For a magnetic coupling resonant wireless power transfer (MCR-WPT) system, the most challenging design issue is to maintain the reasonable transfer efficiency and the output power, especially over varying transmitting...
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For a magnetic coupling resonant wireless power transfer (MCR-WPT) system, the most challenging design issue is to maintain the reasonable transfer efficiency and the output power, especially over varying transmitting distances. To address this issue, this study proposes an adaptive impedance matching (AIM) system with a fast optimization control algorithm for the MCR-WPT system. By dynamically monitoring system input impedance variations at a given frequency, the matching system can be automatically activated. The matching precision is enabled by combining an amplitude-phase measurement circuit, a Pi-type impedance matching (IM) network, and the local optimization algorithm. Experimental comparisons show a significant improvement in the transfer efficiency. The maximum improvement is over 32.6%. The results also show that the local optimization algorithm helps the MCR-WPT system to effectively overcome the efficiency challenges caused by parasitic parameters, resulting in a maximum improvement of 19.8%. The stability of the output power is also significantly improved, with a 27.8% reduction in oscillation amplitude compared to the scenario without the local optimization algorithm, and a 60.0% reduction compared to the scenario without the IM network. The output power range (1.55 -2.21 W) could meet the power requirements of most implantable medical devices. Moreover, this system adopts a control algorithm of tracking the theoretically optimal matching solution through Microcontroller Unit (MCU) computation, leading to a notable enhancement in matching accuracy.
In high speed processing system, using common optimization algorithm to solve minimum entropy, it is slow and could produce “explosive” with computation dimension increasing. According to this problem, this paper ca...
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ISBN:
(纸本)9781457721205
In high speed processing system, using common optimization algorithm to solve minimum entropy, it is slow and could produce “explosive” with computation dimension increasing. According to this problem, this paper carry out an improved Hopfield neural network optimization algorithm, introducing the punish operator, and make it applied to the minimum entropy value calculation. Calculation results show that Hopfield neural network can efficiently solve the minimum entropy of constraint condition, high speed and cann’t happen “explosive”.
In order to improve the accuracy and the convergence speed of the sphericity error, an improved teaching and learning algorithm is proposed to evaluate the sphericity error. Based on the basic teaching-learning-based ...
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In order to improve the accuracy and the convergence speed of the sphericity error, an improved teaching and learning algorithm is proposed to evaluate the sphericity error. Based on the basic teaching-learning-based optimization, the initial solution quality is improved by logistic chaotic initialization;At the end of each iteration, the interpolation algorithm is applied to the global optimal solution to further improve the search accuracy of the algorithm. Finally, one group of sphericity error algorithm though the measurement data in the related literature is verified the effectiveness of the ITLBO, the test result show that the ITLBO algorithm has advantages in the calculating accuracy and iteration convergence speed, and it is very suitable for the application in the sphericity error evaluation.
In this paper,a novel method for the synthesis of shaped-beam antenna arrays by combining matrix resolution with particle swarm optimization（PSO）algorithm is *** amplitude and phase distribution of the antenna array...
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In this paper,a novel method for the synthesis of shaped-beam antenna arrays by combining matrix resolution with particle swarm optimization（PSO）algorithm is *** amplitude and phase distribution of the antenna array are achieved firstly by matrix resolution,and then the results are used as the initial values of PSO algorithm in the following optimization to obtain a wide angle shaped-beam radiation *** examples,an 18-element antenna array and a 24-element antenna array are *** results show that the optimization algorithm can improve the shaped-beam precision and it is an effective method for solving the wide angle shaped-beam problem when the element number of the antenna array is small.
This paper proposed an optimization algorithm for exit choice for the *** algorithm aims at minimizing the overall evacuation time from the global *** basic concept of the algorithm is balancing the number of pedestri...
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This paper proposed an optimization algorithm for exit choice for the *** algorithm aims at minimizing the overall evacuation time from the global *** basic concept of the algorithm is balancing the number of pedestrians selecting each exit to make all the evacuees leave the building simultaneously as far as *** a theater with six exits as an example,the FDS+EVAC software is used to simulate the evacuation *** results of pedestrians;exit choice and the evacuation time with and without optimization are compared and *** shows that adopting the optimized choice strategy can greatly improves the utilization of the exits during the evacuation,and all of the evacuees leave the building almost simultaneously.
