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Thermal error modeling of electric spindles based on cuckoo algorithm optimized Elman network

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第3-4期132卷 1365-1375页

作者： Dai, Ye Wang, Xin Li, Zhaolong He, Sai Yu, Baolei Zhou, Xingwen Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automat Machinery Prod Dev Harbin 150080 Peoples R China

In order to improve the accuracy of the thermal error model of the electric spindle, a thermal error modeling method based on the optimized Elman neural network using the cuckoo algorithm is proposed. To analyze the thermal behavior of the electric spindle, an ANSYS analysis approach is utilized to create a temperature map. Based on the simulation analysis outcomes, an experimental platform is established to gather temperature data and thermal displacement data. The electric spindle temperature is optimized through the utilization of fuzzy cluster analysis and the Spearman rank correlation coefficient method in combination. The comparison between the established model and the Elman model and the GA-Elman model proves that the CS-Elman model has good prediction accuracy and stability.

关键词： cuckoo algorithm Elman neural network Thermal displacement Spearman rank correlation coefficient method Thermal error modeling

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Coverage and Routing Optimization of Wireless Sensor Networks Using Improved cuckoo algorithm

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IEEE ACCESS 2024年 12卷 39564-39577页

作者： Yang, Jian Xia, Yimin Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China

Wireless sensor networks have gradually attracted widespread attention from academia and industry due to their wide application potential and versatility. In response to the coverage and routing issues of wireless sensor networks, this study combines the improved cuckoo algorithm and non-uniform clustering algorithm to design a coverage and routing optimization scheme for wireless sensor networks. Firstly, a wireless sensor network system is studied and built. Subsequently, the traditional cuckoo algorithm is optimized using the Cauchy distribution, and a coverage optimization scheme for wireless sensor networks based on the Cauchy distribution improved cuckoo algorithm is designed. Finally, by optimizing the dynamic range of network area and cluster radius respectively to improve the shortcomings of non-uniform clustering routing algorithms, a wireless sensor networks routing optimization scheme based on dynamic cluster radius optimization non-uniform clustering algorithm is designed. The research results indicated that the designed coverage scheme and routing scheme achieved good application results. Among them, the designed coverage scheme had a minimum running time of 0.89 minutes in node coverage problems and a maximum node coverage rate of 99.52%. The designed routing scheme had a minimum running time of 1.47 minutes and an energy loss rate of 0.84% in the routing optimization problem. In summary, the coverage scheme and routing optimization scheme designed by this research institute have good application effects and can provide certain technical support for the application of wireless sensor networks in other fields.

关键词： Wireless sensor networks Signal integrity Signal processing Routing protocols Optimization methods Clustering algorithms Heuristic algorithms Energy loss Dynamic range algorithm design and analysis cuckoo algorithm wireless network sensors router signal coverage node

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Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved cuckoo algorithm

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PROCESSES 2024年第4期12卷 718页

作者： Huang, Zhangchenlong Bei, Lei Wang, Ben Xu, Linlin Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Hubei Univ Econ Collaborat Innovat Ctr Emiss Trading Syst Coconstr Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol State Key Lab Coal Combust & Low Carbon Utilizat Wuhan 430074 Peoples R China

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensitive to load changes. The solution iteration of the cuckoo algorithm is 18 times. Meanwhile, the system requires four updates of capacity allocation results for 20 years of operation. This result validates the effectiveness of the proposed model and methodology. It also provides a reference for the research and construction of capacity allocation of microgrid systems at the park level.

关键词： hybrid energy storage analytic hierarchy process cuckoo algorithm multi-objective optimization

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A RLC parameter identification method for synchronous buck converters based on simulated annealing coupled cuckoo algorithm

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ELECTRICAL ENGINEERING 2024年 1-14页

作者： An, Xiaoyu Zhao, Feng Dou, Zhifeng Lu, Falong Wang, Qian Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China

Fault detection technology based on parameter identification is advancing rapidly. For power electronic circuits, parameter identification is essential for accurately evaluating performance. The commonly used identification algorithms in engineering include least squares method and particle swarm optimization algorithm, etc. The least squares method has the disadvantage of large identification error and low identification accuracy, while the convergence instability and susceptibility to local optima of particle swarm optimization algorithm also need to be improved. To address the shortcomings of the least squares method and leverage the advantages of the cuckoo algorithm, such as strong global search capability and fast convergence, this paper focuses on the synchronous buck circuit. A local linear model of the synchronous buck circuit is established to mitigate the influence of nonlinear signals, and the signals collected include the terminal voltage VS2, inductor current iL and output voltage V0. The cuckoo algorithm is employed to identify the parameters. To overcome the local optimum problem associated with the cuckoo algorithm, an improved cuckoo algorithm is proposed that integrates simulated annealing to identify the parameters L, C, RL and RC in the circuit. To demonstrate the robustness and convergence of the proposed algorithm, we use the MATLAB/Simulink platform for comparative analysis against the conventional least squares method. The results show that the improved cuckoo algorithm achieves faster convergence and greater robustness. Finally, validation on a physical experimental platform indicates that the data extracted from the actual circuit maintains high identification accuracy and convergence speed, with identification errors below 5%, further verifying the effectiveness of the algorithm.

