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Model and Optimize the Magnetic Composite Fluid (MCF) Polishing Process with Machine Learning Modeling and Intelligent optimization algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第9期23卷 957-967页

作者： Fan, Jinwei Ren, Xingfei Pan, Ri Wang, Peitong Tao, Haohao Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China

In the magnetic composite fluid (MCF) polishing process, appropriate polishing parameters are the basis of achieving high-quality polishing without damage. Appropriate polishing parameters are mainly based on an accurate polishing model and an excellent polishing parameters optimization algorithm. However, due to the complicated principle of MCF polishing and various influencing elements, traditional modeling methods have the limitations of low accuracy, poor application, and difficulty in correcting. Therefore, it is challenging to obtain the optimal polishing quality by optimizing the polishing parameters based on the traditional model. This study proposed an online modeling approach considering data cleaning based on machine learning modeling, and the particle swarm optimization (PSO) algorithm was used to optimize polishing parameters. Then, copper polishing experiments were carried out to validate the modeling and optimization methods. The results demonstrate that the proposed machine learning online modeling method can establish an accurate MCF polishing model, and the nano-scale fine polishing of copper can be achieved by the optimized polishing parameters of PSO, and the surface roughness of the copper sample was reduced by 85% to 0.031 mu m.

关键词： Magnetic composite fluid polishing particle swarm optimization algorithm Polishing parameters Surface roughness

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An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19321-19339页

作者： Pu, Qiumei Gan, Jingkai Qiu, Lirong Duan, Jiaxin Wang, Hui Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

particle swarm optimization (PSO) algorithm is one of the typical example of swarm Intelligence (SI) algorithm. This article addresses such problems of PSO algorithm as random initial position of each particle, unsmooth speed weight change, and poor search ability, and proposes an optimization algorithm-hybrid PSO (HPSO) algorithm to solve these problems. This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the "position-velocity" model, and introducing the "search restriction" mechanism, and the "fly-back" mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm. Furthermore, experimental results were analyzed and verified. The experiment compares HPSO algorithm with K-Means algorithm, PSO algorithm, and other two typical improved algorithms from the literature on six of the UCI standard clustering test data sets. The results indicate that HPSO algorithm has good performance in stability, clustering effectiveness, robustness and global search ability.

关键词： swarm intelligence algorithm particle swarm optimization algorithm HPSO algorithm K-means algorithm Single point crossover operator

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New control strategy for suppressing the local vibration of sandwich beams based on the wave propagation method

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2022年第1期33卷 231-247页

作者： Xu, Jun Chen, Yandong Tai, Yongpeng Shi, Guodong Chen, Ning Yao, Jialing Nanjing Forestry Univ Dept Mech & Elect Engn 159 Longpan Rd Nanjing 210037 Peoples R China Nanjing Forestry Univ Dept Automobile & Traff Engn Nanjing Peoples R China

The general vibration control strategy of beam structures is a global vibration control method based on the principle of modal superposition. However, the vibration wave control of beams can achieve local control of the vibration energy. This paper presents a wave control method for the local vibration control of fractional viscoelastic composite beams based on the operating principle of a piezoelectric sheet. To obtain better control performance, a particle swarm optimization algorithm was adopted to optimize the parameters of the piezoelectric sheet. A linear quadratic regulator control algorithm was designed to verify the validity of the proposed method. In addition, the effects of the piezoelectric sheet number and fractional order on the amplitude response and optimization parameters were investigated. We observed that the proposed method has a good control effect on the local area vibration, and it can control the flow direction of the vibration power. The proposed method can be used to directly design the voltage and phase of a piezoelectric sheet without real-time feedback computation. This method is suitable for reducing local vibration under single repetitive operating conditions in engineering and can provide a theoretical basis for a follow-up study on the acoustic black hole phenomenon.

关键词： Fractional order composite beam piezoelectric sheet local vibration control particle swarm optimization algorithm wave control method

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Damage localization in pressure vessel using guided wave-based techniques: Optimizing the sensor array configuration to mitigate nozzle effects

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APPLIED ACOUSTICS 2022年 185卷

作者： Hu, Chaojie Yang, Bin Xiao, Biao Xuan, Fu-Zhen Xiang, Yanxun East China Univ Sci & Technol Sch Mech & Power Engn Shanghai Peoples R China Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai Peoples R China Shanghai Inst Special Equipment Inspect & Tech Re Shanghai Peoples R China

