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2023 China Automation Congress, CAC 2023

作者： Shi-Jun, Tang Yun-Feng, Hu Zhong-Bo, Sun Chong, Zhang Yu-Zhe, Fei Xing-Tian, Xiao Jilin University College of Communication Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China Changchun University of Technology Intelligent Rehabilitation Engineering Technology Laboratory Department of Control Engineering China

ISBN: (纸本)9798350303759

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. To make OMRA accomplish repetitive motion, obstacle avoidance, and trajectory tracking tasks in complex dynamic environments with the presence of obstacles and external disturbances, a new noise-suppressed repetitive motion obstacle avoidance (NSRMOA) scheme is proposed. As established previously many OMRA motion planning assumes that the simulation is noise free. However, noise interference may prevent OMRA from accomplishing repetitive motion, obstacle avoidance, and trajectory tracking tasks, which may lead to task failure. The proposed the NSRMOA scheme with intrinsic noise suppression can solve external noise interference, so that the OMRA can accomplish the repetitive motion, obstacle avoidance and trajectory tracking tasks in the presence of external noise interference. Finally, the optimal solution of the NSRMOA scheme can be effectively obtained by using recurrent neural network (RNN). The effectiveness and accuracy of the scheme are verified by examples and comparisons. © 2023 IEEE.

关键词： Robotic arms

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Prediction of Heat Load of Thermal Station Based on Neural Network 24

Prediction of Heat Load of Thermal Station Based on Neural N...

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6th International Conference on control and Computer Vision, ICCCV 2024

作者： Zhu, Xinying Geng, Liqing Yang, Genghuang Huo, Yang Xu, Shiyuan Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin University of Technology and Education China Co. Ltd China

ISBN: (纸本)9798400718045

To solve the problem of improving the prediction accuracy of thermal station, multi-strategy fusion Sparrow Search Algorithm to optimize the Neural Network prediction method. First, the acquired data is preprocessed to ensure the continuity of the data;second, the Pearson Correlation Coefficient method is used to determine the input feature variables and construct the data set through the data;and finally, Sparrow Search Algorithm and multi-strategy fusion Sparrow Search Algorithm respectively optimizes Back Propagation Neural Network and Long Short-Term Memory Neural Network models for heat supply prediction. After example verification and comparison, the multi-strategy fusion Sparrow Search Algorithm optimizes the Back Propagation Neural Network model with the minimum prediction error, which effectively improves the prediction accuracy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Estimation of Plant Growth Environment Temperature based on Plant Electrical Signals 7

Estimation of Plant Growth Environment Temperature based on ...

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7th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2024

作者： Wang, Yifan Tian, Liguo Li, Meng College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin300222 China

ISBN: (纸本)9798350370782

Electrical signals are an important type of signals in plants, which can not only regulate normal physiological activities, but also reflect the current environmental temperature of plants to a certain extent. The plant electrical signal was collected and the Peak value, Peak to peak and Mean value were extracted as eigenvalues, and the BP neural network was trained to obtain the correspondence model between the eigenvalues of the signal and the ambient temperature. The mean square deviation (MSE) and correlation coefficient (R) of the trained BP neural network model were 0.0058 and 0.9408, respectively. In the re-collected data, 90% of the estimated values fall between 01, and the judgment effect is good, which proves that the model has a high accuracy of judgment. In the future, plant electrical signals can be combined with neural network models to detect temperature, humidity and other parameters in plant growth states, which has high application value in smart agriculture. © 2024 IEEE.

关键词： BP neural network model judgment plant electrical signals relational model smart agriculture Temperature detection

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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Implementation and Application of Solvers for Simulation and Execution of control Algorithms in NCSLab 6

Implementation and Application of Solvers for Simulation and...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Su, Jiuzheng Lei, Zhongcheng Hu, Wenshan Liu, Guo-Ping Wuhan University Department of Artificial Intelligence and Automation Wuhan China Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen China

ISBN: (纸本)9798350353594

In order to solve the problems of high dependency and low flexibility caused by calling three-party services during simulation and experiments, this paper uses the NCSLab (Networked control System laboratory) as an application platform to implement the functions of multiple ordinary differential equation solvers, which are used for real-time simulation and experimentation of control algorithm. This paper mainly integrates the common ordinary differential equation solving methods and NCSLab platform, and verifies the support of NCSLab platform for different solving methods through the actual simulation process. © 2024 IEEE.

关键词： Networked control systems

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion 7

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Zheng, Chen Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance. © 2024 IEEE.

