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Estimating state of health of lithium-ion batteries based on generalized regression neural network and quantum genetic algorithm

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Xue, Anrong Yang, Wanlin Yuan, Xueming Yu, Binpeng Pan, Chaofeng Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Key Lab Secur Technol & Ind Cyberspace Zhenjiang 212013 Jiangsu Peoples R China ASTAR 2 Fusionopolis Way Singapore 138634 Singapore Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China

In order to solve the problem of inaccurate estimation of the state of health (SOH) of electric vehicle batteries, this paper proposes a novel SOH estimation algorithm based on particle filter (PF), quantum genetic algorithm (QGA) and generalized regression neural network (GRNN). A denoising method integrating PF and anomaly detection on grouping is proposed to make the network input parameters more stable. To improve estimation accuracy and speed, an optimized GRNN-based SOH estimation model is proposed. Based on the advantages of GRNN with fewer layers and fewer hyperparameters, the Pearson correlation coefficient and QGA are used to optimize its weights to realize the adaptive determination of hyperparameters. The experiment results based on NASA and the real vehicle dataset show that the proposed algorithm has the advantages of high estimation accuracy and low computational cost, which is of great significance to the SOH estimation of electric vehicle batteries under actual operating conditions. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Generalized regression neural network quantum genetic algorithm Particle filter State of health Lithium -ion battery

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Performance analysis and optimization of a CCHP-GSHP coupling system based on quantum genetic algorithm

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SUSTAINABLE CITIES AND SOCIETY 2019年 46卷

作者： Li, Bolun Hu, Pingfang Zhu, Na Lei, Fei Xing, Lu Huazhong Univ Sci & Technol Sch Environm Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Hubei Peoples R China

A combined cooling, heating and power (CCHP) and ground source heat pump (GSHP) coupling system with heat exchanger is presented to enhance the comprehensive performance, which was compared with the CCHP-GSHP system without heat exchanger in performance. Using the comprehensive evaluation and indicator consisting of life cycle cost saving rate and primary energy consumption saving ratio compared with separated generation system (SG), quantum genetic algorithm (QGA) and simple genetic algorithm (SGA) were selected to solve the optimization problem of the coupling system for a case study of a hotel building. It indicates that QGA is better than SGA in solution for optimal configuration of energy system under following thermal load (FTL) strategy. The optimal configuration of the coupling system are obtained. Five operating strategies were employed to analyze energy performance of the system. The results illustrated that the new strategy, following maximum comprehensive performance of the energy system (Max-cp) strategy is the best one to enhance the comprehensive performance, and the maximal index value of comprehensive performance is 0.43264 for the CCHP-GSHP coupling system. The configuration of the energy system and the suitable operating strategies may provide reference for further research and application.

关键词： CCHP-GSHP coupling system quantum genetic algorithm Comprehensive performance Optimal configuration Operating strategies

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Optimization of Impedance-Accelerated Inverse-Time Over-Current Protection Based on Improved quantum genetic algorithm

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ENERGIES 2023年第3期16卷

作者： Zhang, Xia Wang, Xiaohua Li, Zhedong Huang, Jingguang Zhang, Yupeng China Three Gorges Univ Coll Elect Engn & New Energy Yichang 433002 Peoples R China State Grid Zhejiang Shaoxing Power Supply Co Shaoxing 312000 Peoples R China

This paper proposes an impedance-accelerated inverse-time over-current protection optimization scheme based on the improved quantum genetic algorithm. First, the speed of remote backup protection is improved by increasing the optimization level of backup protection. Second, to ensure the coordination of protection when the distributed generation is connected to the distribution network, a mathematical model for the optimization of inverse time protection parameters is established. The mathematical model takes the minimum total action time of the optimized main and backup protection as the objective function, and the selectivity and sensitivity requirements of the protection as the constraints. In addition, the genetic algorithm is improved from four aspects: coding method, population initialization, quantum revolving gate, and variational evolution. The theoretical analysis and simulation results show that the proposed scheme can effectively improve the selectivity and operation speed of the protection.