Ultrasonic guided waves are desirable technologies for damage monitoring in large structures such as pipes and pressure vessels since they could propagate over a long distance with low attenuation. However, due to the complex scattering of guided waves, it is a great challenge to extract the damage information when using guided wave-based techniques to monitor the pressure vessels equipped with accessories. As an attempt, we investigated the interactive behavior between guided waves and pressure vessel nozzles. Ensemble empirical mode decomposition, fast Fourier transform and ellipse-based damage localization algorithm are adopted to perform the signal processing and damage localization analysis, respectively. A sensor array configuration optimization method is presented to mitigate the nozzle effects on the propagation of guided waves. The optimal number of sensors is determined experimentally, and the sensor array placement is optimized by a particle swarm optimization algorithm. The accurate experimental damage localization results in the pressure vessel and curved plate indicate the effectiveness of our methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Pressure vessel nozzle Sensor array Guided wave Damage localization particle swarm optimization algorithm

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Spatial Consistency Calibration Based on Phase Difference Minimization for Parallel Slightly Off-Axis Digital Holographic Microscopy

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2022年第3期14卷 1页

作者： Jin, Chuan He, Yu Tang, Yan Liu, Junbo Sun, Haifeng Hu, Song Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Sichuan Peoples R China

In this study, we propose a spatial consistency calibration method for slightly off-axis digital holographic microscopy (SO-DHM). The acquisition of relative position errors is summarized into a solution of a nonlinear optimization problem in which the root mean squared error of phase aberration of holograms is minimized. The particle swarm optimization algorithm is chosen to solve this optimization problem due to its simple structure, high convergence efficiency, and robust global search ability. Phase-only wavefronts based on phase aberration, which remove the influence of noise, are used for calibration. The simulation result indicates that the proposed method has subpixel-level precision, and the experiment demonstrates the effectiveness of the proposed method in the SO-DHM system.

关键词： Calibration optimization Charge coupled devices Optical attenuators Layout particle swarm optimization Optical polarization Digital holographic microscope particle swarm optimization algorithm slightly off-axis spatial consistency calibration

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Hybrid PSO-GA algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures

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PURE AND APPLIED GEOPHYSICS 2022年第6-7期179卷 2231-2254页

作者： Sohouli, A. N. Molhem, H. Zare-Dehnavi, N. Islamic Azad Univ Petr Min & Mat Engn Dept Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Phys Karaj Branch Karaj Iran Islamic Azad Univ Dept Phys Cent Tehran Branch Tehran Iran

The optimization algorithm is a very fast method for modeling magnetic anomalies from an ideal geological model. In addition, these models can be used to explore and estimate mineral deposits. However, in exploratory geophysics, it is common to use modeling methods with regular geometric shapes to estimate anomalous parameters such as shape, material, depth, and elongation angles. In this study, the parameters of the magnetic model are estimated using a hybrid PSO-GA method, which is an evolutionary algorithm based on a combination of particle swarm optimization (PSO) and genetic algorithms (GA). In this method, the PSO algorithm improves the magnetic data, while the GA modifies the decision to estimate the model parameters. Moreover, the balance between exploration and exploitation capabilities improves the performance of this method by combining genetic operators in the hybrid PSO-GA algorithm. Here, Gaussian white noise has been added to the artificial data with different percentages in the range of 0-25% to analyze the error of the obtained model. The results show that the proposed method can provide valuable outcomes in estimating model parameters up to 25% noise. Moreover, the accurate data from an aeromagnetic survey in the Basiran region in South Khorasan province are used to validate the estimated model parameters. The results show that the estimation of the parameters obtained from the current method is consistent with the parameters given from the commercial software and the published geological results.

关键词： Parameter estimation parameters of the magnetic model magnetic anomalies modeling particle swarm optimization algorithm genetic algorithm airborne magnetic data

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Classification algorithm for class imbalanced data based on optimized Mahalanobis-Taguchi system

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APPLIED INTELLIGENCE 2022年第9期52卷 10674-10691页

作者： Mao, Ting Zhou, Li Zhang, Yueyi Sun, Yefang China Jiliang Univ Sch Econ & Management Hangzhou 310000 Peoples R China China Natl Inst Standardizat Beijing 100000 Peoples R China

Imbalanced data classification is a challenge in data mining and machine learning. To improve the classification performance for imbalanced data, this paper proposes an imbalanced data classification algorithm based on the optimized Mahalanobis-Taguchi system (OMTS). At the feature selection stage, important feature variables are determined by four principles, namely maximizing mutual information between features and classes, minimizing mutual information between features, maximizing the initial classification accuracy, and selecting features that produce not only the local maximum or minimum of the difference between the mean Mahalanobis distances (MDs) of normal and abnormal samples but also the largest number of features. At the threshold determination stage, using the selected features, particle swarm optimization is used to determine the optimal threshold for classifying normal and abnormal samples according to the principle of maximizing classification accuracy. At the classification and discrimination stage, the samples are divided into two classes according to their MDs and optimal threshold. Experimental results show that OMTS obtains 0.92, 0.95, 0.81, 0.88, and 0.74 in accuracy on the Forest Type Mapping UCI, Fetal Health Classification, Connectionist Bench, Wine Quality, and Oil datasets, respectively, and has better classification performance than other algorithms.

