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International Conference on Advances in Materials Research (ICAMR)

作者： Faizal, U. Mohamed Jayachitra, R. Vijayakumar, P. Rajasekar, M. Sri Shakthi Inst Engn & Technol Dept Mech Engn Coimbatore 641062 Tamil Nadu India PSG Coll Technol Dept Mech Engn Coimbatore 641062 Tamil Nadu India

In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.

关键词： Vehicle routing Bio-medical waste Time delay Inbound logistics particle swarm optimization algorithm

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Research on RFID Virtual Tag Location algorithm Based on Monte Carlo 13

Research on RFID Virtual Tag Location Algorithm Based on Mon...

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IEEE 13th International Conference on Computer Research and Development (ICCRD)

作者： Guan, Tingliang Wang, Ding Su, Yixin CNNP Nucl Power Operat Management Co Ltd Dept Purchasing Management Jaxing Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665422604

Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation methods of virtual reference tag signal strength on positioning accuracy, and proposes a RFID virtual tag positioning algorithm based on Monte Carlo. The algorithm uses dynamic particles to replace the traditional static reference tag;introduces particle swarm optimization algorithm to update the Monte Carlo sample particle swarm, and gives different weights to the sampling particles based on the signal strength difference between the sampling particles and the undetermined tags, and finally completes the localization of the unknown tags through Monte Carlo resampling. The simulation results show that the algorithm can effectively improve the accuracy and stability of RFID positioning system compared with the traditional virtual tag positioning algorithm.

关键词： RFID VIRE algorithm Monte Carlo particle swarm optimization algorithm

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Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour

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ENERGY 2022年第0期243卷 122976-122976页

作者： Li, Bin Dong, Xujun Wen, Jianghui Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430063 Peoples R China

Recently, electric vehicles (EVs) have become an important traffic tool in connected and automated environments due to their own advantages. However, the optimal driving control strategies of EVs and charging modes of wireless charging lanes are complex since wireless charging technology is limited when EVs are moving and traffic conditions are random. This paper constructs a scenario in which wireless charging facilities are deployed in part of a single lane and proposes a cooperative control method of mixed fleets including EVs and fuel vehicles based on the four objectives of driving safety, traffic efficiency, passenger comfort and energy management. Combined with the particle swarm optimization algorithm, the average driving velocity, lane-changing frequency and location of vehicles under different deployment lengths of charging facilities and mixing ratios are compared to discuss the influence of deployment length on the control effect of energy consumption and traffic efficiency. The numerical results indicate that when the lane length is 1500 m, with the increasing proportion of EVs, the optimal deployment lengths are approximately 700 m, 550 m and 400 m in the case of low traffic density and approximately 550 m, 500 m and 450 m in the case of high traffic density.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Cooperative control method of mixed fleet particle swarm optimization algorithm Optimal deployment Energy consumption

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ADRC Based on Artificial Neural Network for a Six-rotor UAV 40

ADRC Based on Artificial Neural Network for a Six-rotor UAV

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40th Chinese Control Conference (CCC)

作者： Xi, Lin Shao, Yunfeng Zou, Suli Ma, Zhongjing Beijing Inst Technol Beijing 100081 Peoples R China Shanxi Elect Power Co Lvliang Power Supply Co Lvliang 033000 Peoples R China

ISBN: (纸本)9789881563804

An on-line intelligent optimization method based on an artificial neural network is proposed for the parameter adjustment of the active disturbance rejection controller. And a cascaded ADRC controller including the artificial neural network attitude ADRC is investigated for trajectory tracking of the six-rotor UAV. First, establish the kinematics and dynamics model of the six-rotor, and design a cascaded active disturbance rejection controller for the six-rotor to deal with the non-linear disturbance problem in flight. Secondly, an artificial neural network is designed to optimize the parameters of the attitude ADRC controller on-line, and the particle swarm algorithm is used to set the initial value of the artificial neural network. Finally, the simulation results demonstrated that ADRC based on the artificial neural network can effectively resist the disturbances and enhance the robustness of the attitude controller and the cascade ADRC controller based on the artificial neural network can track the reference trajectory quickly and accurately.

关键词： active disturbance rejection control particle swarm optimization algorithm artificial neural network

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Multi-Objective Optimal Control for Distribution Network Loop-Closing Operation Using PE-MOPSO algorithm 6

Multi-Objective Optimal Control for Distribution Network Loo...

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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Zhou, Shengchao Zhou, Yanping Wei, Yucheng Kang, Linchun Zhang, Xiaoli Yunnan Power Grid Co Ltd Kunming Power Supply Bur Kunming 650011 Yunnan Peoples R China

ISBN: (纸本)9781665432634

The loop-closing operation is usually used to transfer power supply without power interruption in the distribution network, but the excessive current induced by loop-closing will endanger the safety and stability of the distribution network. This paper presents a multi-objective optimal control method of loop-closing in distribution network. Firstly, the mathematical optimization model of loop-closing optimal regulation is established, which not only ensures the security of loop-closing operation, but also takes into account the stability and voltage quality of distribution network operation. Then, Pareto entropy-based multi-objective particle swarm optimization (PE-MOPSO) algorithm is proposed to solve the optimization problem. Finally, an actual example is simulated based on MATLAB platform. The simulation results show that the optimization model is correct, and PE-MOPSO algorithm has good diversity and convergence, and can give a reasonable control scheme.

