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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121-130 订阅

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Third International conference on High Performance Computing and Communication Engineering, HPCCE 2023

Third International Conference on High Performance Computing...

引用

3rd International conference on High Performance Computing and Communication Engineering, HPCCE 2023

ISBN: (纸本)9781510674646

The proceedings contain 62 papers. The topics discussed include: multi-level supervised vision language model based steel surface defect detection;EGCR: an enhanced graph neural network for cache replacement;K-Means aided spatio-temporal CNN-LSTM network for air quality forecasting;malware detection method based on TPE optimized stacking;oil well acoustic dynamic liquid level detection based on EEMD;research on the optimization of local lane segmentation in BEV based on dynamic serpentine convolution;SEC-rdMA: a scheme to enhance security for rdMA one-sided operations;research on the correlation of building mechanical and electrical installation material names based on HMM-nf;fault tree generation and analysis based on extended SysML model;and implementation and application analysis of DNP3 security authentication.

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A Novel Energy optimization Management Strategy of Interconnected Power Grid Based on Source Grid and Load Interaction 3

A Novel Energy Optimization Management Strategy of Interconn...

引用

3rd International conference on New Energy and Power Engineering, ICNEPE 2023

作者： Wang, Tong Chu, Tianfeng Yu, Tongwei Cui, Jia Li, Jiafu Zhu, Chunxiang Tian, Aixin Electric Power Research Institute of State Grid Liaoning Electric Power Co. Liaoning Province Shenyang China Shenyang University of Technology School of Electrical Engineering Liaoning Province Shenyang China

ISBN: (纸本)9798350393224

With the deepening of the interactive studies on the source network and load of the power grid, the adjustable resources of the power grid are increasing. The interconnected power grid system forms by the interconnection and mutual supply of each power grid in a certain region. It has become the object of attention of scholars at home and abroad. Firstly, the energy optimization management method of the interconnected power grid is proposed by using the hierarchical interconnection structure in this paper. Secondly, taking transferable load as the representative of demand side management, an energy optimization management method of interconnected power network is proposed for the joint dispatching of interconnected power network and distribution network based on source network and load interaction. Finally, numerical examples show that the proposed algorithm outperforms existing algorithms in source grid and load interaction. © 2023 IEEE.

关键词： demand side management energy optimization management genetic algorithm interconnected power grid

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Research on Automatic control and Regulation System of Central Air Conditioning Based on Computer Automatic control Technology 3

Research on Automatic Control and Regulation System of Centr...

引用

3rd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Wang, Zekun Taiyuan University of Technology Building Environment and Energy Application Engineering Shanxi Province Taiyuan China

ISBN: (纸本)9781665472784

Based on the computer automatic control technology, this paper designs the energy efficiency optimization method for the traditional water-cooled central air conditioning host system. The chilled water control based on load prediction fuzzy control and the cooling water control based on adaptive fuzzy algorithm are used in the system, and the cooling water supply of the chiller is matched with the end load demand, and the cooling water system is running at maximum efficiency. Then on the basis of the single point fault tolerance design and the key points of energy-saving automatic control design, the design effect of HVAC system in data room is analyzed. Compared with PID control and single neural network reinforcement learning control, the total energy consumption of the system is reduced by 5.36% and 1.64%, and the total proportion of non-comfort time is reduced by 2.32% and 1.37%, respectively. © 2023 IEEE.

关键词： Cooling water

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Research and Application of Intelligent Flexible Switch control Ability Considering Economic Operation of Distribution network 3

Research and Application of Intelligent Flexible Switch Cont...

引用

3rd International conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Wang, Lin Hong, Ya Sun, Liangzhi Zhang, Le State Grid Liaocheng Power Supply Company Shandong Province Liaocheng252000 China

ISBN: (纸本)9798350318180

With the extensive access of electric power equipment to the electricity net controlling system, the control strategy of the distribution network with intelligent flexible switches has become a technical problem, especially to solve the optimal power flow of the distribution network. Taking the minimum voltage deviation and minimum network loss as objective functions, this paper establishes a multi-objective optimization control model of a distribution network with intelligent flexible switches and takes the active and reactive power of each port of intelligent flexible switches as control variables. Using IEEE33 nodes as an example, this paper studies the influence of different port numbers on the control ability of intelligent flexible switches and conducts a comparison and verification of four schemes. The experimental data show that compared with the dual-port intelligent flexible switch with the same number of converters, the multi-port intelligent flexible switch can realize the connection between more branches and feeders with the same number of converters and reduce operating loss. © 2023 IEEE.

关键词： economic operation of distribution network intelligent flexible switch control optimize control

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616978

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, Cloud Computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay;Model Migration and Joint Communication and Computing Resource Allocation in Native AI Wireless networks;FTB-RRP control for 2-D Continuous-Discrete Systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf optimization Algorithm;A Bat-BP optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural networks;research and Application of Computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection control;state Estimation and control of Switched Neural networks with Mode-Dependent Hybrid Dwell Time;Distributed Consensus control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural network;a Tiered Native Digital Twin for Telco networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation control in Multi-agent Systems with Disturbance Rejection Using RISE controller;model Predictive control for Contour control in Biaxial System.

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819624676

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616930

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Research on Steady-state Reactive Power optimization Considering Synchronous Condensers for Wind Power Centralized Systems 3

Research on Steady-state Reactive Power Optimization Conside...

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3rd International conference on Electrical Engineering and control Science, IC2ECS 2023

作者： Zhang, Haiyan Pan, Xueping Guo, Jinpeng Xu, Yi College of Energy and Electrical Engineering Hohai University Nanjing China

ISBN: (纸本)9798350382426

In order to improve the voltage stability problem in wind power centralized systems, a steady-state reactive power optimization scheme based on Artificial Fish Swarm Algorithm (AFSA) is proposed. The improvement is achieved through the distribution of reactive power support among multiple reactive power compensation equipment such as wind turbines, Synchronous Condensers and Static Var Generators. Several objectives are satisfied such as the good controllability of the voltage of the grid-connected point, the improvement of the voltage safety margin of the whole system and the reduction of network loss of the wind farm. The proposed reactive power optimization and voltage control strategy provides an efficient way to improve the economy of the whole wind farm operation and ensures the safety and stability of the power grid. © 2023 IEEE.

关键词： AFSA reactive power compensation voltage stability wind power centralized systems

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Deep Dual-Branch network: Design and optimization of High-Accuracy Sky Segmentation 3

Deep Dual-Branch Network: Design and Optimization of High-Ac...

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3rd International conference on Computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Fu, Xiangrong Shu, Yang Tian, Liya Wang, Binzi Yang, Wanyu Ding, Youdong Shanghai Film Academy Shanghai University Shanghai China

ISBN: (纸本)9798350372052

This paper presents an innovative network based on deep learning for sky segmentation, a key task in image processing. Sky segmentation has various applications in computer vision, but traditional methods struggle to meet accuracy and efficiency requirements due to the diverse and complex nature of the sky in different scenes. To address this challenge, we propose a semantic-detail dual-branch sky segmentation network that draws inspiration from the visual characteristics and segmentation needs of the sky. The network aims to enhance the accuracy and detail sensitivity of the sky area through the parallel design of the semantic branch and the detail branch, along with the introduction of a feature fusion module. We train the network using the AED20K subset of datasets, which enables fine segmentation of sky regions. Through extensive experiments on multiple datasets, we demonstrate that our network achieves significant performance improvements in sky segmentation tasks, delivering excellent accuracy and outstanding sensitivity to detail. © 2023 IEEE.

关键词： Semantics

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural network (V-AdCapNet) and Multi-instance Attention network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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