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Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

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SN APPLIED SCIENCES 2023年第5期5卷 1-12页

作者： Shen, Weinan Suzhou Vocat Univ Sch Management Suzhou 215000 Jiangsu Peoples R China

In this study, we present a highly practical marketing course. Through learning marketing, we can cultivate students' ability to analyze, solve and practice problems. Therefore, this paper would study the problems encountered in the marketing course through cloud computing technology and propose corresponding countermeasures, so as to better improve students' ability to analyze and solve problems and enhance the effectiveness of the classroom. This paper also analyzed the data aggregation algorithm based on particle swarm optimization (PSO) algorithm. The results showed that when other conditions were the same, only 6 students approve of the traditional marketing teaching mode, accounting for 12%;there were 44 people who did not approve, accounting for 88%. This number was more than half;49 students recognized the optimized marketing teaching mode, accounting for 98%;there was 1 person who did not approve, accounting for 2%. The proportion of people who did not approve after optimization was far less than that of the traditional model, which showed that cloud computing data aggregation algorithm could find out the problems in marketing teaching and provide correct countermeasures to solve them, so that the teaching model could be optimized. (1) The data aggregation algorithm based on cloud computing has a good optimization effect on the evaluation of marketing teaching problems and countermeasures, indicating a new direction for future research. (2) The combination of cloud computing and data aggregation algorithm is not only a novel research perspective, but also a perfect idea. (3) Marketing course is a very important course in today's society. The study of its teaching problems and countermeasures evaluation will help students improve their learning efficiency and enhance their ability to adapt to the society.

关键词： Marketing teaching Cloud computing Data aggregation algorithm Analysis of problems and countermeasures particle swarm optimization algorithm

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Lyapunov Control of High-Dimensional Closed Quantum Systems Based on particle swarm optimization

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IEEE ACCESS 2020年 8卷 49765-49774页

作者： Guan, Xiaoke Kuang, Sen Lu, Xiujuan Yan, Jiazhen Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

For high-dimensional closed quantum systems, this paper proposes a novel quantum Lyapunov control scheme based on the particle swarm optimization algorithm and achieves a high-probability population transfer of the system to a non-isolated target eigenstate in the LaSalle invariant set under usual smooth Lyapunov control laws. Via a quadratic Lyapunov function with unknown parameters, a control law with the unknown parameters is designed;based on the LaSalle invariance principle and the energy-level connectivity graph, the stability of the system is analyzed;by using the particle swarm optimization algorithm, a set of optimal parameters is obtained to achieve the control goal. In particular, we propose a path planning method based on the energy-level connectivity graph to determine the initial values of the unknown parameters, which is such that the optimization algorithm can efficiently and conveniently find a set of optimal solutions of the unknown parameters. Numerical simulation experiments on a five-level quantum system and a three-qubit system demonstrate that the proposed Lyapunov control scheme based on the particle swarm optimization algorithm has a good control effect.

关键词： Lyapunov methods Sociology Statistics particle swarm optimization Control systems Quantum entanglement High-dimensional quantum systems Lyapunov control particle swarm optimization algorithm parameter optimization initial values energy-level connectivity graph

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Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

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Energy Engineering 2023年第5期120卷 1185-1201页

作者： Yuqian Qi Mingshui Li Yu Lu Baitong Li Tianjin University Tianjin300110China

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is *** on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is *** on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered *** proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution *** simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.

关键词： Shore power harmonic control multi objective optimization particle swarm optimization algorithm

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D-PSO: An optimization algorithm for Wireless Sensor Network Layout in Power Systems 9

D-PSO: An Optimization Algorithm for Wireless Sensor Network...

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9th International Conference on Automation, Control and Robotics Engineering (CACRE)

作者： Ma, Pengwei Zhu, Yuanjiao Sun, Zheng Wei, Jingyi Luo, Yunchen Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this paper proposes a novel method named the Distributed particle swarm optimization algorithm (D-PSO). This method mitigates the premature convergence issue of heuristic algorithms by introducing a regional operator. Additionally, considering the high-interference environment in power systems, relay node strategy (RNS) is incorporated to ensure communication quality. Simulation results validate the effectiveness and superiority of the D-PSO method and demonstrate the necessity of the RNS. Compared to some advanced particle swarm algorithms, this method better balances energy consumption, coverage, and communication quality, thereby significantly enhancing the overall performance of the wireless sensor network.

关键词： wireless sensor network power systems particle swarm optimization algorithm

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Automatic scheduling and control technology of pump gate clusters of regional water conservancy project

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DESALINATION AND WATER TREATMENT 2023年 293卷 82-88页

作者： Sun, Aijun Hu, Donghui Shan, Chengju Wang, Jun Yuyao Water Resources Bur Ningbo 315470 Peoples R China Zhejiang Univ Water Resources & Elect Power Hangzhou 310018 Peoples R China

With the frequent regional flood disasters in recent years, the study of automatic scheduling and control technology of pump gate clusters has become crucial in related academic circles. Here, an automatic flood control and scheduling model for pump and gate clusters of water conservancy projects is constructed by combining hormone regulation and particle swarm algorithm. The model can regulate the maximum discharge flow and maximum water level of reservoirs according to the actual situation in order to achieve flood prevention and consumption prevention. The model is validated using two reservoirs in city A as an example. The results show that the maximum dis-charge flow of the two reservoirs after the model treatment is 1,996.16 and 16,738.28 m3/s, which reduces the incidence of flooding in city A. In addition, after the optimal flood control scheduling, the maximum water levels of the once-in-a-century flood and once-in-two-centuries flood in city A become 80.41 and 81.66 m, respectively, which are in the safe water level range. The study is of great significance for the reduction of flooding.

