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Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 503-512页

作者： Li, Bei Sun, Ying Li, Gongfa Kong, Jianyi Jiang, Guozhang Jiang, Du Tao, Bo Xu, Shuang Liu, Honghai Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England

Aiming at the disadvantages of greedy algorithms in sparse solution, a modified adaptive orthogonal matching pursuit algorithm (MAOMP) is proposed in this paper. It is obviously improved to introduce sparsity and variable step size for the MAOMP. The algorithm estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations. Finally, the step size is adjusted to select atoms and approximate the true sparsity at different stages. The simulation results show that the algorithm which has proposed improves the recognition accuracy and efficiency comparing with other greedy algorithms.

关键词： pursuit algorithm Gesture recognition Pattern recognition Sparse representation Estimation

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An Efficient Parallel pursuit algorithm 8

An Efficient Parallel Pursuit Algorithm

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8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Ge, Hao Guo, Ying Li, Shenghong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781509007684

Parallel operation of learning automata (LA), which is firstly proposed by Thathachar and Arvind, is a promising mechanism that can reduce the convergence time without compromising accuracy, by utilizing the parallel nature of specific environments. Parallel operation of pursuit algorithm is the most fundamental parallel algorithm. However, from our perspective, the design of the parallel pursuit algorithm suffers several principle weaknesses, therefore the performance is expected to be further improved. In this paper, by introducing distributed probability updating, inertial exploration and optimistic initial values, a novel parallel pursuit algorithm is presented, with the purpose to overcome the weaknesses of existing algorithm and be more efficient. Comparison of the two algorithms with different parallel sizes is carried out in two benchmark environment. Simulation shows that the proposed algorithm outperforms the classical one.

关键词： learning automaton parallel operation pursuit algorithm

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Revised pursuit algorithm for Solving Non-stationary Linear Programming Problems on Modern Computing Clusters with Manycore Accelerators 2nd

Revised Pursuit Algorithm for Solving Non-stationary Linear ...

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2nd Russian Supercomputing Days Conference (RuSCDays)

作者： Sokolinskaya, Irina Sokolinsky, Leonid South Ural State Univ 76 Lenin Prospekt Chelyabinsk 454080 Russia

ISBN: (纸本)9783319556680;9783319556697

This paper is devoted to the new edition of the parallel pursuit algorithm proposed the authors in previous works. The pursuit algorithm uses Fejer's mappings for building pseudo-projection on polyhedron. The algorithm tracks changes in input data and corrects the calculation process. The previous edition of the algorithm assumed using a cube-shaped pursuit region with the number of K cells in one dimension. The total number of cells is K-n, where n is the problem dimension. This resulted in high computational complexity of the algorithm. The new edition uses a cross-shaped pursuit region with one cross-bar per dimension. Such a region consists of only n(K - 1) + 1 cells. The new algorithm is intended for cluster computing system with Xeon Phi processors.

关键词： Non-stationary linear programming problem Fejer's mappings pursuit algorithm Massive parallelism Cluster computing systems MIC architecture Intel Xeon Phi Native mode OpenMP

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Residential Demand Response using Q-Learning with pursuit algorithm for Action Selection

Residential Demand Response using Q-Learning with Pursuit Al...

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2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy, PESGRE 2023

作者： Rajeev, Amala S Jayaraj, Saritha Sundar, Sumod Ahamed, T P Imthias A.P.J Abdul Kalam Technological University Tkm College of Engineering Kerala Kollam India A.P.J Abdul Kalam Technological University Providence College of Engineering Kerala Alappuzha India

ISBN: (纸本)9798350310573

Demand response (DR) has been extensively explored in various works utilizing reinforcement learning (RL) techniques. However, the prevailing objective functions in these studies primarily focus on minimizing energy costs for consumers or reducing maximum utility demand, potentially overlooking consumer discomfort. It is crucial to acknowledge that different load scenarios can lead to varying levels of discomfort for consumers. This paper proposes a novel RL framework that incorporates the notion of discomfort into the decision-making process. To achieve this, we employ the pursuit algorithm, a highly efficient method for action selection during the learning phase. Furthermore, we conduct a comprehensive analysis to assess the impact of the convergence rate on the algorithm's performance. © 2023 IEEE.

关键词： Demand Response pursuit algorithm Reinforcement Learning Smart grid

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Comparison of pursuit and ε- Greedy algorithm for Load Scheduling under Real Time Pricing

Comparison of Pursuit and ε- Greedy Algorithm for Load Sche...

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IEEE International Conference on Power and Energy (PECon)

作者： Maqbool, S. D. Ahamed, T. P. Imthias Ali, S. Q. Pazheri, F. R. Malik, N. H. King Saud Univ Coll Engn EE Dept Saudi Aramco Chair Elect Power Riyadh 11421 Saudi Arabia

ISBN: (纸本)9781467350198;9781467350174

Demand Response (DR) is a useful tool to develop a balance between the available generation and loads under smart grid environment. There are various price based schemes to implement DR and flatten the load profile. Hence, for the benefit of customers, proper load scheduling is required to lower the usage of electricity during peak load periods in order to decrease the electricity cost. This work formulates load scheduling as multi stage decision making problem or Markov Decision Problem (MDP). Reinforcement learning (RL) has been used to solve many decision making problems under stochastic environment. epsilon- Greedy algorithm is the most popular exploration method used in RL. In this paper, pursuit algorithm is developed to achieve a balance between exploration and exploitation process of the RL. The performance of both the algorithms is compared which shows the supremacy of pursuit algorithm over epsilon- greedy algorithm.

关键词： Reinforcement Learning Demand Response pursuit algorithm Smart Grid Real Time Pricing

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SAR image reconstruction by expectation maximization based matching pursuit

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DIGITAL SIGNAL PROCESSING 2015年第1期37卷 75-84页

作者： Ugur, S. Anikan, O. Gurbuz, A. Cafer Meteksan Savunma Ankara Turkey Bilkent Univ Elect & Elect Engn Dept Ankara Turkey TOBB Univ Econ & Technol Dept Elect & Elect Engn Ankara Turkey

Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Synthetic Aperture Radar Expectation Maximization based Matching pursuit algorithm Compressed sensing Autofocus

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

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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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A strategy for controlling nonlinear systems using a learning automaton

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AUTOMATICA 2000年第10期36卷 1517-1524页

作者： Zeng, X Zhou, J Vasseur, C Univ Sci & Technol Lille Automat Lab I3D F-59655 Villeneuve Dascq France ENSAIT Lab GEMTEX F-59070 Roubaix 1 France

This paper presents an application of learning automaton (LA) for nonlinear system control. The proposed control strategy utilizes a learning automaton in which the reinforcement scheme is based on the pursuit algorithm interacting with a nonstationary environment. Modulated by an adaptive mechanism, the LA selects, at each control period, a local optimal action, which serves as input to the controlled system. During the control procedure, the system output value takes into account the changes occurring inside the system and provides reward/penalty responses to the learning automaton. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： learning automata nonlinear system control strategy pursuit algorithm non-stationary environment modulation of control period

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