关键词： Convolution

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Task Allocation and Path Planning for Multiple Destinations Oriented Towards Task Updates 6

Task Allocation and Path Planning for Multiple Destinations ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Yang, Yong He, Dingxin Mao, Huantao Chen, Hong Wu, Hongtao Liu, Zhi-Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350356618

This study addresses the complexities of orchestrating multi-target transportation tasks within multi-agent systems, constrained by load capacity. The primary objective is to engineer an advanced path planning framework that assures collision avoidance and optimal load distribution. Diverging from conventional single-path strategies, this research incorporates load factors and requires continuous dynamic multi-target point and path planning to ensure agents can efficiently navigate through a series of predetermined key points. To address this complex requirement, a two-stage multi-agent task allocation and path planning method is proposed. First, an initial solution is obtained using a suboptimal algorithm, followed by optimization iterations using the large neighborhood search algorithm to improve task allocation. In the second stage, an accelerated algorithm based on priority search is used to plan optimal paths for each agent in a predetermined order. The proposed algorithm's effectiveness is comprehensively evaluated through a series of experimental evaluations and comparisons with the commercial solver Gurobi within a limited time. The results show that the proposed method achieves optimality in both running time and minimum path cost while ensuring the load balance of agents. © 2024 IEEE.

关键词： Motion planning

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Integrated design and performance optimization of three-electrode sliding discharge plasma power supply

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Plasma Science and technology 2024年第11期26卷 152-161页

作者： Borui ZHENG Linwu WANG Jianbo ZHANG Shaojie QI Yuhong CHEN Haodong LIU Dongliang BIAN School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048People’s Republic of China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038People’s Republic of China

The three-electrode sliding dielectric barrier discharge(TES-DBD) plasma actuator significantly enhances the ionization rate and momentum exchange between charged particles and neutral particles by incorporating a parallel DC electrode into the standard DBD design. This design improves the body force and induced jet velocity while allowing flexible control of the induced jet angle, overcoming the limitations of discharge extension and uncontrollable direction in traditional DBD plasma actuators. An integrated plasma power supply has been designed specifically for TES-DBD plasma actuators, streamlining the power supply management. The methodology involves designing the circuit topology for the TES-DBD power supply, followed by simulating and validating its operating principles using Multisim software. The operational performance of the power supply is evaluated through a comprehensive analysis of its electrical,thermal, and aerodynamic properties specific to TES-DBD plasma actuation.

关键词： plasma flow control dielectric barrier discharge three-electrode sliding discharge plasma power supply

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A Novel GNN-Based Node Importance Ranking Method in a Heterogeneous Network 7th

A Novel GNN-Based Node Importance Ranking Method in a Hete...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, Shuyu Ji, Dedong Wang, Jingwei Xia, Weiguo Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Shenyang Aircraft Design and Research Institute Shenyang China

ISBN: (纸本)9789819733231

In a heterogeneous network, a reliable importance ranking of nodes can provide a useful reference for decision-making. Nevertheless, existing node importance ranking methods may not effectively integrate the characteristics of the edges and nodes in the graph, as well as the structural information. In this paper, a novel graph neural network-based heterogeneous network node importance ranking (GNN-HNNIR) method is proposed to rank the importance of different functional nodes in a heterogeneous network. GNN-HNNIR can adjust the node importance score based on those of neighboring nodes, which employ a multi-head attention mechanism to aggregate scores by pooling information from edges, nodes, and the graph structure. Finally, numerical experiments show that GNN-HNNIR can effectively evaluate the importance scores of different nodes with given small amount of data for training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heterogeneous networks

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Design technology of Multi-AGV Warehouse intelligent Scheduling Optimization control System 15th

Design Technology of Multi-AGV Warehouse Intelligent Schedul...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Pan, YanPeng Zhang, XueTing Zhang, FuQuan Tan, LiLi Yang, LiJie Ma, Jun China Academy of Launch Vehicle Technology Beijing100076 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819700677

Different from the traditional warehousing system, in the multi-AGV warehouse scheduling control system, the constraints of poor connectivity of the conveying track have led to the emergence of urgent problems such as vehicle conflicts in the task, low efficiency of multitasking concurrency, difficulty of delineating the conditions for entering the transporting area and so on. This paper proposes a design of multi-AGV warehouse intelligent scheduling optimized control system, which takes multitask scheduling research as an entry point, designs the warehouse layout and vehicle operation rules assumptions and modeling, studies the scheduling solution of Monorail Vehicles in multiple tasks, designs a scheduling optimization method based on the algorithm of Time Window, solves conflicts between key process and operation task process and optimize the scheduling, so as to effectively enhance the efficient scheduling capability of multi-AGV warehouse intelligent scheduling optimized control system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Warehouses

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