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Mobile resources big data deduplication and offloading algorithm based on new computing network architecture 3

Mobile resources big data deduplication and offloading algor...

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3rd International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Xiong, Qi Computer and Information Engineering Guangzhou Huali College Guangzhou511325 China

ISBN: (纸本)9781510661301

The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture. © 2023 SPIE.

关键词： network architecture

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optimization of network graph for minimal H2 consensus error

Optimization of network graph for minimal H2 consensus error

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3rd International conference on control Theory and Applications (ICoCTA 2023)

作者： Y. Ding H. Peng T. Qi J. Chen Department of Electrical Engineering City University of Hong Kong Hong Kong People's Republic of China School of Automation Guangdong University of Technology Guangzhou People's Republic of China School of Automation Science and Engineering South China University of Technology Guangzhou People's Republic of China

This paper concerns the optimization problems of consensus performance over network graphs. We assume the firstorder unstable agents interconnected by an undirected graph, and coordinated by a state feedback protocol where agents are subject to inter-agent delays. In particular, the minimization of H 2 consensus performance has been investigated, which amounts to optimizing the H 2 norm of the transfer function. By incorporating key graph constraints, we show how the network may be optimized by distributing the graph Laplacian eigenvalues, so as to improve the consensus performance.

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Prediction and optimization of Power Grid Cross Section Exceeding Limits based on Generative Adversarial networks 3

Prediction and Optimization of Power Grid Cross Section Exce...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Li, Bao Su, Yinsheng Zhou, Xin Zhao, Ligang Zhen, Hongyue Xu, Yuan Power Dispatch and Control Center China Southern Power Grid Guangdong Guangzhou China Power Dispatch and Control Center China Southern Power Grid Guangdong Guangzhou China

ISBN: (纸本)9798331508456

Exceeding the cross-sectional power limit of the power grid poses significant risks to the safety and stability of power system operations. Traditional prediction methods based on models and historical data face challenges in accurately capturing the nonlinear characteristics of power grids under complex environments. This study proposes a novel method using Generative Adversarial networks (GANs) to predict and optimize cross-sectional power violations. GANs are utilized to train a generator network to learn the distribution of power grid operation data and simulate realistic scenarios, combined with a discriminator network in an adversarial framework to enhance prediction accuracy. Additionally, a power optimization scheduling strategy is developed to minimize the probability of exceeding limits using the prediction results. Experimental findings reveal that the GAN model significantly outperforms ARIMA and ANN models in cost optimization, achieving an average reduction of 1919 yuan/month and a maximum of 1984 yuan/month. These results demonstrate the superior performance of GAN models in predicting and optimizing power grid operations. © 2025 IEEE.

关键词： Discriminators

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Uplink Capacity optimization for High Throughput Satellites using SDN and Multi-Orbital Dual Connectivity

Uplink Capacity Optimization for High Throughput Satellites ...

引用

IEEE International conference on Communications (ICC)

作者： Dazhi, Michael N. Al-Hraishawi, Hayder Shankar, Mysore R. Bhavani Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg

ISBN: (纸本)9781665426718

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications, it is not been widely explored in satellite systems. In this paper, Dual Connectivity is implemented in a multi-orbital satellite network, where a network model is developed by employing the diversity gains from Dual Connectivity and Carrier Aggregation for the enhancement of satellite uplink capacity. An introduction of software defined network controller is performed at the network layer coupled with a carefully designed hybrid resource allocation algorithm which is implemented strategically. The algorithm performs optimum dynamic flow control and traffic steering by considering the availability of resources and the channel propagation information of the orbital links to arrive at a resource allocation pattern suitable in enhancing uplink system performance. Simulation results are shown to evaluate the achievable gains in throughput and latency;in addition we provide useful insight in the design of multi-orbital satellite networks with implementable scheduler design.

关键词： Satellites Heuristic algorithms conferences System performance Software algorithms Throughput Dynamic scheduling

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

Network Transformation of Telecontrol Device based on Comput...

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Big Data, Information and Computer network (BDICN), International conference on

作者： Qiangxing Liu Jin Wang Changzuo Geng Nari Technology Co. Ltd. Nanjing Jiangsu China Nari Group Corporation(State Grid Electric Power Research Institute) Nanjing Jiangsu China

ISBN: (数字)9798350363821

ISBN: (纸本)9798350374612

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.

关键词： Cloud computing Power system stability Big Data network security Computer networks Stability analysis Power industry

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Research on simulation accuracy of equivalent aggregate electromagnetic model optimization for large renewable energy stations 3

Research on simulation accuracy of equivalent aggregate elec...

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2024 3rd International conference on Electrical, control and Information Technology, ECITech 2024

作者： Wu, Xiaorong Cao, Bin Xing, Yue Shen, Zhuoxuan Power China Sichuan Electric Power Engineering Co. Ltd Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China Chengdu Mite Intelligent Power Technology Co. LTD Chengdu China

The traditional power grid is rapidly transforming into a new power system with high penetration of new energy. Simulation modeling plays an important role in supporting the cognition and research of the new power system. A high proportion of renewable energy is connected to the grid through power electronic components, which puts forward a higher demand for research on the accuracy and stability of power grid simulation modeling. Renewable energy plants usually have a large number of single units, and equivalent aggregation modeling is the usual way to simplify the modeling of renewable energy plants. However, the transmission line power multiplier equivalent module inevitably introduces series impedance into the network, resulting in errors between simulation and actual engineering. Therefore, the study of lossless multiplier elements based on Modified Nodal Analysis (MNA) can improve the simulation accuracy, and it is verified with the simulation results of detailed models in actual scenarios, proving that MNA lossless multiplier elements can optimize the accuracy and stability of equivalent aggregation modeling of large-scale multi-unit renewable energy stations. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Electric power transmission

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Research on Deployment Scheme of Aggregation control Gateway for Hybrid network of Virtual Power Plant 3

Research on Deployment Scheme of Aggregation Control Gateway...

