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6th IEEE Advanced Information Management, Communicates, Electronic and automation control Conference, IMCEC 2024

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

ISBN: (纸本)9798350316520

Plant electrical signal is a kind of non-smooth and non-linear weak signal, and it is a low-frequency random signal. The plant growth state analyzer of the plant electrical signal is to receive the data from the plant electrical signal acquisition board first, and then preprocess the electrical signal data collected from the acquisition board to finally obtain the plant electrical signal with high signal-to-noise ratio, extract the maximum value, minimum value, peak value, average value, area, rising speed, falling speed as the feature value, and then get the deep learning model through the training of WOA-LSTM neural network to obtain the collected plant electrical signal and a relationship with the influences on the plant growth state. model to get the collected plant electrical signals and a relationship with the influence of plant growth state. The experimentally trained WOA-LSTM neural network model has a mean square error MSE and correlation coefficient R of 0.0232 and 0.9408, respectively. which is a good judgment, proving that the model has a high judgment accuracy. Finally, the model was deployed to the development board and the plant growth status was displayed on the feedback interface using QT. The plant growth state analyzer speculates the current growth state of the plant through the change of the electrical signal of the plant in order to adjust the environment of the plant in time. Through the electrical signal to determine whether the plant grows in a good direction or a bad direction to control it, it helps to promote the development of agriculture in a more accurate, efficient and sustainable direction. © 2024 IEEE.

关键词： Neural network models

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Substation Insulator Inspection Robot System Based on Deep Learning 7

Substation Insulator Inspection Robot System Based on Deep L...

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7th IEEE Information Technology, Networking, Electronic and automation control Conference, ITNEC 2024

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

ISBN: (纸本)9798350370782

Substation plays an important role in power system As the key electrical isolation component in substation, the state of insulator is directly related to the safe operation of power grid In order to improve the inspection efficiency and safety of substation insulators, this paper presents a substation insulator inspection robot system based on ResNet18 model in order to improve the inspection efficiency and safety of substation insulators The system consists of visual inspection module, navigation module and main control system The visual inspection module adopts Hi3516 visual identification terminal to accurately inspect insulators;The navigation module uses lidar and Rising Sun X3 to collect map data and SLAM algorithm to realize accurate positioning and path planning;The main control system takes STM32 core board as the center and is responsible for motor control network communication and other tasks In addition, a man-machine interface is designed to display the inspection status and inspection results of the robot in real time Experiments show that the system has high accuracy and robustness in practical application, which provides technical support for automatic inspection of insulators in substations. © 2024 IEEE.

关键词： Motion planning

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Multiple Static Target Hunting Based on the RRT and OSPA Distance

Multiple Static Target Hunting Based on the RRT and OSPA Dis...

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2024 China automation Congress, CAC 2024

作者： Liu, Weifeng Ren, Shiwei Tang, Jiawei Su, Zijun School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi’an China School of Automation Hangzhou Dianzi University Hangzhou China University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9798350368604

This paper considers multiple static target hunting control and path planning using multi-agents. The whole hunting process can be classified into two stages. The first is cruising stage, where the agents move in a certain formation to approach the multiple static targets. Once the agents enter into certain ranges around the targets, the agents will switch into the second stage of hunting. In these two stages, it is required to consider several issues: the agent controlling, path planning, and task allocation. Specifically, the traditional multi-agent controlling algorithm, rapidly-exploring random tree (RRT), and optimal sub-pattern assignment (OSPA) are respectively used in the process. Simulation experiment shows that the path planning and hunting algorithm can efficiently realize the formation movement and multiple static targets hunting. © 2024 IEEE.

关键词： Multi agent systems

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Practically Predefined-Time Adaptive Fuzzy Tracking control for Nonlinear Stochastic Systems

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IEEE Transactions on Cybernetics 2023年第12期53卷 8000-8012页

作者： Zhang, Tianliang Su, Shun-Feng Wei, Wei Yeh, Ruey-Huei Qingdao University of Technology School of Information and Control Engineering Shandong Qingdao266520 China Nanjing University of Science and Technology School of Automation Jiangsu Nanjing210094 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan

This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems, where the system under consideration includes stochastic disturbances and uncertain parameters. First, in this study, practically predefined-time stochastic stabilization (PPSS) in the $p$ th moment sense is introduced, and a Lyapunov-type criterion for PPSS is proposed to assure the stabilization of the system considered. With these ideas, based on the backstepping design method, a semiglobally practically predefined-time adaptive fuzzy tracking control algorithm is proposed with a fuzzy system used to approximate the unknown part of the system. Moreover, the settling time of the system response can be arbitrarily adjusted in a mean-value sense, and such freedom can be used to improve the stochastic finite-/fixed-time control results. Finally, a practical example and a numerical example of a comparison are provided to validate the effectiveness of the proposed control strategy. © 2013 IEEE.

关键词： Stochastic systems

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Research on Optimization Method of Injection Molding Machine Temperature control System Based on IK-SS

Research on Optimization Method of Injection Molding Machine...