关键词： Regional water conservancy project Pump gate clusters Hormone regulation particle swarm optimization algorithm Scheduling and control

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Research Review of Ship Power Load Forecasting Based on Intelligent optimization algorithms

Research Review of Ship Power Load Forecasting Based on Inte...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Meng, Ningjia Mei, Jiahao Shen, Kang Liang, Tianqin Zhang, Yuanyuan Xie, Jijin Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou Peoples R China Beibu Gulf Univ Eastern Michigan Joint Coll Engn Qinzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Amidst the swift advancements of the global shipping industry and the escalating demand for energy conservation and emission reduction, ship power load forecasting has emerged as a pivotal research domain to guarantee the stable operation of ship power systems. Precise power load forecasting not only optimizes the scheduling and management of ship power systems but also markedly enhances fuel utilization efficiency and mitigates emissions. In recent years, intelligent optimization algorithms, renowned for their notable advantages in addressing complex nonlinear problems, have been increasingly applied to the study of ship power load forecasting. This paper delves into ship power load forecasting methodologies predicated on intelligent optimization algorithms, with a primary focus on the application of genetic algorithms, particle swarm optimization algorithms, and artificial neural networks. These algorithms emulate natural evolution, swarm intelligence, and neural system mechanisms, thereby effectively addressing complex power load forecasting challenges. In conclusion, ship power load forecasting founded on intelligent optimization algorithms holds substantial theoretical significance and extensive application prospects. With the advancement and application of related technologies, the operational efficiency and reliability of ship power systems will be further augmented, thereby furnishing robust support for attaining green shipping and sustainable development.

关键词： intelligent optimization algorithms ship power load forecasting genetic algorithm particle swarm optimization algorithm

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Hierarchical Optimal Scheduling Strategy for Electric Vehicles with Dynamic Time-of-Use Tariff

Hierarchical Optimal Scheduling Strategy for Electric Vehicl...

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International Conference on Wireless Power Transfer (ICWPT)

作者： Wang, ChuangXin Chen, Zhong Changsha Univ Technol Sch Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9789819708796;9789819708772;9789819708765

The disorderly charging tactics of large-scale electric vehicle (EV) will lead to the increase of peak-valley load difference of the power grid and destroy the stability of the power system. In order to solve this problem, considering the peak adjustment demand of the grid side and the different demands of the users on the charging amount and charging cost, this paper proposes a hierarchical optimization scheduling method based on the dynamic TOU time segment model. This method updates the load curve according to the load information of each EV when it is connected to the grid. Meanwhile, the dynamic time-of-use electricity price model proposed in this paper is used to dynamically update the peak-valley electricity price of the EV. Among them, the upper layer model takes the minimum variance of the total load of the grid as the objective function, and the lower layer model takes the minimum deviation of the agent scheduling plan, the maximum amount of charging and the minimum cost of charging as the objective function, and adopts the improved PSO algorithm to optimize the charging (discharging) tactics of each EV. Finally, the simulation analysis is carried out with a concrete example. The results show that this method can effectively reduce the peak-valley difference of load demand curve on the premise of ensuring the travel demand and economic benefits of EV owners.

关键词： particle swarm optimization algorithm user responsiveness electric vehicle orderly charge and discharge

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Selective Maintenance of Multistate Systems Considering the Random Uncertainty of the System Mission Period and Mission Breaks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第5期48卷 7059-7075页

作者： Wang, Hai-Peng Duan, Fu-Hai Wang, Xiao-Long He, Yu-Ling North China Elect Power Univ Dept Mech Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance Fa Baoding 071003 Hebei Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Liaoning Peoples R China

A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particle swarm optimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained.

关键词： Selective maintenance Multistate system Random uncertainty particle swarm optimization algorithm

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Blockchain-Based Power Transaction Method for Active Distribution Network

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Energy Engineering 2023年第5期120卷 1067-1080页

作者： Fei Zeng Zhinong Wei Haiteng Han Yang Chen College of Energy and Electrical Engineering Hohai UniversityNanjing211100China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213164China

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading ***,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in ***,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware ***,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power *** experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

关键词： Blockchain active distribution network power transaction energy request mechanism particle swarm optimization algorithm

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A new two-axis solar tracker based on the online optimization method:Experimental investigation and neural network modeling

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Energy and AI 2023年第4期14卷 341-359页

作者： Amir Pouya Masoumi Vahid Bagherian Ali Reza Tavakolpour-Saleh Elham Masoomi Department of Mechanical and Aerospace Engineering Shiraz University of TechnologyShirazIran Department of Mechanical Engineering Shiraz UniversityShirazIran Department of Computer Engineering Qazvin BranchIslamic Azad UniversityQazvinIran

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement *** this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the *** magnitude of the sunray is considered as the cost function of all ***,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization *** initialization leads to faster convergence compared to random *** results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 *** average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other *** also performs well with a population size of 15 and 7 ***,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from *** of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network *** performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target ***,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.

关键词： Intelligent two-axis solar tracker optimization algorithm particle swarm optimization algorithm Online optimization method Artificial neural network

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