关键词： inverse-time over-current protection improved impedance acceleration speed parameter optimization backup protection optimization stages quantum genetic algorithm

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A combination of spectral graph theory and quantum genetic algorithm to find relevant set of electrodes for motor imagery classification

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APPLIED SOFT COMPUTING 2020年 97卷

作者： Kirar, Jyoti Singh Agrawal, R. K. Shiv Nadar Univ Greater Noida India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

At present, more number of electrodes are used to develop brain computer interface (BCI) devices based on motor imagery. However, the number of trials for a given subject is usually less. Under this situation, the performance of motor imagery task classification may degrade. In this research work, we propose a combination of graph theoretic spectral method and quantum genetic algorithm (QGA) to obtain a subset of relevant and non-redundant electrodes for effective motor imagery task classification. Stationary Common Spatial Pattern method, which can handle non-stationarity issue, is used for extraction of features from the reduced set of electrodes. Support Vector Machine (SVM) is used as a classifier. Improvement in classification performance on publicly available dataset signifies efficacy of the proposed method. Friedman statistical test demonstrates that the performance of the proposed method is significantly better in comparison to existing CSP and its variants. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Motor imagery BCI CSP quantum genetic algorithm EEG Feature selection

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An Enhanced quantum genetic algorithm and Its Application in the Health Monitoring of a Rocket Engine

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SENSORS AND MATERIALS 2023年第10期35卷 4585-4596页

作者： Xiang, Hao Yulin Normal Univ Guangxi Coll Yulin 537000 Peoples R China Yulin Normal Univ Univ Key Lab Complex Syst Optimizat & Big Data Pro Yulin 537000 Peoples R China

In this study, the characteristics of different intelligent algorithms, from the simulated annealing algorithm (SA) and genetic algorithm to the quantum genetic algorithm, are analyzed. By utilizing the variety of the population and the rapidity of the convergence of the real doublechain coding objective gradient quantum genetic algorithm, this algorithm is fused with SA;the resulting real double-chain coding objective gradient quantum genetic simulated annealing algorithm is proposed. As the performance of the least squares support vector regression (LSSVR) is very sensitive to its key parameters, the proposed algorithm is applied to optimize these parameters to improve the generalization ability of LSSVR. Following that, a new hybrid nonparametric regression prediction model is put forward. The health monitoring of a liquid-fuel rocket engine is a very important topic, and all types of sensors are used to monitor the factors that can affect the engine thrust from different aspects. The relationship between the thrust and these factors is nonlinear and difficult to express using some formulas. The proposed model is used in the fault prediction of liquid-fuel rocket engine thrust, and the simulation results show that the average relative error is determined to be 0.37% using LSSVR and 0.2977% using the proposed model. Thus, this model is applicable to small samples, nonlinearity, and high dimensions of failure prediction, and is worth promoting to a certain extent.

关键词： quantum genetic algorithm simulated annealing algorithm LSSVR fault prediction liquid-fuel rocket engine

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A Multi-Objective quantum genetic algorithm for MIMO Radar Waveform Design

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REMOTE SENSING 2022年第10期14卷

作者： Liu, Tianqu Sun, Jinping Wang, Guohua Lu, Yilong Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Hertzwell Pte Ltd Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95-3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.

关键词： MIMO radar correlation function multi-objective optimization orthogonal waveform set design quantum genetic algorithm

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Dynamic modeling for SO₂-NO_x emission concentration of circulating fluidized bed units based on quantum genetic algorithm - Extreme learning machine

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JOURNAL OF CLEANER PRODUCTION 2021年 324卷

作者： Yu, Haoyang Gao, Mingming Zhang, Hongfu Chen, Yue North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Sch Control & Comp Engn Beijing 102206 Peoples R China

