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16th International Power Electronics and Motion Control Conference and Exposition (PEMC)

作者： Boukra, Tahar Lebaroud, Abdesselam Univ 20 Aout 1955 Skikda Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479920600

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the existence of the fault to failure data is rare and the development of degradation based model is difficult, we must be addressed to the identification of new features having the qualities mentioned above. This paper present an new selection method based upon the particle swarm optimization (PSO) algorithm to identify the advanced prognosis feature and the Hidden Semi Markov Model (HSMM) for the prediction of the remaining useful life. This method was validated on a set of experimental data collected from bearing failures.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection Hidden Semi Markov Model

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Economic Operation Research for Wind-PV-ES Hybrid Micro-grid

Economic Operation Research for Wind-PV-ES Hybrid Micro-grid

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IEEE Grenoble PowerTech (POWERTECH) Conference

作者： Liu, Yanhua Zhang, Nan Zhang, Xu North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467356688

This paper researches economic operation of wind-PV-ES hybrid micro-grid. Firstly, operational strategy of micro-grid and its energy system are proposed. Then the economic operation optimization model is built and particle swarm optimization algorithm is simulated with MATLAB. Based on a practical wind-PV-ES hybrid micro-grid, the simulated results give the optimization cost according to the load and wind power/PV output prediction under kinds of typical weather condition. The results comparison shows that the proposed optimization method is useful to reduce operational cost.

关键词： Economic operation Energy management particle swarm optimization algorithm Wind-PV-ES hybrid micro-grid

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Optimizing Future Predictions in Children's Health: Implementing OGPA-enhanced Deep Learning for Precise Child Height Forecasting in the Social Media Age 27

Optimizing Future Predictions in Children's Health: Implemen...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Shao, Yantao He, Tianxiang Mao, Yan Fang, Kai Wang, Wei Mao, Keji Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China Zhejiang A&F Univ Sch Math & Comp Sci Hangzhou Peoples R China Shenzhen MSU BIT Univ AI Res Inst Guangdong HK Macao Jt Lab Emot Intell & Pervas Co Shenzhen Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children's stage-wise height. When the difference between the actual and predicted values is within +/- 2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.

关键词： Social Media Height Prediction Clustering algorithm Neural Network particle swarm optimization algorithm

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Modeling and Joint optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems

Modeling and Joint Optimization of Security, Latency, and Co...

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IEEE International Conference on Communications (IEEE ICC)

作者： Li, Zukai Tian, Wei Wu, Jingjin BNU HKBU United Int Coll Dept Stat & Data Sci Zhuhai Guangdong Peoples R China Guangdong Prov Key Lab Interdisciplinary Res & Ap Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350333077

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of healthcare systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete particle swarm algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical particle swarm optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios.

关键词： IoT-enabled healthcare blockchain joint optimization integer programming particle swarm optimization algorithm

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A direct numerical method for solving the Foppl-Hencky equation 12

A direct numerical method for solving the Foppl-Hencky equat...

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12th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zhao, Min Zhang, Xiaoping Sun, Yongping Commun Univ Zhejiang Coll Media Engn Hangzhou Peoples R China

ISBN: (纸本)9781728146522

We rewrite the Foppl-Hencky equation as an unconstrained optimization, and then solve the equation by an optimization algorithm. The optimization algorithm is constructed by combining the intelligent algorithms with an numerical algorithm for different equation. We give three numerical experiments, and the simulation results show that the Rounge-Kutta method based particle swarm optimization algorithm solves the equation very well.

关键词： Foppl-Hencky equation particle swarm optimization algorithm Runge-Kutta method

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Research on the Reserve Market Adapted to the New Power System Considering Strategic Quotations of Market Participants 2

Research on the Reserve Market Adapted to the New Power Syst...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Fan, Menghua Lyu, Chen Zhang, Xiangyu He, Xiran Jing, Zhaoxia State Grid Energy Res Inst Beijing Peoples R China State Grid Shanxi Econ Res Inst Taiyuan Peoples R China South China Univ Technol Sch Elect Power Guangzhou Peoples R China

ISBN: (纸本)9798350349047;9798350349030

With a high proportion of renewable resources connected to the system, sufficient flexible regulation capacity is a necessary condition to ensure the safe and economic operation of the power system. There is an urgent need to study and design a market mechanism for reserve service to motivate flexible resources to actively participate in regulation and promote renewable energy consumption. For the reserve ancillary service, existing studies have focused on the identification of market capacity demand and the construction of market clearing models. However, fewer studies have discussed the efficiency of the combined energy-reserve market under different market competition structures from the point of view of allowing market participants to make strategic offers. It is difficult to effectively analyze the impact of the establishment of a reserve market and its coupled clearing with the energy market on the pre-existing market in a real situation. Thus, this paper identifies a joint clearing mechanism for the energy-reserve market that takes into account the participation of demand-side resources such as interruptible loads. A bi-level model with strategic decision-making by market entities at the upper level and clearing transactions by different market mechanisms at the lower level is also established. Subsequent solving by the particle swarm optimization algorithm can obtain accurate market equilibrium results. This paper is based on the IEEE-30 node system for case analysis. To cope with the entities' games, two scenarios of oligopolistic and perfect competition are considered for simulation, and the bidding behavior of the entities and the total system procurement cost are analyzed under both the current domestic mechanism and the joint energy-reserve market, to verify the validity of the identified market mechanism. The conclusions provide a reference for the construction of a reserve market mechanism that supports the development of flexible regulation resources

