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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Wu, Yue Li, Guoping Zhang, Zhiwei He, Longlong Xian Univ Sci & Technol Sch Mech Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9798350363272;9798350363265

As an important part of coal mining mechanization, the reliability of hydraulic support is an important guarantee for the hydraulic support system of the comprehensive mining face, and the fast, accurate, and intelligent selection of hydraulic support determines its reliability. To realize the intelligent selection of hydraulic support for the slow inclined comprehensive mining face, and to improve the efficiency and accuracy of the selection of the hydraulic support, a hydraulic support selection method based on the PSO-BP neural network is proposed in this paper. A relationship model between geological parameters and hydraulic support technical parameters is established, and the model is trained by using hydraulic support selection case data as samples. Based on the training results, the model can be used to input new geological parameters to intelligently recommend the selection plan of hydraulic support technical parameters. The actual selection scheme of a coal mine is analyzed as a case study, and the results are compared with the traditional BP neural network selection results. The results indicate that the PSO-BP neural network can efficiently generate the hydraulic support technical parameter selection program. It outperforms the traditional BP neural network in terms of computational accuracy and convergence speed. This provides a reliable reference for intelligent hydraulic support selection.

关键词： hydraulic bracket particle swarm optimization algorithm BP neural network intelligent selection

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SA-PSO Approach for Optimizing QoS Management in SDN Service Chains

SA-PSO Approach for Optimizing QoS Management in SDN Service...

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2024 IEEE Cyber Science and Technology Congress

作者： Ji, Wen Lin, Chenhan Liu, Jiang Xu, Hongyang Xu, Liangjie Hu, Jun State Grid Fujian Elect Power Co Ltd Informat & Commun Branch Fuzhou Peoples R China Anhui Jiyuan Software Co Ltd Syst Integrat Business Unit Hefei Peoples R China

ISBN: (纸本)9798331532109;9798331532093

In recent years, the development of software-defined networking (SDN) and network function virtualization (NFV) has provided greater flexibility and programmability for network services. As a novel network architecture, Service Function Chaining (SFC) offers operators more possibilities in deploying and managing network service functions. However, current research often overlooks user Quality of Service (QoS) requirements, focusing primarily on operator interests. This paper addresses this gap by proposing a novel approach that integrates multidimensional QoS parameters and combines Simulated Annealing (SA) and particle swarm optimization (PSO) algorithms to optimize QoS service management in SDN/NFV environments. Specifically, we devise a hybrid optimization strategy based on SA and PSO to achieve efficient resource allocation and service chain scheduling, aiming to enhance the overall QoS experience for users. Experimental results demonstrate significant improvements in network service delivery efficiency and user satisfaction, validating the effectiveness and practicality of the proposed approach.

关键词： Quality of Service Software-defined Networking Network Function Virtualization Service Function Chaining Simulated Annealing algorithm particle swarm optimization algorithm

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Credit Value Based Autonomous Vehicles Joint Task Offloading optimization

Credit Value Based Autonomous Vehicles Joint Task Offloading...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Yang, Yongyi Li, Taoshen Ge, Zhihui Nanning Univ Sch Informat Engn Naning Peoples R China Guangxi Univ Sch Comp Elect & Informat Naning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

To address the issue of selfish behavior demonstrated by autonomous vehicles in vehicle-infrastructure cooperation, where vehicles having spare computing resources are disinclined to share them, we propose a vehicle selection-assisted computing scheme based on a credit value incentive mechanism. This scheme is aimed at resolving the reluctance of autonomous vehicles to supply their spare computing resources while also facilitating low-performance vehicles to participate effectively in task computing. Additionally, considering the optimization problem of delay and energy consumption during offloading and computing tasks for autonomous vehicles, we formulate a mathematical problem that integrates weighted sums of delay and energy consumption. To handle this problem, we propose a double populations adaptive particle swarm optimization algorithm (DPAPSO) based on the traditional particle swarm optimization algorithm (PSO). The DPAPSO forms two populations of particles in accordance with our model and utilizes different updating strategies for each population. Experimental results show that the proposed DPAPSO outperforms the traditional particle swarm optimization algorithm in terms of optimization effects within this model. Furthermore, it presents superior performance and stability compared to both the simulated annealing algorithm (SA) and the genetic algorithm (GA).