关键词： Imbalanced data Classification Mahalanobis-Taguchi system Mutual information particle swarm optimization algorithm

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optimization algorithm for E-commerce Order Delivery Route 2

Optimization Algorithm for E-commerce Order Delivery Route

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Dong, Dandan Shaanxi Fashion Engineering University School of Economics and Management Xi'an China

ISBN: (纸本)9798350317558

With the rapid development of e-commerce, logistics distribution has become an important means for e-commerce enterprises to improve their competitiveness. This article focuses on the optimization problem of e-commerce order delivery routes, using particle swarm optimization algorithm for research, aiming to improve delivery efficiency, reduce costs, and enhance customer satisfaction. This article compares the time for solving the optimal path before and after optimization. The experimental results show that after optimizing with the particle swarm optimization algorithm, the average solving time is reduced from 67.12s to 27.49s. The research results indicate that the particle swarm optimization algorithm is superior to traditional algorithms in terms of solving efficiency and quality, providing strong support for e-commerce enterprises in fierce market competition. © 2024 IEEE.

关键词： E-commerce Order Experimental Testing Order Delivery particle swarm optimization algorithm Route optimization

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Structure optimization based on phase-locked loop and controller parameters optimization of Y-connected modular multi-level converter for fractional frequency offshore wind power system under weak grid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第18期16卷 3605-3616页

作者： Meng, Yongqing Jia, Feng Wu, Kang Duan, Ziyue Wang, Xiuli Yang, Yong Wang, Xifan Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Shaanxi Peoples R China Zhejiang Windey Co Ltd Hangzhou 310000 Zhejiang Peoples R China State Grid Gansu Elect Power Co Elect Power Res Inst Lanzhou Gansu Peoples R China

Based on a practical long-distance large-capacity transmission engineering case, the small-signal stability of fractional frequency transmission system (FFTS) with Y-connected modular multi-level converter (Y-MMC) is mainly researched in this paper. Different from the previous control strategies of Y-MMC, this paper establishes a frequency decoupling mathematical model and proposes the decoupling control strategies. Then, the small-signal model of the Y-MMC system is obtained. Considering the existence of phase locked loop (PLL) will deteriorate the stability of the system under weak grid, this paper applies the short-circuit ratio (SCR) for analysis and proposed a new structure of PLL, called phase-shifted PLL (PS-PLL), to improve the small-signal stability of the system. In addition, for better stability and dynamic response performance of the closed-loop control system, this paper proposes a new parameter optimization objective function for parameters adjustment. Controller parameters optimization is conducted by using the particle swarm optimization (PSO) algorithm and stability evaluation of the system is conducted based on the eigenvalue distribution. Finally, the feasibility and superiority of the proposed strategies and optimization are verified in MATLAB/ Simulink.

关键词： Y-MMC system PS-PLL fractional frequency transmission system Matlab-Simulink power system stability wind power plants particle swarm optimisation phase locked loop power transmission control power grids Y-MMC closed loop systems frequency decoupling mathematical model eigenvalue distribution HVDC power convertors system small-signal stability particle swarm optimization algorithm closed-loop control system SCR small-signal model offshore installations parameter optimization objective function voltage-source convertors eigenvalues and eigenfunctions fractional frequency offshore wind power system phase locked loops FFTS phase-locked loop weak grid controller parameter optimization HVDC power transmission parameter adjustment dynamic response decoupling control strategies structure optimization PSO algorithm power generation control phase-shifted PLL Y-connected modular multilevel converter large-capacity transmission engineering

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Dynamic performance analysis and optimization of pulp-molding machine frames

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1期122卷 209-217页

作者： Lou, Junbin Gong, Yaping Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316000 Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Zhejiang Peoples R China Zhejiang Ocean Univ Maritime Sch Zhoushan 316000 Peoples R China

The pulp-molding product is a new degradable and pollution-free packaging material replacing traditional packaging materials. Countries are strongly recommending that pulp-molding machines are mainly used to produce pulp-molding products. The stability, efficiency, and rapidity of its products' molding and processing quality are largely controlled by the structural dynamic mechanics of the molding machine frame. Its equipment's dynamic performance is measured by the 1st-order natural frequency. The pulp-molding machine frame was taken as an example for optimizing the design and investigating the dynamic performance in the work. Based on finite elements, the beam structure was divided into finite elements, and the global independent generalized displacement coordinates of equipment were extracted using multi-point constraint elements. The global independent generalized displacement coordinates and the Lagrange equation were used to establish the device's elastic dynamic model. Besides, the correctness of the theoretical model was verified by the finite element method. Finally, the first natural frequency was taken as the optimization design goal, and the particle swarm optimization algorithm was utilized to optimize the section size of the beam in equipment. The results could optimize the molding machine's design and analyze the dynamic performance at the pre-design stage.

关键词： Pulp-molding machine Elastodynamic modeling Natural frequency particle swarm optimization algorithm

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