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Image super-resolution using sparse coding over redundant dictionary based on effective image representations

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 26卷 50-65页

作者： Sajjad, Muhammad Mehmood, Irfan Baik, Sung Wook Sejong Univ Coll Elect & Informat Engn Seoul South Korea

Recent years have shown a growing research interest in the sparse-representation of signals. Signals are described through sparse linear combinations of signal-atoms over a redundant-dictionary. Therefore, we propose a novel super-resolution framework using an overcomplete-dictionary based on effective imagerepresentations such as edges, contours and high-order structures. This scheme recovers the vector of common sparse-representations between low-resolution and corresponding high-resolution imagepatches by solving the l(1)-regularized least-squared problem;subsequently, it reconstructs the HR output by multiplying it with the learned dictionary. The dictionary used in the proposed-technique contains more effective image-representations than those in previous approaches because it contains featuredescriptors such as edges, contours and motion-selective features. Therefore, the proposed-technique is more robust to various types of distortion. A saliency-map quickens this technique by confining the optimization-process to visually salient regions. Experimental analyses confirm the effectiveness of the proposed-scheme, and its quantitative and qualitative performance as compared with other state-of-the-art super-resolution algorithms. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Super-resolution Reconstruction Denoising Representation-coefficients Sparse-coding vercomplete-dictionary Visual-saliency pursuit algorithm

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The Development of Demand Elasticity Model for Demand Response in the Retail Market Environment

The Development of Demand Elasticity Model for Demand Respon...

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PowerTech, 2015 IEEE Eindhoven

作者： Babar, M. Nguyen, P. H. Cuk, V. Kamphuis, I. G. Eindhoven Univ Technol Dept Elect Engn Elect Energy Syst NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9781479976959

In the context of liberalized energy market, increase in distributed generation, storage and demand response has expanded the price elasticity of demand, thus causing the addition of uncertainty to the supply-demand chain of power system. In order to cope with the challenges of demand uncertainty under the unbundled electricity market, the concept of Market-based Control Mechanism (MCM) in retail market environment has been emerging. This paper presents the concept considering demand elasticity as an opportunity in retail market environment for inventing a new bid mechanism. This work formulates demand elasticity model as a Markov decision problem and implements pursuit algorithm as a machine learning technique to evaluate the price elasticity of demand by predicting the price. The performance of the algorithm is compared with the numerical calculation of price elasticity of demand for the given simulation settings.

关键词： Demand Response Demand Elasticity Electricity Market pursuit algorithm

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Zhang, JunQi Li, ZeZhou Kang, Qi Zhou, MengChu Tongji Univ Dept Comp Sci & Technol Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China New Jersy Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781479938407

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset pursuit algorithm

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The Development of Demand Elasticity Model for Demand Response in the Retail Market Environment

The Development of Demand Elasticity Model for Demand Respon...

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IEEE Eindhoven PowerTech Conference

作者： M. Babar P. H. Nguyen V. Cuk I. G. Kamphuis Department of Electrical Engineering Electrical Energy Systems Eindhoven the Netherlands

关键词： Demand Response Demand Elasticity Electricity Market pursuit algorithm

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

关键词： Learning Automata Optimal subset pursuit algorithm

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Learning Automata algorithms for Load Scheduling

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2013年第3期41卷 286-303页

作者： Ali, Syed Q. Parambath, Imthias Ahamed T. Malik, Nazar H. King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia

Utilities are employing various price-based demand response schemes to curtail the maximum demand on the system. For price-based demand response to succeed, better algorithms, dataset and critical studies analyzing the effect of different pricing schemes need to be done. This article models the load-scheduling problem from the consumers' perspective as a decision-making problem and proposes algorithms to learn the optimal schedule. This article provides a simulation framework with a generalized load pattern and a generalized tariff profile so that different tariffs and load profiles can be tested with different load-scheduling algorithms to design and evaluate different demand response policies. The framework is also generalized in terms of temporal granularity. Moreover, a parameter udc to account for user discomfort due to the delay in scheduling the load is also incorporated. The effect of this parameter on load scheduling is studied and guidelines to choose the same are presented. The performance of the proposed algorithms is tested using this framework. The effect of penetration (defined as the percentage of consumers participating in the demand response program) of scheduling algorithm on the maximum demand is also studied.