To meet the stringent emission standards and achieve cleaner production of circulating fluidized bed units, it is necessary to build a dynamic model of pollutants emission for creating an economical and environmentally friendly pollutant removal operation mode. This article fources on the modeling and accurate prediction of SO2-NOx emission concentration of the circulating fluidized bed unit. According to the generation and reduction mechanism of pollutants, the model inputs are selected and determined by Pearson coefficient. Then, a dynamic model of SO2-NOx emission concentration based on extreme learning machine is developed, and quantum genetic algorithm is used to optimize the connection weight between the input layer and the hidden layer and threshold of the extreme learning machine, which contributes to increase prediction performance. The test result shows that the optimized model can effectively imitate the dynamic trends of actual measured data with appropriate accuracy, the mean absolute percentage error of SO2 concentration and NOx concentration are 4.63% and 3.09% respectively. And the model's satisfactory generalization ability is demonstrated based on the generalization experiment. In addition, compared with other methods, the proposed modeling method for SO2-NOx concentration has more suitability and accuracy under dynamic conditions, which contributes to online optimization of pollutant control and intelligent development of circulating fluidized bed units.

关键词： CFB SO2-NOx emission Extreme learning machine quantum genetic algorithm Data-driven method

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Design optimization of public building envelope based on multi-objective quantum genetic algorithm

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JOURNAL OF BUILDING ENGINEERING 2024年 91卷

作者： He, Lihua Wang, Wei China Univ Petr East China Sch Econ & Management Qingdao 266580 Shandong Peoples R China

The sustainable development of buildings addresses climate change which can be achieved by the envelope design. However, when optimizing design schemes for building energy conservation, the impact of embodied carbon emissions from building materials is often overlooked. Establishing a rapid and effective optimization model to balance diverse objectives holds significant research importance in this context. Therefore, this paper aims to develop an optimal decision support model for envelope materials. The genetic algorithm is commonly utilized in optimizing building design. However, it has the drawback of being prone to local optima. In response, the paper proposes an approach for envelope design optimization based on the Multi -Objective quantum genetic algorithm (MOQGA). Finally, the model and algorithm are validated through a case study conducted on a public building in Yantai, evaluating the obtained Pareto front to demonstrate the trade-offs among objectives. The findings indicate that the model effectively decreases energy consumption, carbon emissions, and investment costs during the building design phase. Comparative analysis with NSGA-II demonstrates the strong search and optimization capabilities of the proposed method. This research provides quantitative references for public building designers to coordinate environmental, economic, and energy efficiency optimization during the design phase.

关键词： Public building envelope design Energy conservation CO 2 emission reduction Multi -objective optimization quantum genetic algorithm

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Designing asynchronous resilient sampled data controller for nonlinear Markov jump systems via hidden Markov model and quantum genetic algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第8期361卷

作者： Kumar, B. Ram Balasubramaniam, P. Aravind, R. Vijay Gandhigram Rural Inst Dept Math Dindigul 624302 TamilNadu India Aalen Univ Res Ctr Complex Syst Aalen Germany

The article delves into the problem of stochastic stability analysis for nonlinear Markov jump systems (MJSs) with fuzzy asynchronous resilient sampled-data controllers (SDCs) using hidden Markov model (& Oslash;) and quantum genetic algorithms (QGAs). A novel framework using QGA has been developed to obtain the control gain matrices by solving the linear matrix inequality (LMI). A new criterion is proposed to guarantee the stochastic stability of fuzzy nonlinear MJSs, relying on the mode -dependent Lyapunov-Krasovskii functional (LKF) approach. Standard Chua's circuit has been used to show the reliability and value of the developed results. Also, the Lorentz system has been explored to demonstrate the non-stochastic case.

关键词： Stochastic stability Hidden Markov model Sampled data control quantum genetic algorithm

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Optimal Scheme of Retinal Image Enhancement using Curvelet Transform and quantum genetic algorithm

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2013年第11期7卷 2702-2719页

作者： Wang, Zhixiao Xu, Xuebin Yan, Wenyao Wei, Wei Li, Junhuai Zhang, Deyun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Xian Univ Technol Sch Engn & Comp Sci Xian 710048 Peoples R China Yanan Univ Xian Innovat Coll Xian 710100 Peoples R China

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

关键词： Retinal image enhancement quantum genetic algorithm sparse representation curvelet

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