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Yongsheng Zhu
School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China

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Journal article
Published: 23 September 2019 in Signal Processing
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Sparsity and reconstruction error are two main objectives to be optimized in sparse signal reconstruction. In this paper, sparse signals are reconstructed by optimizing these two objectives simultaneously. This reconstruction method mainly consists of three steps. First, a one-dimension-dominated method is used to find a uniformly distributed optimal compromise solution set between these two objectives. Second, the Iterative Half Thresholding method is employed to improve the sparsity. Third, a robust selection method is proposed to choose a final solution from the solution set. The proposed method is compared with eight sparse reconstruction algorithms on twelve sparse test instances. Experimental results show that the proposed algorithm is able to reconstruct both noisy and noiseless sparse signals. In addition, the effectiveness of the proposed algorithm is demonstrated in practical application instances.

ACS Style

Caitong Yue; Jing Liang; Boyang Qu; Yuhong Han; Yongsheng Zhu; Oscar D. Crisalle. A novel multiobjective optimization algorithm for sparse signal reconstruction. Signal Processing 2019, 167, 107292 .

AMA Style

Caitong Yue, Jing Liang, Boyang Qu, Yuhong Han, Yongsheng Zhu, Oscar D. Crisalle. A novel multiobjective optimization algorithm for sparse signal reconstruction. Signal Processing. 2019; 167 ():107292.

Chicago/Turabian Style

Caitong Yue; Jing Liang; Boyang Qu; Yuhong Han; Yongsheng Zhu; Oscar D. Crisalle. 2019. "A novel multiobjective optimization algorithm for sparse signal reconstruction." Signal Processing 167, no. : 107292.

Journal article
Published: 01 December 2017 in Energies
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The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

ACS Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Jingjing Liang; Ling Wang. Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm. Energies 2017, 10, 1991 .

AMA Style

Boyang Qu, Baihao Qiao, Yongsheng Zhu, Jingjing Liang, Ling Wang. Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm. Energies. 2017; 10 (12):1991.

Chicago/Turabian Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Jingjing Liang; Ling Wang. 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm." Energies 10, no. 12: 1991.