Recently, the integrated power communication network has gained considerable attention, because of new differentiated business. To improve the energy efficiency (EE) for information acquisition services, reconfigurabl...
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Recently, the integrated power communication network has gained considerable attention, because of new differentiated business. To improve the energy efficiency (EE) for information acquisition services, reconfigurable intelligent surface (RIS) is proposed. Under the constraints of the minimum rate of each user, the maximum transmit power limit of the base station and the unit modulus constraint of the phase angle of RIS, this paper aims for maximizing EE in RIS-aided multiple-input-single-output (MISO) systems. Firstly, the maximum signal-to-interference-noise ratio (SINR), the total power consumption of the system and the phase matrix of RIS are analyzed and derived, and then the optimization problem is established with the beamforming and transmission power of the base station transmitting multi-user as variables. Thirdly, in order to solve the optimization problem, this paper proposes to use the maximum ratio to send pre-coding to maximize the SINR received by users, and uses an improved sine and cosine optimization algorithm to optimize the phase matrix of RIS. Finally, a scheme of alternating iterative optimization of phase matrix and power is designed. Simulation results show that the proposed algorithm is very effective in improving the energy efficiency of the system. Compared with the traditional relay amplification scheme, the improved SCA optimization scheme achieves about 30% improvement in energy efficiency, which confirms the feasibility of the proposed method in improving the energy efficiency of MISO system.
Aiming at the problem that the traditional filtering method will filter out some useful signals when extracting weak fault features of rotating machinery, resulting in the loss of characteristic signals, a method for ...
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Aiming at the problem that the traditional filtering method will filter out some useful signals when extracting weak fault features of rotating machinery, resulting in the loss of characteristic signals, a method for extracting weak fault features based on sin-cosinealgorithm (SCA) is proposed. Combining the sensitivity of kurtosis to impact signals and correlation coefficients to interference noise, the paper proposes a new stochastic resonance (SR) performance evaluation index-weighted power spectrum kurtosis (WPSK), which solves the shortcoming of the traditional evaluation index that the fault frequency needs to be known in advance. The structural parameters of the SR are optimized by the SCA to improve the “resonance” effect. The SCA-based SR method is applied to the weak feature extraction of faulty bearings and compared with the SR model of particle swarm optimization, the results show that when the bearing inner-race fails, the value of WPSK increases by 33.5 %, and when the outer-race fails, the value of WPSK increases by 44.1 %.
Consider an unrelated parallel machine scheduling problems with machine eligibility, where each processed workpiece can only be arranged on specific machines for processing. In this paper, we propose a particle swarm ...
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ISBN:
(纸本)9798350332162
Consider an unrelated parallel machine scheduling problems with machine eligibility, where each processed workpiece can only be arranged on specific machines for processing. In this paper, we propose a particle swarm optimization (PSO) algorithm which introduces an elite reverse learning strategy and sine and cosine optimization algorithm to minimize the maximum completion time. In this algorithm, the elite reverse learning strategy is introduced heuristically to provide better feasible solutions for the algorithm randomization, thus accelerating the convergence rate. In addition, the sine and cosine optimization algorithm is introduced in the particle iteration process to update the velocity vector of the particle swarm, so as to enhance the diversity of the population. The effectiveness and advantage of the new PSO in solving the problem are verified by simulation experiments and comparation with other algorithms.
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