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Design of improved four-coil structure with high uniformity and effective coverage rate

HELIYON 2023年第4期9卷 e15193页

作者： Zhu, Xuehua Liu, Chuangchuang Su, Hao Miao, Yutong Cheng, Hongde AnHui Polytech Univ AnHui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China

Helmholtz coils have extensive applications in biological medicine, aerospace, and other in-dustries depending on the simple structure and miraculous magnetic field characteristics. How-ever, the uniform zone generated by them is not appropriate for scientific experiments with large devices. Due to the limitations of Helmholtz coils in application, a novel design technique is proposed to improve the homogeneity and region of magnetic field. The main approach is to add an auxiliary coil on each side of Helmholtz coils to compensate for the magnetic field that exists farther out from the center point. To analyze the size relationship between the auxiliary coil and the main coil to obtain the best magnetic field distribution, the traditional Maclaurin expansion method and particle swarm optimization (PSO) algorithm are used to research and discuss. The magnetic field distribution and the corresponding effective coverage rate (ECR) of the improved schemes with different structural parameters are calculated under the relative deviations of 0.1%, 0.5% and 1%, respectively. The results obtained by the above optimization methods are verified by the finite element software COMSOL and specific experiments. Both optimization methods manifest that the maximum effective coverage rate can be achieved when the size of the auxiliary coil is consistent with that of the main coil. In addition, we compare the improved four-coil structure proposed in this paper with the existing four-coil square structure under the same volume. The data show that the improved structure has certain advantages in the spatial magnetic field distribution. The corresponding tri-axial coil system is established by adopting the param-eters on the single axis, which can achieve a constant magnetic field in arbitrary directions by controlling the magnitude and direction of current on each axis. This provides a theoretical basis for the application of magnetic navigation technology.

关键词： Uniform magnetic field Maclaurin expansion method particle swarm optimization algorithm Effective coverage rate Finite element simulation

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Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期221卷

作者： Pan, Qiaosheng Zhang, Chi Wei, Xinyuan Wan, Aode Wei, Zeping Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Anhui Peoples R China Sichuan Changhong Elect Co Ltd Mianyang 621000 Sichuan Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China

Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.

关键词： Cable aging status assessment Extreme gradient boosting algorithm particle swarm optimization algorithm

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Building a consensus for the best-worst method in group decision-making with an optimal allocation of information granularity

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INFORMATION SCIENCES 2023年 619卷 630-653页

作者： Qin, Jindong Ma, Xiaoyu Liang, Yingying Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Res Ctr Data Sci & Intelligent Decis Making Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Res Inst Digital Governance & Management Decis Inn Wuhan 430070 Hubei Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Sichuan Peoples R China Beijing Inst Technol Sch Management Beijing 100081 Peoples R China

In a multiple-criteria group decision-making process, it may be unrealistic to assume that all experts are specialized in all aspects of the entire problem and can reach full agreement. To obtain a suitable result with more flexibility and robustness in group decision-making, this study concerns the introduction of information granules to the best-worst method model, regarded as an essential design asset to reach a consensus in a group decisionmaking scenario. More specifically, each pairwise comparison is performed through information granules instead of single numbers, by using an optimal allocation of information granularity. Further, we develop an optimization model that considers both consistency and consensus in one problem. The particle swarm optimization algorithm is used as an optimization method to find the optimal particle of the granular decision model. Furthermore, a new convergent iterative algorithm is developed to obtain the desired decision matrix. Next, an experimental study is presented to demonstrate the effectiveness, flexibility, and essence of the proposed model. Finally, a case study of *** is conducted to select suitable automobiles for a company based on online reviews. The solving procedures are demonstrated, to further illustrate the feasibility of the proposed method.

关键词： Multiple-criteria group decision making Best-worst method Information granules particle swarm optimization algorithm Consensus reaching process

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Digital Image Encryption algorithm Based on Double Chaotic Map and LSTM

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Computers, Materials & Continua 2023年第11期77卷 1645-1662页

作者： Luoyin Feng Jize Du Chong Fu School of Computer Science and Engineering Northeastern UniversityShenyang110819China School of Electrical Engineering and Computer Science University of MissouriMissouri65201USA

In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital ***,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic *** signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic *** the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training *** overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM ***,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted *** research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and *** algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential ***,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.

关键词： Digital image encryption LSTM particle swarm optimization algorithm DCM

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Prediction of high-performance concrete compressive strength through novel structured neural network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 1791-1803页

作者： Li, Huan Nanchong Vocat & Tech Coll Dept Civil & Architectural Engn Nanchong Sichuan Peoples R China

The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two precise optimization algorithms, namely Henry's gas solubility algorithm and particle swarm optimization algorithm. The results defined both models' best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry's gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry's gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength.

