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Air quality prediction using a novel three-stage model based on time series decomposition

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-26页

作者： Sun, Mingyue Rao, Congjun Hu, Zhuo Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Air pollution control is one of the most important aspects of sustainable development in modern times, in which air quality prediction is the key in the process of governance. How to further improve the accuracy of air quality prediction and provide more powerful decision support for relevant departments is the main task of this paper. The air quality index (AQI) is a comprehensive indicator used to measure the air quality level. Taking AQI as the research object, this paper establishes a three-stage prediction model based on time series decomposition with improved particle swarm optimization-back propagation neural network (IPSO-BPNN), and makes empirical analysis of AQI in Wuhan City, China from 2014 to 2023. Specifically, at the first stage, the raw data is decomposed into season, trend, cycle and error subsequences by means of seasonal decomposition and empirical mode decomposition. At the second stage, the adaptive formula for the inertia weight in the particle swarm algorithm is newly defined by combining the sigmoid function with the scaling factor and control factor, and the improved particle swarm algorithm is used to optimize the initial weights and thresholds of BP neutral network. The cycle and error subsequences are predicted using this hybrid model. At the last stage, the four subsequences are recombined to obtain predictions of AQI. The empirical analysis results show that the MAPE of this hybrid prediction model reaches 0.04823, which is lower than the other two comparative models. The experimental results prove that the hybrid prediction model proposed in this paper can make an effective prediction.

关键词： Sustainable development Air quality prediction Time series decomposition improved particle swarm algorithm IPSO-BPNN model

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Research on Optimization of Project Time-Cost-Quality Based on particle swarm Optimization

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INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT 2019年第2期12卷 76-88页

作者： Song, Yanqing Hou, Genran Yellow River Conservancy Tech Inst Kaifeng Peoples R China

In order to make proper time-cost-quality decisions for projects, an improved particle swarm optimization algorithm is applied. First, the optimal model of project time-cost-quality is constructed considering all factors. Second, the basic theory of particle swarm algorithms is summarized, and the improved particle swarm algorithm is put forward based on vector principle, and then the rotational base technology is introduced into the improved particle swarm algorithm to construct a multiple objective optimization algorithm. Finally, the simulation analysis is carried out using a project as example, and the optimal parameters are obtained.

关键词： improved particle swarm algorithm Optimization particle swarm algorithm Time-Cost-Quality Optimization

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Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第3期55卷 827-838页

作者： Dong, Liang Chen, Zeyu Hua, Runan Hu, Siyuan Fan, Chuanhan Xiao, Xingxin Jiangsu Univ Natl Res Ctr Pumps Zhenjiang 212013 Jiangsu Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430060 Hubei Peoples R China

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and nonstationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%. (c) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the

关键词： improved particle swarm algorithm Variational modal decomposition Kullback-Leibler divergence value Relevance vector machine Centrifugal pump rotor faults

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Evaluation of cloud computing resource scheduling based on improved optimization algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2021年第4期7卷 1817-1822页

作者： Yu, Huafeng Zhejiang Tech Inst Econ Sch Digital Informat Technol Hangzhou 310018 Zhejiang Peoples R China

Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

关键词： Cloud computing improved particle swarm algorithm CloudSim Resource scheduling

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Optimization of the grinding trajectory of the engine piston skirt robot based on machine vision

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SN APPLIED SCIENCES 2023年第4期5卷 1-22页

作者： Lu, Na Wang, Youmin Li, Jun Anhui Polytech Univ Sch Mech Engn Wuhu 241000 Anhui Peoples R China

In this study, we establish the monocular vision system of the grinding path, complete the calibration experiment, automatically extract the grinding path type value points of the engine piston skirt, use the edge detection operator to detect the contour of the grinding path, and use Harris corner point detection to obtain the pixel coordinates of the grinding path type value points, in this study, the robot trajectory planning method and its evaluation criteria are proposed, the trajectory planning under joint space is carried out for the data points, and simulation experiments are conducted, the time-optimal trajectory optimization model is established, the kinematic constraints are imposed on each joint, and the time-optimal trajectory optimization of the grinding trajectory is carried out based on five-order B-spline trajectory planning with the improved particle swarm algorithm, and the optimal trajectory is obtained, and the robot running time is reduced by The optimal trajectory is obtained, and the robot running time is reduced by 3.186 s, which verifies the effectiveness of the new algorithm for grinding path time optimization.

关键词： Piston skirt Machine vision Image processing Grinding Five-order B-spline Trajectory planning improved particle swarm algorithm

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Credibility Evaluation of Web Big Data Information Based on particle swarm Optimization

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JOURNAL OF WEB ENGINEERING 2022年第2期21卷 405-423页

作者： Zhao, Nannan Guangdong Ocean Univ Yangjiang Sch Comp Sci & Engn Yangjiang Peoples R China

In order to improve the credibility evaluation effectiveness of web big data information, the improved particle swarm optimization is established. Firstly, framework of web big data is designed to include web big data source, data storage, data processing and data analysis. The global credibility calculation formula of whole web is established. Secondly, the improved particle swarm algorithm is constructed through updating weight and training factor, introducing cross and mutation operations into the algorithm, and improving population diversification based on mountain climbing algorithm. Finally, ten sample industries and one hundred sample stocks are selected to carry out experiment analysis, and the results show that the proposed algorithm can effectively distinguish trusted and untrustworthy records. And the proposed algorithm of calculating credibility of web big data has high reasonableness.