关键词： Vehicle-infrastructure cooperation Autonomous vehicles Task offloading particle swarm optimization algorithm

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Dynamics modeling and placement optimization of multi-robot system for the transportation of flexible structures 19

Dynamics modeling and placement optimization of multi-robot ...

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IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Yinbo Jia, Shiyuan Chen, Gang Wang, Yifan Beijing Univ Posts & Telecommun Sch Automat Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350360875;9798350360868

Space robots have been considered as vehicles for the assembly of large spacecraft. Multi-robots offer the advantage of transporting the modular structure to the target position. This paper investigates the dynamics of a multi-robot connected to a flexible structure rigidly by manipulators, along with the optimization of end-effector placement on the flexible structure. The assumed mode method is employed to describe the vibration of the flexible structure, while the dynamics are modeled using the Kane method. A comprehensive performance index is proposed based on the model, incorporating energy consumption and vibration amplitude. The placement of end-effectors is optimized using the particle swarm optimization (PSO) algorithm. Various cases involving two transportation directions and two magnitudes of vibration amplitude metrics are investigated. Numerical simulations are conducted to demonstrate the feasibility of the optimized placement.

关键词： Dynamics modeling Placement optimization Space multi-robots Flexible structure particle swarm optimization algorithm

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EV Charging Scheduling for Flattening Load Profile Based on Improved particle swarm 36

EV Charging Scheduling for Flattening Load Profile Based on ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Zhipeng Li, Xiaohui Mao, Shanli Cai, Bin He, Jie Zhu, Jialu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Netizen Technol Co Ltd Res Ctr Wuhan Peoples R China Shenglong Elect Grp Co Ltd Res Ctr Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The load profile can reflect the change of the grid load over time. By connecting electric vehicles to the grid for charging and discharging, the goal of peak shaving and valley filling is realized, thus flattening the load profile and improving the stability of grid operation. Aiming at this characteristic, a method for optimal scheduling of electric vehicle charging based on an improved particle swarm algorithm is proposed in this paper. First, the proposed method constructs the electric vehicle charging scenario by Monte Carlo. Then, the improved particle swarm algorithm is used to solve the optimal charging and discharging quantity of electric vehicles in each period to minimize the load variance. By comparing the improved particle swarm algorithm with other intelligent algorithms on the benchmark function test set, the improved particle swarm algorithm is verified that it has superiority in convergence ability and optimization accuracy. Simulation results show that the scheduling of electric vehicle charging using the improved particle swarm algorithm reduces the peak valley difference of the power grid by 6.31% and the load variance by 74432kW(2) compared with the classical particle swarm algorithm.

关键词： load profile electric vehicles Monte Carlo particle swarm optimization algorithm load variance

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Trajectory optimization algorithm Based on Time-Optimal Cooperative Six-Axis Robot 8

Trajectory Optimization Algorithm Based on Time-Optimal Coop...

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8th International Conference on Robotics, Control and Automation (ICRCA)

作者： He, Chunlai Liu, Yunfei Liu, Jinglong Wang, Weijun Sun, Wei Liu, Shujian Cheng, Shuo China Elect Technol Robot Co Ltd Robot Engn Ctr Shanghai Peoples R China

ISBN: (纸本)9798350344738;9798350344721

The trajectory optimization problem of cooperative six-axis robot based on time optimization is to optimize the trajectory of the robot under the premise of satisfying the kinematic constraints, dynamic constraints and task constraints of the robot, so that the time of movement in the working process is the shortest. After completing the kinematics solution of the six-axis cooperative robot, based on the 5-order B-spline interpolation trajectory, the particle swarm optimization algorithm is used to optimize the time interval between the trajectory nodes, and the particle swarm optimization algorithm is used to solve the robot trajectory with the lowest energy consumption. Finally, a suitable experiment is designed to prove the feasibility of the optimization scheme.