关键词： High-performance concrete Henry's gas solubility algorithm particle swarm optimization algorithm radial base function neural network

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optimization of the square and squared- off cascades to separate stable isotopes usingmeta-heuristic algorithms

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ATOMIC ENERGY 2023年第5-6期134卷 397-416页

作者： Ardestani, Fatemeh Safdari, Jaber HassanMallah, Mohammad Imani, Morteza Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch Tehran Iran

In this paper, the optimization of the square and squared-off cascades for the separation of stable isotopes with a certain number of gas centrifuges (GCs) using particle swarm optimization (PSO) and the whale optimization algorithm (WOA) was studied. For this purpose, the "SC-PSO" and "SC-WOA" codes for the square cascade (SC), and the "SOC-PSO" and "SOC-WOA" codes for the squared-off cascade (SOC) were developed. The performance of the PSO and WOA algorithms in the optimization of the square and squared-off cascades as an example for the separation of 124Xe from nine stable isotopes of xenon up to an enrichment of 85% was compared. The recovery coefficient of 124Xe isotope by the codes "SC-PSO", "SC-WOA", "SOC-PSO", and "SOC-WOA" was obtained 86.14, 78.11, 92.84, and 83.41% respectively. The performance of the algorithms indicates the higher yield of the PSO algorithm in the optimization of SC and SOC. The efficiency of the PSO has been demonstrated by the Griewank test function compared with the WOA, the sine cosine algorithm (SCA), and the dragon-fly algorithm ( DA) (PSO>WOA>SCA>DA). Furthermore, the results show that SOC performs better than SC.

关键词： Square cascade Squared-off cascade Gas centrifuge particle swarm optimization algorithm Whale optimization algorithm Multicomponent separation

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A new uncertain remanufacturing scheduling model with rework risk using hybrid optimization algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第22期30卷 62744-62761页

作者： Zhang, Wenyu Wang, Jun Liu, Xiangqi Zhang, Shuai Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Informat Engn Sch Hangzhou 311305 Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China

As a resource-conserving and environmentally friendly manufacturing paradigm, remanufacturing with the potential to realize sustainability in production has been extensively investigated. Scheduling plays a significant role in achieving the remanufacturing benefits. However, the remanufacturing process involves intricate uncertainties because it takes end-of-life products with different qualities as workblanks, which increases the risk of rework and complicates remanufacturing scheduling. Though the traditional stochastic optimization methods or fuzzy theory have been employed to address uncertainties in the remanufacturing scheduling problem, they are constrained with the limited historical data which renders it difficult to describe uncertainties accurately and intuitively. Therefore, a new uncertain remanufacturing scheduling model with rework risk is proposed, in which the interval grey numbers are applied to describe the uncertainty clearly and consider the rework risk in remanufacturing process. To solve this model, a hybrid optimization algorithm that combines differential evolution and particle swarm optimization algorithms through an efficient representation scheme is proposed. Besides, this algorithm integrates multiple improvements to maintain the diversity of the population and enhance its performance. Simulation experiments are conducted on 18 sets of instances with different scales, and the results demonstrated that the proposed algorithm obtains a better optimal solution than other baseline algorithms on 17 sets of instances. The main finding of this study is providing a new method for solving uncertain remanufacturing scheduling problem with rework risk practically and effectively.

关键词： Differential evolution algorithm Interval grey number particle swarm optimization algorithm Remanufacturing scheduling Rework risk

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Fast reconstruction method for defect profiles of ferromagnetic materials based on metal magnetic memory technique

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MEASUREMENT 2023年第1期215卷

作者： Li, Junting Su, Sanqing Wang, Wei Liu, Xinwei Zuo, Fuliang Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

In this paper, a fast reconstruction method for surface defect profiles of ferromagnetic materials is proposed based on the metal magnetic memory technology. An improved magnetic charge model that can adapt to rectangular and V-shaped defect profiles and a new particle swarm optimization algorithm based on a chaotic initial distribution, sigmoid inertia weight coefficient, and sine cosine acceleration coefficients are established as the forward model and iterative means of the method, respectively. The proposed method is verified with theoretical and experimental data, and the influence of noise is considered. The reconstruction method has good accuracy, repeatability, and robustness.

关键词： Metal magnetic memory Defect reconstruction Magnetic charge model particle swarm optimization algorithm

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Thermodynamic Multi-Field Coupling optimization of Microsystem Based on Artificial Intelligence

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MICROMACHINES 2023年第2期14卷 411-411页

作者： Shan, Guangbao Wu, Xudong Li, Guoliang Xing, Chaoyang Zhang, Shengchang Fu, Yu Xidian Univ Sch Microelect Xian 710071 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China China Acad Aerosp Standardizat & Prod Assurance Beijing 100071 Peoples R China

An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 degrees C, 55 degrees C, 35 degrees C, 180 Mpa and 12 mu m, the optimization results are as follows: the peak temperature is 97.90 degrees C, the bump temperature is 56.01 degrees C, the TSV temperature is 31.52 degrees C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 mu m. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 mu m, the pitch is 65 mu m and the thickness of SiO2 is 0.83 mu m. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves

关键词： TSV optimization particle swarm optimization algorithm microsystem

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A particle swarm Optimizer with adaptive dynamic neighborhood for multimodal multi-objective optimization

A Particle Swarm Optimizer with adaptive dynamic neighborhoo...

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第35届中国控制与决策会议

作者： Jingyue Wei Enze Zhang Rui Ge Information Engineering College of Yangzhou University

In this paper,a multi-objective particle swarm optimizer based on adaptive dynamic neighborhood(ADNMOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective *** the proposed algorithm,a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the *** addition,an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm,allowing the size of the subpopulation to change automatically when the neighborhood switch time is *** prevent the algorithm from premature convergence,a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood ***,the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multiobjective optimization *** results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.

关键词： Multi-objective optimization multimodal multi-objective optimization particle swarm optimization algorithm sub-swarm regrouping ring topology

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