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3rd International conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)

作者： Bao, Xingchuan Liu, Shidong Liu, Chuan Bu, Xiande State Grid Smart Grid Inst Co Ltd Nanjing 210003 Jiangsu Peoples R China State Grid Lab Elect Power Commun Network Technol Nanjing 210003 Peoples R China

ISBN: (纸本)9781665454551

Acquisition Sensing Infrastructure (ASI) is the main technology of Virtual Power Plant. It can send commands to Smart Meters to establish two-way communication. The meters data is aggregated at the Aggregation control Gateway (ACG) and then send to the Virtual Power Plant center server. In complex and changeable distribution network environment,a data aggregation deployment scheme based on wired - wireless hybrid network is proposed to reduce the cost of ACG placement. This scheme presents the optimization problem of finding the optimal location of the aggregation nodes and divide the placement nodes into available nodes and potential nodes. This kind of hybrid network facilitates communication between the user and the microgrid in its own interior, reducing the latency of energy services and the overhead of data aggregation. Besides, the improved K-means algorithm is proposed to decrease the algorithm complexity. The data results show that the virtual power plant network infrastructure and ACG deployment model based on wired/wireless hybrid network can reduce the cost and the density of ACG.

关键词： Virtual Power Plant Smart Grid Hybrid networking Aggregation control Gateway(ACG) K-means Algorithm

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Transmission Characteristics Analysis and optimization of Coherent Structured Light Fields in Turbulent Atmosphere Based on Deep Learning 3

Transmission Characteristics Analysis and Optimization of Co...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Hu, Xuehua Jiangxi University of Engineering Jiangxi Xinyu China

ISBN: (纸本)9798331508456

In order to address the attenuation, distortion and distortion problems of light field transmission in a turbulent atmosphere, a coherent structured light field analysis method based on deep learning is introduced, aiming to improve the anti-disturbance ability of the light field in atmospheric turbulence and optimize its transmission characteristics. By establishing an optimization framework combining a turbulent atmosphere model and DNN (Deep Neural network), the transmission performance of coherent structured light fields under different turbulence intensities is studied. Firstly, a numerical model of turbulent atmosphere is established, and the transmission process of coherent structured light field under turbulence of different intensities is obtained through Monte Carlo simulation. Secondly, DNN is used to correct the distortion of the light field. The network is trained to enable it to recognize and optimize the spatial structure and phase information of the light field to reduce the impact of turbulence on the light field. The network model is trained using a large-scale dataset to ensure that it can adaptively adjust the characteristics of the light field under different turbulence conditions. Finally, the modified light field is controlled by real-time feedback combined with the optimization algorithm to maximize the transmission efficiency and recovery accuracy, thereby achieving optimized transmission of the light field in a turbulent environment. Under strong turbulence conditions, the MSE (Mean Square Error) without feedback control is the highest, which is 0.20-0.22, and the SSIM (Structural Similarity) is the lowest, which is only 0.68-0.70, and the transmission efficiency is 0.63-0.65;after real-time feedback control, the MSE drops to 0.15-0.16, the SSIM increases to 0.76-0.78, and the transmission efficiency improves to 0.74-0.75. The coherent structured light field optimization method based on deep learning provides an effective solution for light

关键词： Feedback control

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Research on Active and Reactive Power Coordination optimization of Active Distribution network Based on Shared Energy Storage

Research on Active and Reactive Power Coordination Optimizat...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Qiyou Lin Jie Wang Pengcheng Wang Tingting Chang State Grid Wuhu Fanchang District Electric Power Supply Branch Wuhu China School of Electrical Engineering Anhui Polytechnic University Wuhu China

Aiming at the coordinated optimization of active and reactive power in Active Distribution network (ADN), this study establishes a coordinated optimization model based on shared energy storage in ADN. The improved NSGA-II algorithm and forward-backward method are integrated for solving, and comparative experiments are conducted with predefined scenarios, analyzed using the Hypervolume metric. Simulation results validate the effectiveness of the proposed strategy and method. Shared energy storage and optimization algorithms not only optimize network losses, improve voltage levels, and enhance power quality but also further optimize electricity costs, providing new insights for the optimization of ADN.

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Trajectory Tracking control of Manipulator Based on Particle Swarm optimization Fuzzy Neural network 3

Trajectory Tracking Control of Manipulator Based on Particle...

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3rd International conference on Smart Power and Internet Energy Systems (SPIES) - Towards A Sustainable and Affordable Energy Transition

作者： Gang, Mingyi Xia, Xingguo Pan, Xiaobo Ning, Pinghua Maanshan Tech Coll Dept Elect Engn Maanshan Peoples R China

ISBN: (纸本)9780738146331

The manipulator system is a multi-input and multi-output system with highly coupling and nonlinear dynamics characteristics, and the system structure and parameters have many unpredictable factors in practical work. A fuzzy neural network model controller is proposed, and the parameters of the controller are optimized by particle swarm optimization algorithm. The simulation results show that the control strategy has strong adaptability, stability and anti-interference performance to the control system, and effectively solves the trajectory tracking problem of the manipulator.

关键词： manipulator Fuzzy neural network Particle swarm optimization Trajectory tracking

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