关键词： demand response -greedy learning automata maximum demand pursuit algorithm load-scheduling

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Online Adaptive algorithm for scheduling PEV Charging

Online Adaptive Algorithm for scheduling PEV Charging

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IEEE International Conference on Control Applications (CCA)

作者： Arif, Anmar I. Ahamed, T. P. Imthias Malik, N. H. King Saud Univ Coll Engn EE Dept Saudi Aramco Chair Elect Power Riyadh 11421 Saudi Arabia

ISBN: (纸本)9781479915590

Plug-in electric vehicles (PEVs) have been increasing in recent years and are expected to keep growing even more. PEVs are charged by using the electricity from the power grid, which leads to higher electricity demands. If PEVs are charged randomly without proper management, the charging might occur during peak periods. Therefore, the charging could cause severe problems to the power system. One way to mitigate this problem is for utilities to employ time dependent pricing such as Critical Peak Pricing or Real Time Pricing (RTP). This paper presents a Learning Automata (LA) algorithm for scheduling PEV under RTP. To train the algorithm and for a good initial schedule, it is used offline with the previous available data, then the algorithm is used online for the current data. It is shown that for a large fleet of PEVs, it might take a few days to learn the best schedule. However, even during this period, the results are better than unscheduled charging. The paper shows that with a proper scheduling algorithm, we can minimize the charging cost of a large number of PEVs.

关键词： Demand Response Learning Automata Online algorithm Plug-in Electric Vehicle pursuit algorithm Real Time Pricing

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MATCHING pursuit WITH STOCHASTIC SELECTION

MATCHING PURSUIT WITH STOCHASTIC SELECTION

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20th European Signal Processing Conference (EUSIPCO)

作者： Peel, Thomas Emiya, Valentin Ralaivola, Liva Anthoine, Sandrine Aix Marseille Univ CNRS LIF 39 Rue Joliot Curie F-13453 Marseille France Aix Marseille Univ CNRS LATP F-13453 Marseille France

ISBN: (纸本)9781467310680

In this paper, we propose a Stochastic Selection strategy that accelerates the atom selection step of Matching pursuit. This strategy consists of randomly selecting a subset of atoms and a subset of rows in the full dictionary at each step of the Matching pursuit to obtain a sub-optimal but fast atom selection. We study the performance of the proposed algorithm in terms of approximation accuracy (decrease of the residual norm), of exact-sparse recovery and of audio declipping of real data. Numerical experiments show the relevance of the approach. The proposed Stochastic Selection strategy is presented with Matching pursuit but applies to any pursuit algorithms provided that their selection step is based on the computation of correlations.

关键词： Sparsity pursuit algorithm Stochastic Procedure

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PHYSICS-DRIVEN STRUCTURED COSPARSE MODELING FOR SOURCE LOCALIZATION

PHYSICS-DRIVEN STRUCTURED COSPARSE MODELING FOR SOURCE LOCAL...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Nam, Sangnam Gribonval, Remi INRIA Rennes Bretagne Atlantique Rennes France

ISBN: (纸本)9781467300469

Cosparse modeling is a recent alternative to sparse modeling, where the notion of dictionary is replaced by that of an analysis operator. When a known analysis operator is well adapted to describe the signals of interest, the model and associated algorithms can be used to solve inverse problems. Here we show how to derive an operator to model certain classes of signals that satisfy physical laws, such as the heat equation or the wave equation. We illustrate the approach on an acoustic inverse problem with a toy model of wave propagation and discuss its potential extensions and the challenges it raises.

关键词： Sparsity cosparsity structured cosparsity dictionary analysis operator pursuit algorithm

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Compressive Sensing SAR Image Reconstruction Based on Bayesian Framework and Evolutionary Computation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2011年第7期20卷 1904-1911页

作者： Wu, Jiao Liu, Fang Jiao, L. C. Wang, Xiaodong Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Compressive sensing (CS) is a theory that one may achieve an exact signal reconstruction from sufficient CS measurements taken from a sparse signal. However, in practical applications, the transform coefficients of SAR images usually have weak sparsity. Exactly reconstructing these images is very challenging. A new Bayesian evolutionary pursuit algorithm (BEPA) is proposed in this paper. A signal is represented as the sum of a main signal and some residual signals, and the generalized Gaussian distribution (GGD) is employed as the prior of the main signal and the residual signals. BEPA decomposes the residual iteratively and estimates the maximum a posteriori of the main signal and the residual signals by solving a sequence of subproblems to achieve the approximate CS reconstruction of the signal. Under the assumption of GGD with the parameter 0 < p < 1, the evolutionary algorithm (EA) is introduced to CS reconstruction for the first time. The better reconstruction performance can be achieved by searching the global optimal solutions of subproblems with EA. Numerical experiments demonstrate that the important features of SAR images (e.g., the point and line targets) can be well preserved by our algorithm, and the superior reconstruction performance can be obtained at the same time.

关键词： Compressive sensing (CS) evolutionary algorithm maximum a posteriori estimation pursuit algorithm

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