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The Innovation and Transfer of Computer Science and Technology in Recent and Modern Railway Transportation

INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2025年第1期21卷

作者： Ye, Yuanlong Zhang, Hui Sanming Univ Sanming Peoples R China Wuhu Univ Wuhu 241000 Anhui Peoples R China

This article aims to discuss the influence of computer technology on the innovation and technology transfer of modern railway transportation. By historical analysis and empirical research, this article first reviews the evolution of railway transportation from steam locomotive to High-Speed Railways, and then compares and analyzes the performance of the improved multi-objective particle swarm optimization (mpso) algorithm and traditional particle swarm optimization (PSO) algorithm in train operation scheduling through designing simulation experiments. The experiment selected the representative train operation data sets, and the first 25 and 15 data items are used as training sets respectively to predict the subsequent data items, and the prediction results of mpso algorithm, traditional PSO algorithm and support vector machine algorithm are compared. The results show that mpso algorithm is excellent in prediction accuracy, convergence speed and error control, and its prediction results are closer to the actual train running state, which verifies the effectiveness of computer technology in optimizing railway transportation scheduling.

关键词： Computer Technology Railway Transportation Technological Innovation Technology Transfer mpso algorithm

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mpso-MOM: A hybrid modified particle swarm optimization and method of moment algorithm for smart antenna synthesis

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ELECTROMAGNETICS 2008年第6期28卷 411-426页

作者： Mahmoud, K. R. El-Adawy, M. I. Ibrahem, S. M. M. Bansal, R. Zainud-Deen, S. H. Univ Connecticut Dept Elect & Comp Engn Unit 2157 Storrs CT 06269 USA Helwan Univ Dept Elect Commun & Comp Fac Engn Cairo Egypt Menoufia Univ Dept Elect & Elect Commun Engn Fac Elect Engn Menoufia Egypt

In this article, a modified version of particle swarm optimization (mpso) and the method of moment (MOM) are combined to achieve the beamforming objective of a practical smart antenna array. The hybrid approach (mpso-MOM) is illustrated by application to a planar uniform circular array (PUCA) with 30 elements of half-wave dipoles. The array feeding is optimized by mpso, and the fitness function is evaluated by MOM simulations. The MOM is used to calculate the response of the array in a mutual coupling environment. The performance of the adaptive array using discrete (quantized) feedings is studied. Also in this article, the convergence capability of the mpso is compared with that of the classical particle swarm optimization and other recent evolutionary-based algorithms using benchmark examples and a linear array synthesis problem.

关键词： evolutionary algorithms mpso algorithm adaptive beamforming PUCA mutual coupling MOM

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Self-Healing Fault Reconstruction Strategy for Island Emergency Power Grid Based on Floyd-mpso

Self-Healing Fault Reconstruction Strategy for Island Emerge...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Li, Binghong Li, Weibo Pan, Junfeng Luo, Ji Wuhan Univ Technol Wuhan Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

In the island power system, due to the special geographical location of the island, variable and unpredictable weather, power outages occur from time to time and power cannot be reliably guaranteed, so it is necessary to establish an emergency power grid to ensure the power supply of important loads in case of faults, and distributed power supplies (DGs) can play an important role with their characteristics of rapid response, simple structure and flexibility. In the fault reconstruction recovery, firstly, the structural characteristics of the distribution network are reasonably optimized to reduce the complexity of the solution;secondly, the key influence of the DG start-up sequence on the recovery path is discussed based on the start-up and operation characteristics of the DG. In order to ensure fast and stable recovery of more important loads, the Floyd-mpso algorithm is combined with the minimum path cost and the maximum recovery of important loads as the objective, and the power balance and other constraints, in which the Floyd algorithm is used to search in advance to obtain the shortest path matrix among DGs with the optimized distribution network as the base, and then the inertia factor of the standard particle swarm optimization algorithm is adjusted adaptively to improve the defect that the particle is easy to fall into the local optimal and cannot jump *** results verify that the combination of the two can improve the effectiveness and speed of fault reconstruction.

