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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1643 条记录，以下是1-10 订阅

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network Transformation of Telecontrol Device based on Computer network and optimization of Acceptance Technology 3

Network Transformation of Telecontrol Device based on Comput...

引用

3rd International conference on Big Data, Information and Computer network, BDICN 2024

作者： Liu, Qiangxing Wang, Jin Geng, Changzuo Nari Technology Co. Ltd. Nanjing Jiangsu211106 China Nanjing Jiangsu211106 China

ISBN: (纸本)9798350363821

In order to understand the network transformation and acceptance technology optimization of remote control devices, a research on network transformation and acceptance technology optimization of remote control devices based on computer networks has been proposed. This article first focuses on the network transformation and technological optimization of the power system. With the rapid development of computer technology, many industries in society have undergone changes relying on computer technology. In the power system, the application of computer network remote control technology has achieved the automation development of the power system, improved the operational efficiency of power enterprises, and effectively promoted the sustainable and optimized development of the power industry. © 2024 IEEE.

关键词： Power systems computer aided design

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optimization of Transmission network Structure Based on Deep Neural networks 3

Optimization of Transmission Network Structure Based on Deep...

引用

3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhang, Weiqi Li, Lixin Yu, Yijun China Elect Power Res Inst Power Automat Dept Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Real-time transmission line switching control is a cost-effective and efficient measure to alleviate line overload. However, due to the nonlinear characteristics of the transmission network structure optimization problem, conventional optimization methods often struggle to find solutions quickly. In recent years, data-driven methods have been widely applied in fields such as power system dispatch optimization. This paper proposes a method based on deep neural networks. By using historical and simulation data of the power system, the neural network is trained to perform exploratory learning and find optimal topology control strategies when line overload occurs. This method is trained and tested on the IEEE 14-bus open-source dataset, and the test results verify its effectiveness.

关键词： deep learning data-driven deep neural networks topology optimization line overload mitigation

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Research on the optimization Strategy of Command and control System Based on Complex network Theory 24

Research on the Optimization Strategy of Command and Control...

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3rd International conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Li, Tengda Wang, Gang Du, Chong Fu, Qiang Liu, Xiangyu Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China

ISBN: (纸本)9798400718212

To deeply analyze the characterization and destruction resistance of the command and control (C2) system, based on the complex network theory, a model of the C2 system is established. The network model of the C2 system is attacked using the method of real-time attack, and it is found that the network is more sensitive to the deliberate attack method and performs poorly. The node-degree based edge-adding method is proposed to enhance the destructibility of the network, and it is found that the policy of low-degree edge-adding method can effectively increase the destructibility of the C2 network system to a certain extent.

关键词： Complex network theory Edge addition strategy C2 system network efficiency

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Trajectory Planning of Industrial Robot Based on Improved Radial Basis Function Neural network 3

Trajectory Planning of Industrial Robot Based on Improved Ra...

引用

3rd International conference on Robotics, Artificial Intelligence and Intelligent control (RAIIC)

作者： Li, Xiong Du, Yuyuan Qu, Dongxu Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350352634;9798350352627

With the more and more extensive use of robot technology in industrial production, the trajectory planning requirements of industrial robots are constantly increasing. In order to solve the problem that the trajectory is difficult to fit due to the nonlinearity in the continuous trajectory planning of robot, this paper presents a trajectory planning method of industrial robots which combines RBF (radial basis function) neural network and PSO (particle swarm optimization) algorithm. When RBF neural network has fewer hidden layer nodes, PSO algorithm is used to optimize the connection weight and threshold of RBF neural network. Through simulation analysis, the RBF neural network before and after improvement is compared. The results show that compared with the trajectory planning algorithm before improvement, the PSO-RBF neural network has smaller error and stronger adaptability, which meets the expected requirements of industrial robot trajectory planning.

关键词： industrial robot trajectory planning RBF neural network PSO algorithm

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A Frequency control Method for Distributed Energy Storage Cluster control 3

A Frequency Control Method for Distributed Energy Storage Cl...

引用

3rd Asian conference on Frontiers of Power and Energy

作者： Li, Ying Liang, Hongfu Gu, Bo Li, Ke Zhang, Jiexin State Grid Chongqing Shinan Elect Power Supply Br Dept Mkt Chongqing Peoples R China

ISBN: (纸本)9798350378467;9798350367676

A frequency control method for distributed energy storage cluster control is proposed to address the issue of poor frequency regulation performance in the power system due to the widespread integration of new energy sources. Firstly, build a system frequency control architecture for distributed energy storage clusters, and unify the regulation of energy storage clusters through aggregation control centers. Then, design an enhanced learning algorithm controller and use the BP neural network optimized by the improved particle swarm optimization algorithm to find the optimal energy storage power increment for frequency control. Finally, use the control output of the controller to achieve system frequency control. Based on the IEEE 39 node system, experimental analysis was conducted on the proposed method, and the results showed that the system frequency did not experience overshoot, and the energy storage output power did not exceed the limit of 75MW, significantly improving the frequency control capability of the new power system.

关键词： distributed energy storage cluster frequency control enhanced learning algorithms BP neural network improve particle swarm optimization algorithm new power system

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The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and control optimization in IoT Environments 3

The Fusion of Deep Reinforcement Learning and Edge Computing...

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3rd International conference on Energy and Power Engineering, control Engineering (EPECE)

作者： Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang No Arizona Univ 1900 S Knoles Dr Flagstaff AZ 86011 USA Univ Elect Sci & Technol China Chengdu Peoples R China Trine Univ Phoenix AZ USA Yantai Univ Tokyo Japan Northwestern Univ Atlanta GA USA

ISBN: (纸本)9798350366105;9798350366099

In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control.