关键词： reserve market flexibly regulated resources bi-level model particle swarm optimization algorithm renewable energy consumption

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Generic layout optimization design methodology for China's loop-star natural gas field pipeline network

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NATURAL GAS INDUSTRY B 2024年第5期11卷 616-629页

作者： Chen, Shuangqing Liu, Tianqing Dong, Hongli Bao, Yunbo Yu, Ze Guan, Bing Jiang, Xiaoyu Zhou, Shun Wang, Shanlong Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China Daqing Oilfield Co Ltd Postdoctoral Program Daqing 163000 Peoples R China Second Oil Prod Plant Daqing Oilfield Co Ltd Daqing 163000 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163319 Peoples R China Daqing Yushulin Oilfield Dev Co Ltd Daqing 163000 Peoples R China Northeast Petr Univ Inst Unconvent Oil & Gas Daqing 163319 Peoples R China

The layout optimization design of a natural gas gathering pipeline network is a multi-objective optimization problem because the extant theories are unable to meet the different decision preferences in scheme design, which restricts the intelligentization of gas gathering pipeline layout optimization. Currently, there are no generic design studies on the loop-star pipeline network. Therefore, this paper proposes a generic layout optimization model containing a large number of discrete and continuous variables, such as pipe connection relationships, pipe sizes, pipe length, and pipe specifications. In the solution section, drawing inspiration from the hormone regulation mechanism and local foraging rule in bionics, an improved particle swarm optimization algorithm based on hormone regulation (HRPSO) is proposed, and it obtains the favorable parameters range of the HRPSO algorithm. The results illustrate that the HRPSO algorithm exhibits convergence to the global optimum with a probability of 1. In comparison to manual design, the comprehensive costs of the optimized scheme are saved by 22.71% with the HRPSO algorithm. Compared to the four PSO variants in the paper, it can save costs by 5.38%, 4.95%, 4.09%, and 3.65%, respectively. (c) 2024 Sichuan Petroleum Administration. Publishing services by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Natural gas gathering Generic layout optimization model particle swarm optimization algorithm Convergence Comprehensive cost

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Identification of convection heat transfer coefficient parameters based on hybrid particle swann algorithm in the secondary cooling zone for steel continuous casting process

Identification of convection heat transfer coefficient param...

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ICSC Congress on Computational Intelligence Methods and Applications

作者： Wu, Guo-Hua Wu, Rong-Yang Dalian Maritime Univ Coll Econ & Management Dalian 116026 Peoples R China

ISBN: (纸本)1424400201

Solidification model is developed based on control volume method for steel continuous casting process, which is nonlinear and non-differential, and parameters of convective heat transfer coefficient for each segment of the secondary cooling zone are ascertained, a new hybrid particle swarm algorithm (CPSO) is introduced to improve the optimizing performance by embedding the chaotic search in the particles swarm algorithm. It is used to identify the convective heat transfer coefficients according to billet surface temperature and shell thickness. Compared with the empirical formula method, it has a better agreement with trail data.

关键词： billet continuous casting convection heat coefficient chaotic particle swarm optimization algorithm simulation

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Prediction Model of MBR Membrane Flux for Elman Neural Network Based on PSO-GA Hybrid algorithm 8

Prediction Model of MBR Membrane Flux for Elman Neural Netwo...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Wang, Xinchang Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software Tianjin Peoples R China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682463

Membrane fouling reduces wastewater treatment efficiency and cause financial and energy costs to some extent. The size of membrane flux reflects the degree of membrane pollution. Make timely cleaning membrane or replacement membrane decision to maintain considerable treatment effect on the basis of the membrane flux. particle swarm optimization (PSO) algorithm can quickly find the global optimum. Genetic algorithm (GA) has the property of global convergence. The prediction model used in this paper is based on the PSO-GA hybrid algorithm. The combination of these not only improves the convergence speed of genetic algorithm but also reduces the probability of particle swarm optimization algorithm falling into local optimum. Elman neural network acts as the basic network. Compared with Elman neural network and BP neural network, the prediction accuracy of PSO-GA-Elman is improved.

关键词： MBR particle swarm optimization algorithm Genetic algorithm Prediction Model

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Application of PSO-GWO optimization algorithm for PI optimization in permanent magnet synchronous motor 7

Application of PSO-GWO optimization algorithm for PI optimiz...

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2023 7th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2023

作者： Xu, Xiao Peng, Qian School of Mechanical and Automotive Engineering Xiamen University of Technology Fujian Xiamen China

ISBN: (纸本)9798400708305

A PI (proportional integral) control parameter optimization method based on particle swarm optimization improved grey wolf algorithm is proposed to address the issues of insufficient parameter adjustment accuracy, slow response speed, and poor stability in traditional PI control for permanent magnet synchronous motors (PMSM). To improve the issue of the traditional grey wolf optimization algorithm easily falling into local optima and causing a decrease in accuracy, the position update strategy of the particle swarm algorithm is applied to the position update of the grey wolf algorithm, effectively enhancing the global optimization capacity of the grey wolf algorithm. By establishing a simulation model of the PMSM vector control system and conducting hardware-in-the-loop experimental tests, the motor system response of the improved grey wolf algorithm and traditional PI control is compared and analyzed. The results show that compared to optimizing PI control parameters using traditional methods, the improved grey wolf algorithm significantly enhances the response speed, anti-interference capability, and robustness of the PMSM control system. © 2023 ACM.

关键词： Improve the Grey Wolf algorithm particle swarm optimization algorithm Permanent magnet synchronous motor PI control paramet

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