research areas. L1-minimisation is a compressive sensing (CS) approach which solves this problem with high resolution and accuracy in the case of spars signals. Band excluded orthogonal matching pursuit is another CS method which uses a greedy algorithm to retrieve time delays and has lower complexity compared with the L1-minimisation method;however, it is only applicable when the signals are well spaced or orthogonal. Moreover, both approaches are established on a discrete basis which inherently limits their accuracy for the constraint on the sampling rate of the system. To mitigate these challenges in this study, the authors first incorporate the L1-minimisation method in a greedy algorithm to achieve a high resolution in the discrete grid. In the next step, to overcome the limitation caused by the sampling rate and refine the obtained time delays, the algorithm is combined with a complex continuous basis pursuit (CCBP) by using a polar interpolation. Their simulation and experiment results show that the proposed combination of L1-minimisation-CCBP can recover time delays in very closely spaced echoes not only with high accuracy but also with low computational time and sampling rate.

关键词： compressed sensing echo delay estimation minimisation greedy algorithms signal sampling interpolation signal resolution iterative methods continuous basis compressive time-delay estimation overlapped echo High-accuracy time-delay estimation compressive sensing approach orthogonal matching pursuit CS method greedy algorithm system sampling rate discrete grid complex continuous basis pursuit polar interpolation L-1-minimisation-CCBP time delay recovery sparse signal

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Cut Pursuit: Fast algorithms to Learn Piecewise Constant Functions on General Weighted Graphs

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SIAM JOURNAL ON IMAGING SCIENCES 2017年第4期10卷 1724-1766页

作者： Landrieu, Loic Obozinski, Guillaume Univ Paris Est ENSG IGN LASTIG MATIS F-94160 St Mande France Univ Paris Est UPEM ESIEE Paris ENPCLIGMUMR 8049CNRS F-77455 Marne La Vallee 2 France

We propose working-set/greedy algorithms to efficiently solve problems penalized respectively by the total variation on a general weighted graph and its l(0) counterpart the Mumford Shah total level-set boundary size when the piecewise constant solutions have a small number of distinct level-sets;this is typically the case when the total level-set boundary size is small, which is encouraged by these two forms of penalization. Our algorithms exploit this structure by recursively splitting the level-sets of a piecewise-constant candidate solution using graph cuts. We obtain significant speed-ups over state-of-the-art algorithms for images that are well approximated with few level-sets.

关键词： working set total variation sparsity Mumford-Shah greedy algorithm

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Efficient Computation of MSE Lower Bounds via Matching Pursuit

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IEEE SIGNAL PROCESSING LETTERS 2017年第12期24卷 1798-1802页

作者： Shalom, Shahar Sar Tabrikian, Joseph Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

The classes of large-error bounds that are based on the covariance inequality, in both Bayesian and non-Bayesian approaches, are characterized as projection-based bounds. Tightening of bounds in these classes involves high computational complexity due to multidimensional optimization procedure. Consequently, projection-based large-error bounds have little popularity, while small-error bounds are frequently preferred, although they are not necessarily tight. In this letter, we first introduce a unified formulation for Bayesian and non-Bayesian projection-based lower bounds and set a general framework, which allows for their approximation via a greedy-based method. This framework is then used to propose the use of optimized orthogonal matching pursuit approach for computing projection-based large-error bounds. We analyze the complexity of the proposed algorithm and show that it is significantly lower than the complexity of the conventional approach. Finally, we apply the algorithm for the problem of multitone estimation and show that for fixed computational resources, the Weiss-Weinstein bound implemented with the proposed algorithm, provides a tighter bound compared to conventional approaches.