关键词： Deep reinforcement learning Edge computing Industrial Internet of Things Lightweight policy network Dynamic resource allocation

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Research on Frequency control and optimization of Power Grid Auxiliary Load for Diversified Vehicle network Interaction 3

Research on Frequency Control and Optimization of Power Grid...

引用

3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Dong, Jinxi Qin, Jianzhong Ling, Long Lin, Xiaoming Chen, Peilu Meng, Feng Guangxi Power Grid Co Ltd Liuzhou Power Supply Bur Liuzhou Peoples R China CSG Elect Power Res Inst Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the transformation of global energy structure and the rapid development of intelligent transportation system, the interaction between power grid and vehicle network has become a research hotspot. Especially the popularization of electric vehicles (EVs) and the progress of smart grid technology play a key role in maintaining the stable operation of power grid, optimizing energy distribution and improving the reliability of power system. The purpose of this study is to deeply discuss the frequency control and optimization of auxiliary load in power grid under the background of diversified vehicle-network interaction, and put forward effective control strategies through analysis and research. The research methods include establishing a fusion model of power grid and vehicle network, proposing an auxiliary load control strategy based on vehicle battery and fleet scheduling, and using MATLAB/Simulink for simulation analysis. Through simulation analysis, the effectiveness of the proposed strategy is verified, including adjusting the charging and discharging behavior of EV to stabilize the power grid frequency, optimizing the dispatching plan of bus fleet to improve the power grid frequency stability and so on. It is of great theoretical and practical significance to study the frequency control and optimization of auxiliary load in power grid for diversified vehicle-network interaction. The strategies and methods put forward in this study not only help to improve the stability and efficiency of power grid, but also help to promote the further development of intelligent transportation system and contribute to the realization of energy saving, emission reduction and sustainable development goals.

关键词： Vehicle network Interaction Power Grid Auxiliary Load Frequency control and optimization

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Research and Practice of Algorithm Platform in Logistics Distribution network optimization 3

Research and Practice of Algorithm Platform in Logistics Dis...

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Yu, He Changchun College of Electronic Technology Changchun130000 China

ISBN: (纸本)9798331519032

Logistics industry as the main distribution mode of online shopping, then the efficiency of logistics distribution will become a very important content for consumers, and will also become the optimization direction of logistics distribution network. Combined with many previous studies, this paper believes that the design of distribution route will become the main way to improve distribution efficiency, and studies the algorithm platform and its potential, and proposes to use an improved ant colony algorithm to design a more efficient distribution route, so as to realize the optimization of logistics distribution network. In addition, this paper also verified the method through a comparative experiment. The two sides of the comparison experiment were the distribution route based on the method in this paper as the experimental group and the conventional distribution route as the control group. In the comparison of delivery time, the average delivery time of the experimental group was 9.69h, and the average delivery time of the control group was 14.01h. The average delivery time of the experimental group based on the proposed method is nearly 5h less than that of the control group, which fully indicates that the proposed method can improve the efficiency of logistics distribution and achieve optimization. © 2024 IEEE.

关键词： optimization algorithms

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Research on the Application of Object Detection Algorithm Based on Neural network in the Regulation and control of Intelligent Building Energy-saving Equipment 24

Research on the Application of Object Detection Algorithm Ba...

引用

3rd International conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Wang, Xinjun Chengdu Polytech Sch Urban Construct Chengdu Sichuan Peoples R China

ISBN: (纸本)9798400709777

To improve the regulation effect of intelligent building energy-saving equipment, an optimization regulation method for air conditioning system based on improved YOLOv5 personnel detection is proposed. Firstly, CBAM attention mechanism and EIOU loss function are introduced to improve YOLOv5 network. Then, based on personnel detection results of the improved YOLOv5, control optimization for air conditioning system is carried out from two aspects: variable refrigerant volume system and fresh air system. The experimental results show that the average precision and F1 score of the improved YOLOv5 algorithm for indoor personnel detection reach 98.62% and 0.95 respectively, which are significantly improved compared with Fast R-CNN and the original YOLOv5 algorithm. The improved YOLOv5 algorithm has a high reliability when applied to calculate personnel occupancy rate and personnel uniformity. The simulation results show that compared to the total power consumption under traditional control strategy, the energy-saving rates reach 64.39% and 8.13%, respectively, which indicates that the proposed method has certain practical value and is worth further research and promotion.

关键词： Object detection Air conditioning system Occupancy rate Regulation optimization

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optimization of Multi-Path Transmission control Protocol Model in 5G network Environment 3

Optimization of Multi-Path Transmission Control Protocol Mod...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bai, Qiaohua Beijing Information Technology College Beijing100070 China

ISBN: (纸本)9798331509675

Communication optimization algorithms in the complex and changing environments are an important research topic in the information field. optimization of the multi-path transmission control protocol model in 5G network environment is selected as the topic of this study. The original intention of this study is to solve the problem that data packets in traditional networks are only transmitted in a single network protocol. From a holistic perspective, this study designs a dynamic path selection optimization framework based on a greedy strategy and roulette algorithm. At the same time, it combines the improved Fuzzing test case generation technology to optimize test efficiency. At the same time, this study proposed a cross-protocol multi-path transmission mechanism. It also designed a store-and-forward solution for the weak connection and narrow bandwidth environments. Such a design can optimize data transmission performance. In complex 5G scenarios, this study further combined the mLoRa technology to solve problem of conflicting data packets through a frequency estimation algorithm. The experiment is carried out in real time with the assistance of Mininet network simulator and BMv2 programmable switch. The experimental results show that the optimized model is significantly better than the traditional method in terms of bandwidth utilization, transmission delay, data packet loss rate and other indicators which show the higher robustness. © 2025 IEEE.

关键词： 5G mobile communication systems

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