关键词： quintic B-spline time optimal trajectory planning particle swarm optimization algorithm robot obstacle avoidance

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Modified Pi Sigma artificial neural networks for forecasting

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GRANULAR COMPUTING 2023年第1期8卷 131-135页

作者： Egrioglu, Erol Bas, Eren Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkey

Pi Sigma artificial neural networks are a type of high-order neural network used in time series forecasting problems. In the Pi Sigma artificial neural networks, the weights between the hidden layer and the output layer are taken as constant and one, and the biases as constant and zero. Although this feature of the Pi Sigma artificial neural networks enables it to work with fewer parameters, it can also be seen as an obstacle to obtaining better forecasting performance. In this study, unlike classical Pi Sigma artificial neural networks, a modified Pi Sigma artificial neural network is proposed by taking the weights and biases as variables between the hidden layer and the output layer of the network. Thus, direct processing of the information coming to the output layer is prevented and the information coming to the output layer is weighted using different weights and bias values. The process of optimizing all the weights and bias values between the input and hidden layer, the hidden layer, and the output layer of the network is carried out together with the particle swarm optimization method. The proposed modified Pi Sigma artificial neural networks are compared with some other artificial neural networks in the literature by analyzing much well-known time series. As a result of the applications, it is seen that the forecasting performance of the modified Pi Sigma artificial neural networks is better than both the classical Pi Sigma artificial neural networks and many other artificial neural networks.

关键词： Pi Sigma artificial neural networks particle swarm optimization algorithm Forecasting

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Research on Power Grid Data Stability Model under Distributed Generation System Access 7

Research on Power Grid Data Stability Model under Distribute...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Wang, Yating Lei, Hui Wen, Jinmei State Grid Gansu Elect Power Co Digital Business Div Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9798350377040;9798350377033

The study studied the optimal load balancing control method after the distributed power source was connected to the grid to improve the stability of the network operation. Starting from the perspective of the power market, this project studied the mechanism of the effect of multiple power access on the stability of the power network. Factors affecting the stability of the power grid operation were proposed. The particle swarm algorithm was used to achieve load balancing control. It was proposed to achieve power balance at each node in the network and reduce the degree of fluctuation of the system. A power network stability analysis method that takes into account the characteristics of distributed power sources was established and simulated. Through experimental verification, the research results of this project can effectively improve the stability of the power network under the condition of distributed power access and reduce the safety hazards of the power system. The research results of the project will lay a theoretical and technical support for the coordinated development of new energy and large power grids, and promote the construction of smart grids in China.

关键词： Distributed generation system power grid data stability model load power distribution control strategy particle swarm optimization algorithm model simulation

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A Route Self-Repair Method for Wireless Sensor Networks 24

A Route Self-Repair Method for Wireless Sensor Networks

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International Conference on Computer and Multimedia Technology (ICCMT)

作者： Guo, Chen Su, Mohan Cui, Fang China Mobile Grp Device Co Ltd Beijing 100053 Peoples R China

ISBN: (纸本)9798400718267

With the evolution of AI technology, several emerging applications (such as computational offload and image recognition) place higher demands on the quantity and quality of data transmitted by wireless sensor networks (WSNs). Existing routing algorithms typically choose the transmission path with fewer hops and shorter distance. For energy-constrained WSN, this will lead to rapid energy loss and loss of several nodes, which will shorten the network life cycle. In this paper, a WSN routing method based on the ant colony optimization algorithm and the particle swarm optimization algorithm is proposed to deal with the above-mentioned problems. By establishing the multidimensional pheromone model of node distance, hop number and energy, the nodes can dynamically adjust the routing according to the residual energy in the data transmission process, so as to ensure the balanced decrease of node energy and prolong the lifetime of the WSN network. In order to improve the convergence rate of ant colony algorithm and reduce the energy consumption caused by reconstruction, particle swarm optimization is applied;the fitness function of particle swarm optimization algorithm is improved to ensure that the path can quickly find the optimal solution of the path when multiple self-repairs or repair failures occur. The simulation shows that this method can improve the network lifetime by 33% by ensuring high transmission effectiveness.

关键词： Wireless sensor network Ant colony algorithm particle swarm optimization algorithm Network routing

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM 36

Prediction of gas utilization ratio based on singular spectr...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Zhuang An, Jianqi Guo, Yunpeng Zhao, Guoyu Wu, Min China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Future Technol Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Gas utilization rate particle swarm optimization algorithm extreme learning machine singular spectrum analysis prediction model

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