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 c...
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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 vari...
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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.
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 ...
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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.
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 algo...
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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.
This paper proposed two neighbor discovery schemes in the wireless D2D networks using directional antennas. The neighbor discovery process is modeled as a deterministic estimator learning automaton. pursuit algorithm ...
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This paper proposed two neighbor discovery schemes in the wireless D2D networks using directional antennas. The neighbor discovery process is modeled as a deterministic estimator learning automaton. pursuit algorithm and Generalized pursuit algorithm are used to further improve the efficiency of neighbor discovery. The nodes in the network adjust the direction of the directional antennas through learning the feedback given by the environment in the history discovery process. Finally, OPNET is used to simulate the proposed schemes, and the simulation results show that the proposed two schemes in this paper can effectively improve the efficiency of neighbor discovery. (C) 2021 Elsevier B.V. All rights reserved.
The study in sparse representation of signals and its applications has changed the field of signal processing into a quickly developing area. These studies have opened tremendous feasible research ideas. Compressed se...
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ISBN:
(纸本)9781538638644
The study in sparse representation of signals and its applications has changed the field of signal processing into a quickly developing area. These studies have opened tremendous feasible research ideas. Compressed sensing is one among the greatest beneficial field of these studies. Signal sparsity property introduced the idea of reconstruction of signals from very few prototype atoms of an overcomplete dictionary. The novel approach in dictionary learning process is K-SVD method. We propose a method to improve the sparse representation of signal by incorporating A* search algorithm in K-SVD. The usage of tree data structure has provided a lot of advantages including the introduction of auxiliary function, tree pruning techniques etc. Experimental results show that the proposed system provides more efficient and improved results than the conventional ones.
Optimization of fuzzy Maximum Power Point Tracking (MPPT) controller using Learning Automata (LA) algorithm is proposed in this paper. The optimal duty cycle of the DC-DC converter circuit is obtained using LA for var...
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Optimization of fuzzy Maximum Power Point Tracking (MPPT) controller using Learning Automata (LA) algorithm is proposed in this paper. The optimal duty cycle of the DC-DC converter circuit is obtained using LA for various environmental conditions through learning process. The fuzzy MPPT controller is developed using the information collected by LA through the learning process. The proposed model is developed and tested using MATLAB for standard test conditions of PV, constant temperature and varying irradiation level, constant irradiation and varying temperature level, and varying temperature and varying irradiation level. The results obtained using the proposed fuzzy MPPT are compared with the conventional Perturb and Observe (P&O) MPPT and variable step size Fuzzy MPPT based PV system. The experimental set up is developed and the test is conducted under different conditions for the solar PV system with P&O MPPT and the proposed LA Fuzzy MPPT. The results show that the proposed LA based Fuzzy MPPT method is more accurate and its tracking response is faster.
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. H...
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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.
Compressed sensing (CS) is a methodology allowing linear measurements much fewer in number than the length of the original signal vectors. This is made possible by using a measurement matrix which converts the origina...
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ISBN:
(纸本)9781509016792
Compressed sensing (CS) is a methodology allowing linear measurements much fewer in number than the length of the original signal vectors. This is made possible by using a measurement matrix which converts the original signal to a much shorter signal. For CS to work, it is necessary that the original signal is sufficiently sparse so that it can be reconstructed from the compressed signal. Hybrid cryptography combines symmetric and asymmetric cryptographies. In order to increase security, the symmetric keys used are transmitted to the receiver by asymmetric cryptography. In this study, the proposed character/text input is proposed to be sensed by compressive sensing using the method of orthogonal matching pursuit (OMP), and then to be encrypted by hybrid cryptography using a transform and amplitude-phase keys similarly to a 4f system of optical cryptography. The overall system achieves both data compression and encryption.
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 d...
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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.
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