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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Liang, Wanlei Guan, Xiaodan North China Inst Aerosp Engn Dept Elect Engn Langfang 065000 Peoples R China

ISBN: (纸本)9783037855034

The mounting process is the key factor of the placement efficiency, it is also important for the improvement of the efficiency of whole production line and decrease of the cost. This paper analyzed the mounting process of the Chip Shooter machine, applied the PSO algorithm, constructed the corresponding coding system, proposed the corresponding particle update mechanism, introduced the partially matched crossover idea of the genetic algorithm into the PSO algorithm, and designed the new re-scheduling method of feeder position assignment to optimize the position assignment of feeders and the pickup and placement sequence of components, thus improved the placement efficiency. After comparing the results before and after the simulation test for selected 8 pieces of PCB, the average efficiency of this algorithm is 7.09% higher than genetic algorithm method that is based on sort encoding. The experimental result shows that, this algorithm is more efficiency on the improvement placement efficiency and decrease of the placement time for the chip shooter machine.

关键词： Chip shooter machine Operation sequence optimization particle swarm optimization algorithm

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Constrained Multi-objective particle swarm optimization algorithm

Constrained Multi-objective Particle Swarm Optimization Algo...

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8th International Conference on Intelligent Computing (ICIC)

作者： Gao, Yue-lin Qu, Min N Ethn Univ Inst Informat & Syst Sci Yinchuan Ningxia Peoples R China

ISBN: (纸本)9783642318368

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

关键词： particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method

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Mutual inductance identification of non-contact excitation system

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ADVANCES IN MECHANICAL ENGINEERING 2024年第8期16卷

作者： Li, Ke Ding, Jie Sun, Xiaodong Meng, Xuan Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Jiangsu Univ Automot Engn Res Inst Xuefu Rd 31 Zhenjiang 212013 Peoples R China

Due to the presence of brush and slip ring in the excitation method of electrically excited synchronous motors, this article proposes a new excitation method - non-contact excitation system. This method transfers electrical energy from the stator to the rotor through magnetic coupling, replacing slip ring and brush. However, the magnetic coupling coils at the primary and secondary ends of the system will deviate, which will affect motor operation quality. In order to effectively reduce the changes caused by mutual inductance, this article proposes an improved particle swarm optimization algorithm for mutual inductance identification. This improved algorithm can effectively reduce the shortcomings of low accuracy and easy to fall into local optima in particle swarm optimization. Simulation and experimental results show that the improved particle swarm optimization algorithm can improve search accuracy.

关键词： Non-contact excitation wireless power transfer mutual inductance identification particle swarm optimization algorithm

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PSO-NMPC control strategy based path tracking control of mining LHD (scraper)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-28页

作者： Liu, Ya Peng, Ping-an Wang, Li-guan Wu, Jia-xi Lei, Ming-yu Zhang, Chao-wei Lei, Ru Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Minist Educ Xinjiang Inst Engn Key Lab Xinjiang Coal Resources Green Min Urumqi 830023 Peoples R China

The automation of underground articulated vehicles is a critical step in advancing digital and smart mining. Current nonlinear model predictive control (NMPC) controllers face challenges such as delays in turning on large curvature paths and correction lags during the control of underground the Load-Haul-Dump (LHD). To address these issues, this paper proposes a PSO-NMPC control strategy that integrates a particle swarm optimization algorithm (PSO) into the NMPC controller to enhance path tracking for LHDs. To verify the effectiveness of the proposed PSO-NMPC control strategy, the local path of the tunnels is selected as the simulation path, comparing it with the pure NMPC controller based on the path characteristics of the actual tunnel. The results demonstrate that the improved NMPC controller significantly enhances the trajectory tracking performance of the LHD, with maximum absolute lateral deviations for experimental paths 2, 3, and 5 improved by 89.7%, 72.2%, and 68.9%, respectively. Additionally, the improved NMPC controller exhibits superior performance in paths with large curvature compared to those with very small curvature and straight-line paths, effectively addressing the challenges of turn delay and backward lag in LHD operation, thus providing practical significance.

关键词： Mining LHD Nonlinear model predictive control particle swarm optimization algorithm PSO-NMPC control strategy Path tracking control

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Vehicle path planning method based on particle swarm optimization algorithm

Vehicle path planning method based on particle swarm optimiz...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Yu, Sheng-long Bo, Yu-ming Chen, Zhi-min Zhu, Kai Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9783037853696

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. particle swarm optimization algorithm a kind of swarm-based optimization *** simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

关键词： particle swarm optimization algorithm genetic algorithm vehicle path planning

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Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

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ENERGY 2023年第PartA期262卷

作者： Du, Xiaoyi Wu, Dongdong Yan, Yabo Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

In this paper, GM(1,N-r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm;(2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables;and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N-r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

关键词： Electricity consumptions forecasting Grey forecasting model GM(1,N-r) model Grey incidence analysis particle swarm optimization algorithm

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A novel integrated hydrogen and natural gas liquefaction process utilizing a modified double mixed refrigerant process pre-cooling system

