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3rd International conference on Communication, control, and Intelligent Systems, CCIS 2024

ISBN: (纸本)9798331528201

The proceedings contain 175 papers. The topics discussed include: an optimized inverse neural network control augmented with feedback PI controller for time-varying systems;image encryption using neural network based chaotic systems;investigating the impact of update frequency on PID controller performance in autonomous robotics;deep learning framework for constellation signal classification in underwater optical wireless communication systems;fuzzy based web automation for smart power supply controller;ensembled machine learning models for antenna optimization in wireless communication/ biomedical applications;vertically integrated substrate integrated cavity based filtering antenna;network intrusion detection system using autoencoders;and a comparative study of Wallace tree multiplier and binary multiplier performance.

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High-level optimization of industrial big data of DCC device based on neural network 3

High-level optimization of industrial big data of DCC device...

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3rd Asia-Pacific conference on Communications Technology and Computer Science (ACCTCS)

作者： Lu, Qi Tianjin Univ Technol Tianjin Peoples R China

ISBN: (纸本)9798350310801

The use of industrial big data high-level optimization to diagnose and analyze DCC devices has been applied in the petrochemical production process, promoting the revolutionary breakthrough of production process control. Combined with the company's idea of enhancing its core competitiveness through information technology, it then elaborates the existing data modeling methods, builds a software model, and tries to optimize its process big data and models through neural network algorithms to improve the potential of process control, so as to achieve the purpose of reducing costs and increasing efficiency. Finally, the characteristics of highlevel optimization of industrial big data and the problems and challenges that may be faced in the process of realizing this are discussed, in order to achieve a breakthrough in the company's overall business value.

关键词： big data neural networks process industries High-order optimization

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An Aeromagnetic Compensation Algorithm Based on Broad Learning System 3

An Aeromagnetic Compensation Algorithm Based on Broad Learni...

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

作者： Wang, Qilong Zuo, Chao Jin, Fang Song, Junlei Dong, Kaifeng China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Hubei Key Lab Adv Control & Intelligent Automat C Minist EducSch Automat Wuhan Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430064 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Aeromagnetic surveys, renowned for their operational flexibility and high efficiency, serve as a crucial technique for measuring the geomagnetic field. However, aeromagnetic surveys are easily affected by magnetic interference from navigation platforms, making the compensation of aeromagnetic interference a crucial step in the measurement process. To address the inadequate consideration of nonlinear magnetic field interference in traditional compensation algorithms, this paper introduces an aeromagnetic compensation approach based on broad learning system. The broad learning system employs an incremental learning mechanism aimed at enhancing the precision of the network alongside the increase in nodes. With each expansion of the network node, computation is streamlined to calculating the pseudo-inverse of the expansion node, eliminating the necessity for retraining the entire network structure. Leveraging the nonlinear fitting characteristics of the broad learning system, this paper improves the accuracy of aeromagnetic interference compensation. Through UAV flight experiments, the broad learning system is compared with methodologies using particle swarm optimization (PSO) and BP neural network. Compared with PSO, training time was reduced by 21.3% and magnetic interference by 33.6%. Compared with BP neural networks, training time was reduced by 34.9% and magnetic interference by 28.6%. This paper provides references and ideas for the selection of aeromagnetic interference compensation algorithms.

关键词： aeromagnetic compensation broad learning system particle swarm optimization incremental learning

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

Proceedings - 2024 3rd International Conference on Data Anal...

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

ISBN: (纸本)9798331519032

The proceedings contain 193 papers. The topics discussed include: visual analysis of artificial intelligence in short video research based on CiteSpace;optimization design of financial shared services based on improved algorithms and artificial intelligence;research on the application of artificial intelligence technology in microelectronics testing and fault diagnosis;artificial intelligence method for v2g oriented power distribution network control strategy and capacity analysis;research on indoor lighting optimization control system based on artificial intelligence algorithm;a model for evaluating the effectiveness of news dissemination under the combination of big data and artificial intelligence;and mechanical and electronic engineering design and control technology under artificial intelligence.

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Cross-Corridor Viable network optimization Based on DE Algorithm 3

Cross-Corridor Viable Network Optimization Based on DE Algor...

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3rd Asia-Pacific conference on Communications Technology and Computer Science (ACCTCS)

作者： Lu, Jilong Gan, Xusheng Wu, Yarong Li, Yongxin Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China Xijing Univ Sch Optoelect & Informat Xian 710123 Shaanxi Peoples R China

ISBN: (纸本)9798350310801

Scientific and reasonable planning and design of cross-corridor network can greatly improve the rapid response capability and support capability of strategic transport in battlefield airspace. For viable network optimization problem of the cross-corridor considering the restricted airspace, firstly, the network optimization problem of the cross-corridor is transformed into the non-interference path point layout optimization problem, and the non-interference path point layout model is established. Then, according to the characteristics of the non-interference path point layout problem, the individual evaluation criteria and individual restoration operators are designed to facilitate the differential evolution algorithm for solving the optimization model. The simulation validates the effectiveness and feasibility of the proposed method in the application of the cross-corridor optimization design.

关键词： Cross-corridor Differential Evolution Algorithm network optimization Fitness Function Battlefield Airspace

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Research on Optimal Power Flow Algorithm of Reactive Voltage in Distribution network Based on Improved PSO 3

Research on Optimal Power Flow Algorithm of Reactive Voltage...