关键词： cuckoo algorithm Fault detection Identification accuracy Parameter identification Simulated annealing Synchronous buck

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Optimum Setting Parameters of Multiple Tuned Mass Damper for Passive Control of Structures Using cuckoo algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第3期48卷 1-14页

作者： Jafarinia, Hamid Ostad-Ali-Askari, Kaveh Islamic Azad Univ Dept Civil Engn Khorramabad Branch Khorramabad *** Iran Amer Univ Dubai Sch Engn Dept Civil Engn POB 28282 Dubai U Arab Emirates Isfahan Univ Technol Coll Agr Dept Water Engn Esfahan *** Iran

Adjustable mass dampers are widely used to reduce the seismic vibrations of engineering structures. The efficiency of these systems requires a proper setting of its parameters. In this study, an efficient and effective method for designing optimal multi-tuned mass dampers (MTMD) is proposed to reduce the vibration response of structures, which is based on the definition of a multi-objective optimization problem. Therefore, the multi-objective optimization algorithm of the cuckoo has been used to solve the issue. MTMD setting parameters are considered as design variables. Since the setting of MTMD parameters is basically a multi-objective optimization problem, three objective functions including minimizing the maximum displacement of the roof, the maximum acceleration of the roof and the maximum displacement of TMDs have been considered in the optimization problem. The results of numerical simulations for a ten-story shear frame exposed to an earthquake show that the cuckoo multi-objective optimization algorithm is able to find appropriate solutions in the form of Parreto curves to set the optimal parameters of MTMDs according to the design objectives. Provided that they are caused by creating a suitable compromise between the objective functions in conflict with each other. Therefore, MTMDs reduce the seismic responses of the structure in a favorable way. Based on the results, it was found that MTMD performance depends on incoming earthquake, TMD mass ratio and number of TMDs. By increasing the number of TMDs, up to a certain limit (10), the seismic response of the seismic system is reduced in a favorable way, but many changes in reducing the seismic response of the structure are not observed. Also, the seismic response of the system is reduced to a certain value (6%), by increasing the mass ratio of TMDs, but the process of reducing the seismic response of structures does not undergo many changes with its further increase.

关键词： Passive control of structures Multiple adjusted mass damper Multi-objective optimization cuckoo algorithm Multi-tuned mass dampers Seismic vibrations Engineering structures Numerical simulations Parreto curves Design objectives Earthquake

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A Control Method Based on Improved cuckoo algorithm for Photovoltaic Hybrid Hydrogen Production System 9

A Control Method Based on Improved Cuckoo Algorithm for Phot...

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9th International Conference on Power and Renewable Energy

作者： Sun, Ziqi Peng, Yunfeng Zhang, Dongdong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Guangxi Univ Sch Elect Engn Nanning Peoples R China

ISBN: (纸本)9798350377477;9798350377460

In view of control issues associated with renewable energy hydrogen production systems, this study focuses on the hybrid hydrogen production system consisting of a photovoltaic-ALK+PEM electrolyzers and proposes a power control strategy based on the improved cuckoo algorithm. By implementing this control method in the DC-DC converters of electrolyzers, the system achieves stable operation and rapid convergence of DC bus voltage. Compared to PI control, the control scheme based on the improved cuckoo algorithm comprehensively enhances the system's performance, ensuring swift stability of DC bus voltage and precise control outcomes. This proposed method not only realizes coordinated control of multiple types of electrolyzers, but also considers the system's rapid response capability and anti-interference ability during the entire process.

关键词： cuckoo algorithm hybrid hydrogen production system DC-DC converter coordinated control for electrolyzers

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Sensitive Vector Search for Logic Circuit Failure Probability based on Improved Adaptive cuckoo algorithm

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JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE 2022年第2期22卷 69-83页

作者： Cai, Shuo Wu, Sicheng Wang, Weizheng Yu, Fei Yin, Lairong Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 HN Peoples R China Changsha Univ Sci & Technol Sch Automot & Mech Engn Changsha 410114 HN Peoples R China

With the development of microelectronics technology, the feature size of integrated circuit continues to shrink, and circuit performance has been improved. At the same time, however, factors such as process disturbance, power noise, and particle radiation are having an increasingly serious influence on the Failure Probability of Circuits (FPC). Searching the input vectors that are sensitive to FPC can assist circuit designers in selectively reinforcing the circuit to reduce the fault-tolerant overhead and improve the fault-tolerant efficiency. In this paper, an Improved Adaptive cuckoo Search (IACS) algorithm is proposed to search sensitive circuit vectors. The vector segmentation strategy is used to change the dimension of the input vector, the hill climbing algorithm is used to improve the quality of the initial population, and the adaptive strategy is used to control parameters such as power-law index, discovery probability and scaling factor. At the same time, a Correlation Separation Approach (COSEA) is proposed to calculate the FPC under specific vector excitation. Experimental results show that the proposed algorithm has higher accuracy and better efficiency compared with existing algorithms.