关键词： Barankin bound greedy algorithm matching pursuit mean-squared-error (MSE) bounds weiss-weinstein class

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Maximum coverage problem with group budget constraints

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2017年第3期34卷 725-735页

作者： Farbstein, Boaz Levin, Asaf Technion Fac Ind Engn & Management IL-32000 Haifa Israel

We study the maximum coverage problem with group budget constraints (MCG). The input consists of a ground set X, a collection of subsets of X each of which is associated with a combinatorial structure such that for every set , a cost can be calculated based on the combinatorial structure associated with , a partition of , and budgets , and B. A solution to the problem consists of a subset H of such that and for each , . The objective is to maximize . In our work we use a new and improved analysis of the greedy algorithm to prove that it is a -approximation algorithm, where is the approximation ratio of a given oracle which takes as an input a subset and a group and returns a set which approximates the optimal solution for . This analysis that is shown here to be tight for the greedy algorithm, improves by a factor larger than 2 the analysis of the best known approximation algorithm for MCG.

关键词： Maximum coverage greedy algorithm Approximation algorithms

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A MIP formulation and a heuristic solution approach for the bottling scheduling problem in the wine industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 105卷 136-145页

作者： Basso, Franco Varas, Mauricio Diego Port Univ Dept Ind Engn Santiago Chile

In this work, we address the bottling scheduling problem that arises in the wine industry when the packing requests from clients need to be allocated to the production lines. This problem also appears in a large variety of industries, but especially in packaged food companies. Based on the operations of a large Chilean winery we worked with, we developed a MIP model that exhibits industry-specific features such as different types of wine resources and oenological process constraints. This model can be reduced to an n job, m parallel machine scheduling problem, which is known to be NP-hard, so we developed a greedy heuristic algorithm in order to find a feasible bottling schedule in a reduced computing time. We show that the proposed solution approach is a very promising alternative to efficient MIP solvers like CPLEX. Particularly, the greedy heuristic is able to schedule all the jobs in 98% of the test instances and the computational times are very reasonable even for large industrial cases. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wine industry Scheduling MIP greedy algorithm

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Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data

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SEISMIC INSTRUMENTS 2018年第1期54卷 8-16页

作者： Kislov, K. V. Gravirov, V. V. Russian Acad Sci Inst Earthquake Predict Theory & Math Geophys Moscow Russia

The number of seismological studies based on artificial neural networks has been increasing. However, neural networks with one hidden layer have almost reached the limit of their capabilities. In the last few years, there has been a new boom in neuroinformatics associated with the development of third-generation networks, deep neural networks. These networks operate with data at a higher level. Unlabeled data can be used to pretrain the network, i.e., there is no need for an expert to determine in advance the phenomenon to which these data correspond. Final training requires a small amount of labeled data. Deep networks have a higher level of abstraction and produce fewer errors. The same network can be used to solve several tasks at the same time, or it is easy to retrain it from one task to another. The paper discusses the possibility of applying deep networks in seismology. We have described what deep networks are, their advantages, how they are trained, how to adapt them to the features of seismic data, and what prospects are opening up in connection with their use.

关键词： deep neural networks deep learning greedy algorithm seismic data multitask learning

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FFT formulations of adaptive Fourier decomposition

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2017年 324卷 204-215页

作者： Gao, You Ku, Min Qian, Tao Wang, Jianzhong Univ Macau Dept Math Macau Peoples R China Univ Aveiro Dept Math CIDMA Aveiro Portugal Sam Houston State Univ Dept Math & Stat Huntsville TX 77340 USA

Adaptive Fourier decomposition (AFD) has been found to be among the most effective greedy algorithms. AFD shows an outstanding performance in signal analysis and system identification. As compensation of effectiveness, the computation complexity is great, that is especially due to maximal selections of the parameters. In this paper, we explore the discretization of the 1-D AFD integration via with discrete Fourier transform (DFT), incorporating fast Fourier transform (FFT). We show that the new algorithm, called FFT-AFD, reduces the computational complexity from O (MN2) to O (MN log N), the latter being the same as FFT. Through experiments, we verify the effectiveness, accuracy, and robustness of the proposed algorithm. The proposed FFT-based algorithm for AFD lays a foundation for its practical applications. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fast Fourier transform Computational complexity Adaptive decomposition greedy algorithm Reproducing kernel Hilbert space

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