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3rd Asia Power and Electrical Technology conference, APET 2024

作者： Li, Kuanhong Cao, Siyang Xiao, Zhongbo Li, Dehuai Wang, Yicen State Grid Taining Electric Power Company Ltd. Taining China State Grid Sanming Electric Power Company Ltd. Sanming China

ISBN: (纸本)9798350367690

Aiming at the supply voltage quality problem of distribution network under the background of large-scale access of distributed clean energy, this paper presents a reactive voltage optimization method considering the risk of reactive voltage overlimit. According to the situation of the distribution network, the constraint model of the comprehensive optimization of the distribution network is extracted. With the minimum active power loss and voltage deviation rate of the distribution network as the optimization objective, the improved particle swarm optimization algorithm is used to establish the reactive power and voltage optimization model, and the optimal control strategy scheme of reactive power resources is generated. Finally, simulation experiments are carried out on the IEEE33 system using the proposed method. The experimental analysis results prove the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Power distribution networks

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Power control Based on Deep Q Learning in Heterogeneous networks 3

Power Control Based on Deep Q Learning in Heterogeneous Netw...

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3rd International Joint conference on Information and Communication Engineering (JCICE)

作者： Zhao, Yingqi Jin, Jin Yang, Tingting Zhengzhou Univ Zhengzhou Peoples R China

ISBN: (纸本)9798350388732;9798350388725

Heterogeneous network is a promising solution to meet higher network capacity requirements and faster transmission rates. Under the spectrum-sharing strategy, the interference problem among user devices is a major factor affecting the network's performance. Power control methods employing deep reinforcement learning are extensively utilized for interference management purposes. A previous study formulated a multi-agent power control algorithm utilizing deep reinforcement learning to tackle the sum-rate optimization problem within a fixed base station deployment scenario in heterogeneous networks. Specifically, the aforementioned research exclusively examined a specific scenario wherein the femto base stations are situated within the central coverage area of the macro base station. Inspired by the research, this paper considers the variation of femto base stations' location and explores the impact of base station distribution on the sum-rate using deep Q learning algorithm. Simulation results show that the average sum-rate enhances with the increase of the sum distance between the macro base station and the femto base stations.

关键词： deep Q learning heterogeneous networks power control

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Research on Autonomous Robots Navigation based on Reinforcement Learning 3

Research on Autonomous Robots Navigation based on Reinforcem...

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

作者： Wang, Zixiang Yan, Hao Wang, Yining Xu, Zhengjia Wang, Zhuoyue Wu, Zhizhong Syracuse Univ Coll Engn & Comp Sci Syracuse NY 13244 USA Bentley Univ Human Factors Informat Design Waltham MA 02452 USA Cranfield Univ SATM Cranfield Beds England Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)9798350352634;9798350352627

Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become one of the key methods to achieve autonomous navigation of robots. In this work, an autonomous robot navigation method based on reinforcement learning is introduced. We use the Deep Q network (DQN) and Proximal Policy optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback. By combining the Q-value function with the deep neural network, deep Q network can handle high-dimensional state space, so as to realize path planning in complex environments. Proximal policy optimization is a strategy gradient-based method, which enables robots to explore and utilize environmental information more efficiently by optimizing policy functions. These methods not only improve the robot's navigation ability in the unknown environment, but also enhance its adaptive and self-learning capabilities. Through multiple training and simulation experiments, we have verified the effectiveness and robustness of these models in various complex scenarios.

关键词： Autonomous robots navigation Reinforcement learning Deep Q network Proximal policy optimization

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Distributed data-driven distributionally robust Volt/Var control for distribution network via an accelerated alternating optimization procedure

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ENERGY REPORTS 2023年 9卷 532-539页

作者： Li, Peishuai Wu, Zaijun Yin, Minghui Shen, Jiawei Qin, Yingming Nanjing Univ Sci & Technol 200 Xiaolingwei St Nanjing 210094 Peoples R China Southeast Univ 2 Sipailou Nanjing 210000 Peoples R China

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov's accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Distribution network Volt Var control Distributed optimization Data-driven distributionally robust optimization Accelerated alternating optimization procedure

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Analysis of hierarchical optimization control technology of distribution network with mobile energy storage 3

Analysis of hierarchical optimization control technology of ...

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2024 3rd International conference on Smart Grid and Green Energy, ICSGGE 2024

作者： Lv, Jiaolong Gai, Di Liu, Chuanbo Bai, Yuanlong Zhang, Mengyu Han, Yishu Ma, Shizhe Ming, Xin Li, Jialin Xiao, Ying State Grid Liaoning Electric Power Co. Ltd. Fushun Power Supply Company Liaoning Fushun113000 China

Mobile energy storage has many application scenarios and high scheduling flexibility. The article introduces the layered optimization control technology of distribution networks to ensure the safety of distribution network operation after mobile energy storage access. The article analyzes the logic principle of distribution network hierarchical control technology, constructs a distribution network hierarchical optimization control model, and introduces the solution algorithm of the optimization model. The case study findings indicate that the examined layered control strategy for distribution networks can appropriately manage the charge and discharge states of mobile energy storage. This enhances the utility of mobile energy storage and the operational dependability of the distribution network. © Published under licence by IOP Publishing Ltd.

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