关键词： Floyd algorithm mpso algorithm distributed generation start-up sequence fault reconstruction

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Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm

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INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES 2020年第3期27卷 603-615页

作者： Kumar, Sagar Singh, Amit Kumar Pathak, Vimal Kumar Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur 302017 Rajasthan India Manipal Univ Jaipur Dept Mech Engn Jaipur 303007 Rajasthan India

In the present study, a systematic methodology has been presented to determine optimal injection molding conditions for minimizing warpage and shrinkage in a thin wall relay part using modified particle swarm algorithm (mpso). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) have been injected in thin wall relay component under different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi's L-9 (3(2)) orthogonal array has been used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage has been developed in terms of the above process parameters using regression model. ANOVA analysis has been performed to establish statistical significance among the injection molding parameters and the developed model. The developed model has been further optimized using a newly developed modified particle swarm optimization (mpso) algorithm and the process parameters values have been obtained for minimized shrinkage and warpage. Furthermore, the predicted values of the shrinkage and warpage using mpso algorithm have been reduced by approximately 30% as compared to the initial simulation values making more adequate parts.

关键词： Shrinkage Warpage mpso algorithm Optimization

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Optimisation-based training of evolutionary convolution neural network for visual classification applications

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IET COMPUTER VISION 2020年第5期14卷 259-267页

作者： Tu, Shanshan Rehman, Sadaqat ur Waqas, Muhammad Rehman, Obaid ur Yang, Zhongliang Ahmad, Basharat Halim, Zahid Zhao, Wei Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Geosci Ctr Schlumberger Peoples R China Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Khyber Pakhtunkhwa Peoples R China Sarhad Univ Sci & IT Dept Elect Engn Peshawar Pakistan Tsinghua Univ Dept Elect Engn Beijing Peoples R China Ghulam Ishaq Khan Inst Engn Sci & Technol Machine Intelligence Res Grp MInG Khyber Pakhtunkhwa 23640 Pakistan Beijing Electromech Engn Inst Beijing 100074 Peoples R China Namal Inst Dept Comp Sci Mianwali 42250 Pakistan

Training of the convolution neural network (CNN) is a problem of global optimisation. This study proposed a hybrid modified particle swarm optimisation (mpso) and conjugate gradient (CG) algorithm for efficient training of CNN. The training involves mpso-CG to avoid trapping in local minima. Particularly, improvements in the mpso by introducing a novel approach for control parameters, improved parameters updating criteria, a novel parameter in the velocity update equation, and fusion of the CG allows handling the issues in training CNN. In this study, the authors validate the proposed mpso algorithm on three benchmark mathematical test functions and also compared with three different variants of the baseline particle swarm optimisation algorithm. Furthermore, the performance of the proposed mpso-CG is also compared with other training algorithms focusing on the analysis of computational cost, convergence, and accuracy based on a standard problem specific to classification applications on CIFAR-10 dataset and face and skin detection dataset.

关键词： baseline particle swarm optimisation algorithm particle swarm optimisation evolutionary convolution neural network visual classification applications convolutional neural nets velocity update equation training algorithms optimisation mpso–CG improved parameters mpso algorithm control parameters optimisation-based training training CNN image classification global optimisation

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Modeling Permanent Magnet Synchronous Motor System in Electrical Automation Engineering Based on Adaptive Neuro-Fuzzy Inference System

Modeling Permanent Magnet Synchronous Motor System in Electr...

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International Conference on Insulating Materials, Material Application and Electrical Engineering (MAEE 2013)

作者： Zhang, Huiying Jilin Inst Chem Technol Coll Informat & Control Engn Jilin Peoples R China

ISBN: (纸本)9783037856703

This paper proposes a novel modeling method for permanent magnet synchronous motor (PMSM) system in electrical automation engineering based on adaptive network based fuzzy inference system (ANFIS). Meanwhile the microhabitat particle swarm optimization (mpso) was used for training the parameters of ANFIS. The proposed modeling method for PMSM system can help in speed and position control. The proposed ANFIS and mpso based modeling method has been successfully applied to PMSM control system.