关键词： Failure probability cuckoo algorithm sensitivity vector logical circuit semiconductors

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Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2022年第1期68卷 58-69页

作者： Hameed, Mazin Kadhum Idrees, Ali Kadhum Univ Babylon Dept Software Babylon Iraq Univ Babylon Dept Comp Sci Babylon Iraq

This study proposes the Sensor Device Scheduling-based cuckoo algorithm (SeDeSCA) for enhancing the lifespan of cluster-based WSNs. SeDeSCA consists of two phases: clustering and scheduling. The WSN is clustered into clusters using the DBSCAN algorithm in the first phase. The scheduling phase is periodic and consists of three steps: cluster head polling, scheduling decision-based optimisation, and covering. The sensors in each cluster choose their cluster head, which executes the cuckoo algorithm (CA) to select a suitable schedule of sensors that will achieve sensing during the current period. The aim of scheduling is to minimise energy consumption and ensure sufficient coverage for the monitored area while maximising network lifespan. The third step is to cover the area of interest with the sensors that are scheduled to be active during this period. The simulation results show that SeDeSCA improves the network lifespan and coverage ratio, as well as the lifespan of WSNs.

关键词： wireless sensor networks cuckoo algorithm DBSCAN lifetime enhancement scheduling algorithms

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Markov chain Monte Carlo inversion with cuckoo algorithm

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CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION 2025年第4期68卷 1511-1520页

作者： Li, Jin Zhang, Huai Shi, Yaolin Univ Chinese Acad Sci Coll Earth & Planetary Sci Natl Key Lab Earth Syst Numer Modeling & Applicat Beijing 100049 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519080 Guangdong Peoples R China Univ Chinese Acad Sci Beijing Yanshan Earth Crit Zone Natl Res Stn Beijing 101408 Peoples R China

Due to the incompleteness of actual geophysical survey data, including geophysical gravitational, magnetic, electrical and seismic measurements, and low signal-to-noise ratio of partial data, the traditional inversion theories based on Tikhonov regularization face issues such as low resolution and poor adaptability. In recent years, stochastic inversion, represented by Bayesian inversion, has become increasingly mature. This method offers advantages of the higher resolution of inversion results, better global convergence and easier combination with artificial intelligence (AI), compensating for the shortcomings of traditional algorithms. The main bottleneck of the Bayesian inversion method lies in a large amount of calculation, slow and unstable convergence of the Markov chain Monte Carlo sampling algorithm. In this paper, the target parameters of geophysical inversion are constructed under the framework of Bayesian inversion. By the physical relationship between the data and the parameters to be inverted, based on the Markov chain Monte Carlo algorithm, the cuckoo algorithm is introduced to optimize sampling, and the inversion of the gravity field and the average velocity of seismic wave is realized, and compared with traditional inversion methods. The results show that method we proposed has higher accuracy, faster convergence and better stability than the traditional Markov chain Monte Carlo inversion method, which provides a new idea and a new approach for realizing the nonlinear inversion theory combining Tikhonov regularization and stochastic inversion in geophysical inversion in the future.

关键词： Stochastic inversion Bayesian inversion Markov chain Monte Carlo algorithm cuckoo algorithm Gravity inversion Seismic wave inversion

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Multi-objective optimization method of smart medical wireless sensor network coverage based on improved cuckoo algorithm 3

Multi-objective optimization method of smart medical wireles...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Xu, Zhen Chen, Ping Jiangxi Science and Technology Normal University Nanchang China

ISBN: (纸本)9798350341546

In order to improve the coverage level of smart medical wireless sensor network, a multi-objective optimization method of smart medical wireless sensor network coverage based on improved cuckoo algorithm is proposed. Construct the routing node deployment structure model of smart medical wireless sensor network, adopt the channel balanced scheduling method to realize the structured scheduling and spread spectrum processing of the transmission layer of smart medical wireless sensor network, adopt the multi-sensor fusion tracking identification method to realize the identification of the coverage parameters of smart medical wireless sensor network, adopt the improved cuckoo algorithm to establish the dynamic distribution function of wireless sensor network coverage multi-objective optimization, and randomly scatter several sensors in the area to be monitored. And set a certain number of anchor nodes, initialize the position parameters of the population, calculate the fitness value of the initial population and sort it from small to large. Through the joint parameter identification of the initial position set and the target position set of the mobile node, the multi-objective optimization of the coverage of the smart medical wireless sensor network is realized. The simulation test results show that this method can improve the coverage level of nodes and reduce the network energy cost and transmission error rate. © 2023 IEEE.

关键词： Coverage cuckoo algorithm Multi-objective optimization Smart medical wireless sensor network

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