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APPLIED THERMAL ENGINEERING 2023年 224卷

作者： Geng, Jinliang Sun, Heng China Univ Petr Natl Engn Res Ctr Oil & Gas Pipeline Transportat S MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Beijing 102249 Peoples R China

The hydrogen liquefaction process is energy-intensive due to the lower liquefaction temperature. To improve the energy efficiency of the hydrogen liquefaction process, a novel integrated liquefaction process employing a modified double mixed refrigerant process pre-cooling is proposed and analyzed. The three-stage adiabatic ortho-para hydrogen conversion makes the process compact. Furthermore, the particle swarm optimization algorithm is utilized to find the optimal decision variables. The result reveals that the total specific energy consumption (SEC) of the process is 6.5921 kWh/kgLH2-LNG, which is 15.83% lower than the base case. The SECs for the production of LNG and LH2 are 0.2445 kWh/kgLNG and 6.3476 kWh/kgLH2. Moreover, the exergy analysis and thermodynamic evaluation reveal that the exergy efficiency, coefficient of performance, and figure of merit of the process are 49.26%, 0.2306, and 0.49, exhibiting high thermodynamic performance. Finally, the sensi-tivity analysis indicates that the evaporation pressure of the sub-cooling refrigerant has the greatest impact on the performance of the process. The proposed process has an important reference value in the design of the combined production of LH2 and LNG from industrial by-products and the H2 liquefaction process.

关键词： Integrated liquefaction process Mixed refrigerant Liquefied hydrogen Liquefied natural gas particle swarm optimization algorithm

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Quantitative determination of TVB-N content for different types of refrigerated grass carp fillets using near-infrared spectroscopy combined with machine learning

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 126卷

作者： Zhang, Qinyu Hu, Zhigang Xu, Zijian Zhang, Peilin Jiang, Yajun Fu, Dandan Chen, Yan Wuhan Polytech Univ Sch Mech Engn Wuhan 430023 Hubei Peoples R China Hubei Prov Cereal & Oil Machinery Engn Technol Res Wuhan 430023 Hubei Peoples R China Hubei Prov Aquat Prod Proc Equipment Engn Technol Wuhan 430023 Hubei Peoples R China

This research aims to accurately forecast the freshness indicators (TVB-N) of skinned and skinless grass carp fillets by integrating near-infrared spectroscopy (NIR) with machine learning algorithms. By comparing the predictive accuracy of machine learning models for the two types of grass carp fillets, the most effective modeling method is identified. Methodologically, the study first applies orthogonal signal correction (OSC) and the first derivative among other algorithms for spectral pre-processing. Subsequently, competitive adaptive reweighted sampling (CARS), moving window partial least squares (MWPLS), and random frog (RF) are used for the selection of variables. Lastly, partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural networks (BPNN), and particle swarm optimization-enhanced BP neural networks (PSO-BP) are employed to quantitatively analyze the NIR data. The most relevant results reveal that the (OSC+D1)-CARSPSO-BP model exhibits superior predictive capabilities. Especially when applied to skin-on fish fillets (R2P =0.988, RMSEP=0.092), this model surpasses that for skinless fish fillet data (R2P =0.987, RMSEP=0.096). Therefore, combining near-infrared with machine learning to predict the freshness (TVB-N) of grass carp fillets based on skin-on samples is a more effective non-destructive testing method.

关键词： Near -Infrared spectroscopy Rapid non-destructive detection Grass carp fillets Total volatile basic nitrogen Machine learning Orthogonal signal correction (OSC) particle swarm optimization algorithm optimized BP neural network (PSO-BP)

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Hybrid Structured Artificial Network For Compressive Strength Prediction Of HPC Concrete

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淡江理工学刊 2023年第7期26卷 989-999页

作者： Liang Chen

This study aims to develop a surrogate artificial neural network-based technique for predicting the compressive strength of concrete, which is the most significant factor in the life service of concrete and its durability in civil construction projects of civil engineering. For this goal, a data set for high-performance concrete is gathered from the literature repository, which includes a different percentage of fly ash, silica fume, and superplasticizer in the mix designs. The data set is applied to train and validate the optimal structured artificial neural network optimized by innovative equilibrium optimization and particle swarm optimization algorithms. The results showed that the EOANN-I and PANN-I model with the R2 and RMSE values of 0.9889, 1.771, 0.9881, and 1.8813, stood at the first and second stage of the most capable models in the prediction of HPC compressive strength. Although the PANN-I model's complexity is lower than the EOANN-I model, the rate of its prediction accuracy covers its lack of complexity.

关键词： HPC concrete compressive strength artificial neural network equilibrium optimization algorithm particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm

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International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2012)

作者： Jiang, Changyuan Zhao, Shuguang Guo, Lizheng Ji, Chuan Donghua Univ Coll Informat Sci & Technol Shanghai 201600 Peoples R China

ISBN: (纸本)9783037854693

Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

关键词： particle swarm optimization algorithm inertia weight sinusoidal changing differential equation

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