关键词： PMSM ANFIS mpso algorithm

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Optimal Scheduling of Microgrid Based on Multi-objective Particle Swarm Optimization algorithm 6

Optimal Scheduling of Microgrid Based on Multi-objective Par...

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6th International Conference on Renewable Energy and Power Engineering (REPE)

作者： Yang, Di CEC Beijing Sci & Tech Dev Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350306194;9798350306187

Safety, stability and efficiency, flexible energy flow, and both economic and environmental benefits are the basis for the low-carbon economic operation of the microgrid. However, with the multi-type distributed power sources and flexible loads of multi-stakeholders connected to the microgrid, the environmental protection and economic dispatch model of the microgrid is established considering the operation cost and environmental cost, and the multi-objective particle swarm optimization (mpso) algorithm is used to solve the optimization model. The effectiveness of the model is verified by Matlab simulation and algorithm comparison, which provides a theoretical basis for the formulation of optimal scheduling strategy for microgrid.

关键词： mpso algorithm microgrid optimal grid scheduling multi-energy microgrid

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A Non Cooperative Game Theoretic Approach for Energy Management in MV grid

A Non Cooperative Game Theoretic Approach for Energy Managem...

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13th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Mangiatordi, F. Pallotti, E. Del Vecchio, P. Fdn Ugo Bordoni Rome Italy Univ Rome Tre Rome Italy

ISBN: (纸本)9781479928026

Demand-side management (DSM) is one of the key functionality of the future power grid as it enables the user to control the energy consumption for an efficient and sustainable allocation of the energy resources. In addition, the DSM promotes the integration of the new renewable sources power generation systems in the traditional electrical grid, improving the balance between local supply and energy demand. This paper proposes a novel DSM technique based on a non-cooperative game framework, to reduce the Peak to Average Ratio (PAR) of the power system, minimizing daily electricity payment of each consumer in the geographical area. Each consumer is considered like a player in an energy game and he/she is encouraged to re-schedule the energy consumption, applying an mpso algorithm to shift in time those loads occurring during peak consumption periods. The dynamic pricing policy applied by the energy providers, leads each player to adopt the best strategy among its Pareto scheduling solutions, to minimize the energy peak in the overall load demand of a geographical area. Simulation results confirm the effectiveness of this distributed game theoretical approach to the DSM problem. An appreciable PAR reduction is achieved at the price of a low information exchange between the energy provider and each consumer, keeping the user privacy safe and minimizing the overhead of signaling information over the network.

关键词： Demand Side Management load scheduling non cooperative game mpso algorithm dynamic energy pricing smart grid

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Warpage and Shrinkage Analysis and Optimization of Rapid Tooling Molded thin Wall Component Using Modified Particle Swarm algorithm

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2019年第1期18卷 85-102页

作者： Kumar, Sagar Singh, Amit Kumar Malaviya Natl Inst Technol Jaipur Rajasthan India

This paper presents a systematic methodology to determine optimal injection molding conditions for minimum warpage and shrinkage in a thin wall relay part using modified particle swarm optimization algorithm ( mpso). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) were injected in a thin wall relay component for different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi's L-9 (3(2)) orthogonal array is used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage is developed in terms of the above process parameters using regression analysis. ANOVA analysis is performed to establish statistical signifficance within the injection molding parameters. The analytical model is further optimized using a newly developed mpso algorithm and the process parameters values are predicted for minimizing shrinkage and warpage. The predicted values of shrinkage and warpage using mpso algorithm are improved by approximately 30% as compared to the initial simulation values and comparable to previous literature results.

关键词： Shrinkage warpage relay component mpso algorithm

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