This article deals with the multi-mode multi-project inverse scheduling problem of the turbine assembly workshop in a Chinese electric power station equipment manufacturing firm considering some unexpected disturbance...
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This article deals with the multi-mode multi-project inverse scheduling problem of the turbine assembly workshop in a Chinese electric power station equipment manufacturing firm considering some unexpected disturbances in the assembly process, such as materials delay, equipment failure and parts rework, etc. The objective is to optimize the assembly cost in inverse scheduling under the constraints of due date, worker modes, cranes, etc. A modified integer and categorical particle swarm optimization algorithm combined with Tabu search (MICPSO-TS) is proposed. In the proposed MICPSO-TS, double-vector encoding is presented to show the execution modes of activities and overtime schedule of projects which are optimized by ICPSO and TS respectively. A hybrid heuristic decoding algorithm (HHDA) including project order selection rules, crane scheduling rules, resource reservation mechanism and overtime determination rules is proposed. Eventually, the feasibility and effectiveness of the proposed MICPSO-TS are verified by the experimental test data and a real-world engineering case.
Most engineering design problems have multiple objectives,under the premise of satisfying constraints,it is necessary to maximize（or minimize） these goals at the same *** normal circumstances,multi-target correspond...
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ISBN:
(纸本)9781510840683
Most engineering design problems have multiple objectives,under the premise of satisfying constraints,it is necessary to maximize（or minimize） these goals at the same *** normal circumstances,multi-target corresponds to a variety of solutions,which usually use genetic algorithm to find its Pareto *** the current genetic algorithm to solve the problem of the process is too complicated,this paper uses the method of bucket and genetic algorithm to solve the multi-objective optimization problem considering two *** comparing the experimental results,it has achieved good *** steps of this algorithm are relatively simple,and provides a new way to solve the multi-objective optimization problem under certain constraints.
Multiple numbers of Building Energy Simulation (BES) programs have been improved and implemented during the last decades. BES models play a crucial role in understanding building energy demands and accelerating the ma...
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Multiple numbers of Building Energy Simulation (BES) programs have been improved and implemented during the last decades. BES models play a crucial role in understanding building energy demands and accelerating the malfunction diagnosis. However, due to the very high number of interacting parameters, most of the developed energy simulation programs do not accurately predict building energy performance under a known condition. Even the energy models which are developed with the very precise assignment of parameters, there is always significant discrepancies between the simulation results and the real-time data measurements. Current study develops an optimization-based framework to calibrate the whole building energy model. The optimization algorithm attempts to set the identified parameters to minimize the error between the simulation results and the real-time measurements. Due to the high number of parameters, the developed optimization algorithm utilizes a Harmony Search algorithm as its search engine coupled with the energy simulation model to accelerate the calibration process. Moreover, to illustrate the efficiency of using the developed framework, a case study of the office building is modeled and calibrated and the statistical analysis was conducted to assess the accuracy of the results. The results of the calibration process show the reliability of the framework. (C) 2019 Elsevier B.V. All rights reserved.
Tax prediction is very significant to make up the economy policy and adjust the structure of economy.A hybrid model of RBF neural network and particle swarm optimization algorithm is proposed to predict tax in the ***...
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Tax prediction is very significant to make up the economy policy and adjust the structure of economy.A hybrid model of RBF neural network and particle swarm optimization algorithm is proposed to predict tax in the *** connection weights which are connected hidden layer and output layer,and the centers and the widths of radial basis function in hidden layer are selected by particle swarm optimization to solve the influence of them on prediction ability of RBF neural *** error of tax prediction between PSO-RBFNN and RBFNN is computed,which illustrates that the tax prediction ability of PSO-RBFNN is better than that of RBFNN.
SMEs play an increasingly important role in promoting national economic development and social progress. With increasingly fierce market competition, the challenges faced by SMEs are gradually increasing, and it is ur...
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SMEs play an increasingly important role in promoting national economic development and social progress. With increasingly fierce market competition, the challenges faced by SMEs are gradually increasing, and it is urgently needed to improve logistics management to increase the economic benefits of enterprises. Although SMEs are not strong in strength, the impact of logistics activities on companies continues to increase, and companies must also pay attention to logistics construction. This paper elaborates the problems existing in SMEs in light of the major problems in the logistics management of SMEs, and analyzes the optimization algorithms of logistics management to lay a solid foundation for further development.
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