关键词： Distribution network loop-closing optimal control Pareto entropy particle swarm optimization algorithm

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Short-Term Wind Generation Combined Forecast Considering Meteorological Similarity

Short-Term Wind Generation Combined Forecast Considering Met...

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IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Liu, Yating Yang, Ming Yu, Yixiao Ding, Tingting Si, Zhiyuan Li, Menglin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.

关键词： BP neural network algorithm Gaussian transform particle swarm optimization algorithm XGBoost short-term wind power combined forecast

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optimization control of hydrometallurgical leaching process based on IDE-MPC

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MINERALS ENGINEERING 2022年第0期176卷 107341-107341页

作者： Niu, Dapeng Liu, Xudong Liu, Yuanqing Jia, Mingxing Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

The utilization rate of raw materials during the hydrometallurgical leaching process has a great influence on the whole economic benefits of the hydrometallurgy plant, so it is necessary for the leaching process to improve the utilization of materials using optimization control methods. In this paper, the dynamic model for the hydrometallurgical leaching process of a gold hydrometallurgy plant is first built based on the reaction mechanism of the process. Then, the model parameters are identified using least-squares fitting. Thereafter, with the maximum economic benefit as the objective function, the steady-state economic optimization model of the leaching process is established, and an improved particle swarm optimization algorithm is used to solve the model. Taking the optimization results as the control objective, a model predictive control method based on an improved differential evolution algorithm is proposed to control the leaching process, so as to improve the intractability and anti-disturbance performance of the controller for the leaching process. The simulation results show that the proposed optimization and control methods achieve satisfactory effects.

关键词： Hydrometallurgical leaching process optimization control particle swarm optimization algorithm Model predictive control Differential evolution algorithm

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Relay Protection Setting Calculation System for Nuclear Power Plant Based on B/S Architecture and Cloud Computing

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ENERGIES 2022年第24期15卷 9648页

作者： Hong, Yuan Yu, You Tian, Jingfu Ye, Han Wang, Bin Yu, Wenxiang Shandong JiaoTong Univ Sch Informat & Elect Engn Jinan 250357 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 110004 Peoples R China Shan Dong Cheersun Intelligent Technol Co Ltd Jinan 250101 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gener Haidian Dist Beijing 100084 Peoples R China

Nuclear power plants have a complex structure and changeable operation mode, which induces low setting calculation efficiency. After analyzing the technology, architecture, and functional logic of a variety of relay protection setting calculation systems and combining the characteristics of the setting calculation of nuclear power plants, the relay protection setting calculation system in nuclear power plants based on B/S architecture and cloud computing is studied in this paper. The system adopts three-tier B/S architecture, applies two key technologies, the cloud computing task distribution synchronization mechanism and the cloud component automatic assembly mechanism, and introduces a particle swarm optimization algorithm to provide technical support for nuclear power plant setting calculation;the running example of the nuclear power plant system fully proves the efficiency and reliability of the relay protection setting calculation system of the nuclear power plant, which has high practical value.

关键词： setting calculation three-tier B S architecture cloud computing cloud component particle swarm optimization algorithm

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An improved two-layer model for rumor propagation considering time delay and event-triggered impulsive control strategy

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CHAOS SOLITONS & FRACTALS 2022年第0期164卷

作者： Dong, Yafang Huo, Liang'an Zhao, Laijun Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The rapid development of the Internet has broadened the existing channels of rumor propagation, it has added many new features during the dynamic process of the rumor propagation that are different from those in the past. In this paper, we construct an improved XYZ -ISR two-layer model considering time delay to describe the dynamic process of rumor propagation in multiple channels. Firstly, we obtain the basic regeneration number R0 by the next generation matrix method. Secondly, we derive the stability condition of the equilibrium point by constructing Lyapunov functions. In order to reduce the negative impact caused by rumor propagation at min-imum cost, we design an event-triggered impulsive control strategy and optimize the control parameters by using a particle swarm optimization algorithm. Then, by comparing the rumor propagation process in the XYZ -ISR two-layer model and the classical rumor DK model, we find that although rumors propagate faster in the XYZ -ISR model in the initial stage, the rumor spreads on a smaller scale than in the DK model in terms of the whole life cycle of the rumor due to the official supervision and timely release of corresponding rumor-debunking infor-mation. Besides, individuals need a certain amount of careful consideration time between receiving the infor-mation and deciding whether to forward it or not, and this time delay slows the behavior spreading and reduces its scale. Finally, we verify the reasonableness of the above theoretical results through numerical simulations. Moreover, by simulating a rumor case on the microblog with real data, we find that the model proposed in this paper can fit the real rumor propagation process well, and the event-triggered impulsive control strategy can control the rumor propagation more effectively in multiple channels.

关键词： Rumor propagation Two -layer model Time delay Event -triggered impulsive control particle swarm optimization algorithm

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Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

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SENSORS 2022年第19期22卷 7249-7249页

作者： Huo, Zimin Wang, Fuchao Shen, Honghai Sun, Xin Zhang, Jingzhong Li, Yaobin Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China Forest Protect Res Inst Heilongjiang Prov Harbin 150040 Peoples R China

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the particle swarm optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively;compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

关键词： MEMS gyroscope convolutional denoising autoencoder temporal convolutional network attention mechanism particle swarm optimization algorithm Kalman filter

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