关键词： Credibility evaluation web big data improved particle swarm algorithm

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An improved Power Control Strategy for Induction Heating Power Supply Based on PSO algorithm

An Improved Power Control Strategy for Induction Heating Pow...

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International Conference on Wireless Power Transfer (ICWPT)

作者： Li, Hui Xu, Sheng Chen, Lezhu Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243000 Peoples R China Taizhou Univ Taizhou Elect Energy Convers & Control Res Ctr Taizhou 215300 Peoples R China

ISBN: (纸本)9789819708710;9789819708697;9789819708680

The traditional PI control suffers from the problems of difficult parameter rectification and poor dynamic performance, which cannot meet the power control requirements of modern high-frequency induction heating power supply. By improving the inertia weight coefficients and learning factors of the traditional particle swarm optimization (PSO) algorithm and combining them with the immune algorithm, a PI control strategy based on the improved immune PSO algorithm is proposed, which effectively avoids the problems of the traditional PSO algorithm, such as the algorithm being prone to premature maturity, poor global search capability and low efficiency of the optimization search. Simulation and experimental results show that, based on this control strategy, the optimal parameter adjustment of the PI controller has a faster response speed and can improve the dynamic and static performance of the power regulation of high-frequency induction heating power supplies.

关键词： induction heating power improved particle swarm algorithm power control parameter optimization

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Research and Simulation of Dual-level Optimal Scheduling of Industrial Park Microgrid Considering Flexible Load 8

Research and Simulation of Dual-level Optimal Scheduling of ...

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8th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Ge, Siwei Yu, Fei Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao Peoples R China

ISBN: (纸本)9781665400886

At present, with the proposal of carbon neutrality goals, more and more industrial parks have responded to the policy to build a large number of smart micro -grid systems to save energy and reduce emissions, improve the capacity of new energy consumption and the economic efficiency of the park's power supply. In this paper, the power supply background of the industrial park, integrating microgrid system's source, nehvork, load and storage capabilities as well as characteristics, study the microgrid's economic scheduling strategy, and build a dual -level scheduling model of the microgrid system include day-ahead and real-time scheduling. The day-ahead model is solved by an improved particle swarm optimization algorithm with adaptive mutation optimization to obtain the optimal economic scheduling. In order to correct the day-ahead forecast errors and smaller gradual change of power, a strategy of real-time adjustment is adopted to make the real-time operation follow the day-ached results to the greatest extent and ensure the economy and reliability of the microgrid. This real-time adjustment strategy takes into account of the flexible load and controllable micro-source coordination. To this end, this paper verifies the feasibility of the strategy by establishing a real-time simulation model in real-time simulator.

关键词： carbon neutrality park microgrid scheduling improved particle swarm algorithm real-time simulation

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Optimization and Development of Marine Engineering Technology Based on Intelligent Control 7

Optimization and Development of Marine Engineering Technolog...

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7th International Conference on Robotics, Control and Automation (ICRCA)

作者： Zhang, Jinliang Tianjin Univ Technol Maritime Coll Tianjin 300384 Peoples R China

ISBN: (纸本)9798350345780

Since the steam turbine unit is the main part of the whole propulsion system, its normal operation is directly related to the safety of the whole ship system. Therefore, it is necessary to introduce an optimization scheme into the design of the unit to improve the safety, reliability, operability and rational layout of the unit. This paper aims to study the optimization and development of hydraulic turbine engineering technology based on intelligent control. On the basis of analyzing the characteristics and working principle of the water turbine, an improved particle swarm optimization algorithm is proposed, and the differential evolution algorithm is introduced into the basic particle swarm optimization algorithm to obtain an improved particle swarm optimization algorithm. Subsequently, this paper establishes a mathematical model of a ship's steam turbine, and designs its high-pressure steam turbine and low-pressure steam turbine. Finally, the improved particle swarm optimization algorithm is used to optimize the design of the ship turbine under the specific structure and characteristic constraints, with the minimum weight as the objective function. The experimental results show that the weight of the turbine is significantly reduced after the optimization method is selected, which proves the effectiveness of the improved particle swarm optimization algorithm for the optimization of the turbine.

关键词： Intelligent Control Ship Turbine Turbine Unit Weight improved particle swarm algorithm

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The Study of MPPT Control Strategy Based on Chaotic particle swarm Optimization 4

The Study of MPPT Control Strategy Based on Chaotic Particle...

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4th Power System and Green Energy Conference (PSGEC)

作者： Cai, Zixuan Liu, Yili Zhu, Xiaolong Zhang, Hengxiang Xian Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9798350365573;9798350365580

When the PV array is shaded, the conventional maximum power tracking strategy cannot effectively track the maximum power point. Firstly, the equivalent model of photovoltaic cells is established, and the essential features of photovoltaic cells output are then examined under various lighting and temperature scenarios. Then, in view of the shortcomings of the particle swarm MPPT control strategy, the core parameters of PSO were dynamically improved and the initialization link was optimized, in order to optimize the improved particle swarm MPPT algorithm, it is crucial to enhance its capacity to track the maximum power of the photovoltaic array under any shadowing conditions and to significantly speed up the time needed to reach the optimal operating point. Finally, the MPPT control strategies based on P&O, PSO and IPSO are simulated and compared in Simulink, and the findings indicate that the method proposed in this paper successfully tracks the maximum power in a variety of environments. This approach also ensures a reduced number of oscillations and a superior convergence speed.

关键词： photovoltaic power generation Maximum power tracking improved particle swarm algorithm Chaos mapping

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