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2024 China automation Congress, CAC 2024

作者： Zou, Haipeng Ye, Quanxiang Li, Xiaoyu Kong, Xiangsong Zhao, Dan Shao, Zhijiang School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Zhangzhou Hongxingtai Electronics Co. Ltd China College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350368604

Injection molding, as a plastic processing technology, is widely used in the mass production of plastic products with high precision and automation. However, the performance optimization process of this system currently faces some prominent issues, such as time consumption, reliance on experience, and difficulty in achieving optimality. Traditional optimization methods typically rely on manual experience and rules, which are inefficient and time-consuming. To enhance the performance of the control system, this paper focuses on utilizing an improved simplex search method based on the data-driven optimization concept to optimize the parameters of the temperature control system of the injection molding machine. The method introduced in this paper is termed the Knowledge-Informed Simplex Search Method Based on Adjacent Historical Centroid Points Information (IK-SS). The IK-SS algorithm is a variant of the simplex search method that utilizes information from the centroids of neighboring simplices to evaluate the current state of the optimization process and dynamically adjust the iteration step size. This approach aims to improve optimization efficiency while reducing computational complexity and time costs. Simulation and experimental validation demonstrate that the IK-SS algorithm significantly enhances the temperature control performance of the injection molding machine (IMM). This demonstrates the potential of the IK-SS algorithm as a tool for optimizing the temperature control system of injection molding machines. © 2024 IEEE.

关键词： Molding

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A Detection Algorithm Based on SegFormer for Unmanned Driving

A Detection Algorithm Based on SegFormer for Unmanned Drivin...

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2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Liu, Ziqi Guo, Shenyan Xie, Ziteng School of Ocean Engineering Harbin Institute of Technology Weihai China School of Automation Science and Engineering South China University of Technology Guangzhou China College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350314670

Semantic segmentation matters in unmanned driving. The accuracy of semantic segmentation determines whether the vehicle can accurately identify the object on the road during driving. At present, the most popular semantic segmentation algorithm is deep learning network model with convolutional neural network as the main body, while SegFormer based on encoder and decoder proposed in recent years has not been introduced into the field of unmanned semantic segmentation. Aiming at this problem, this paper firstly divides the city road images into several small blocks. Secondly, the obtained block is sent to the encoder as input to get the multilayer feature maps. Finally, the feature maps are sent to the decoder predictive segmentation mask. On the Cityscape dataset, using SegFormer improved mIoU accuracy by an average of 6.564% compared to algorithms such as DeepLabV3+ and FPN, and SegFormer mIoU(ms+flip) similarly improved accuracy by an average of 6.072% in the case of data enhancement. It can be seen that SegFormer has unique advantages in the field of semantic segmentation, and will play an infinite potential in unmanned driving technology. © 2023 IEEE.

关键词： Semantic Segmentation

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Interleaved Flyback Photovoltaic Microinverters with PI and Repetitive control

Interleaved Flyback Photovoltaic Microinverters with PI and ...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Tian, Minwei Zeng, Hanchao Zheng, Xiangrong Zhang, Damin Zheng, Xueqin School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350380514

The grid-connected current output from a flyback photovoltaic microinverter is prone to distortion due to nonlinear factors such as switching tube dead zones, driving asym-metry and grid voltage disturbances. Aiming at the traditional PI control which cannot effectively improve the influence of nonlinear factors, this paper proposes an interleaved flyback photovoltaic microinverter based on PI and repetitive control, and in-depth research on the circuit topology, control strategy and the design method of the PI and repetitive controller of this inverter is carried out. Simulation results show that the Photo-voltaic micro-reverse grid-connected current with PI and repet-itive control is of high quality, with better dynamic performance and anti-interference capability, while realizing the maximum power point tracking. © 2024 IEEE.

关键词： Electric power transmission networks

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Detection of inter row navigation lines in corn fields based on improved CenterNet

Detection of inter row navigation lines in corn fields based...

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International Conference on Computer Vision, Robotics, and automation engineering, CRAE 2024

作者： Zhang, Zhipeng Li, Huiyan Meng, Qingkuan Zhang, Ruitong Zhang, Wenkang School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781510682283

In complex corn orchard environments, traditional machine vision algorithms often struggle to handle changes in light, occlusion between crops, and interference from weeds, which leads to inaccurate navigation line extraction. This article proposes a corn field navigation route detection method based on improved CenterNet to address this issue. Firstly, we replaced the CenterNet backbone network with ResNeSt, which can effectively improve detection performance when facing small targets and complex hierarchical image content. At the same time, we added CBMA attention mechanism in CenterNet to enhance the recognition of target features of corn seedlings and weaken background interference. We use the midpoint of corn seedlings as the navigation positioning base point, use an improved CenterNet model to detect corn seedlings on both sides, use the center point of corn seedlings as the positioning reference point, and then use the least squares method to fit the center points of corn seedlings on both sides. The experimental results show that the improved CenterNet model has a detection accuracy of 76.44% and a detection speed of 42.09 frames per second. Compared with the original CenterNet model, it has improved by 4.28 percentage points and 1.51 frames per second, respectively. From this, it can be seen that the model can accurately identify corn seedlings, and the average processing time for one frame of image is 0.082 seconds, which fully meets the needs of navigation in corn fields. © 2024 SPIE.

关键词： Seed

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Intelligent Robot for Crop Growth Detection 6

Intelligent Robot for Crop Growth Detection

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6th IEEE Advanced Information Management, Communicates, Electronic and automation control Conference, IMCEC 2024

作者： Li, Meng Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350316520

Tomato is an important vegetable species, also known as tomato. However, it is often jeopardized by invasive diseases, resulting in lower eating quality and yield impacts. The traditional method of detecting pests and diseases mainly relies on manual visual observation, which is more dependent on past experience, and the subjective factors have a greater impact, so the actual effect is not accurate and less efficient. In this paper, we explore a tomato pest and disease detection mobile robot for the detection of pest and disease conditions during tomato cultivation in greenhouse environment. The mobile visual inspection robot uses STM32 microprocessor as the core of the mobile control system, and the FreeRTOS operating system is transplanted to complete the robot's autonomous walking, position detection and other tasks;equipped with the NLE-AI800 artificial intelligence board at the same time using the YOLOV5 modeling framework for the target diagnosis as well as the board end deployment. © 2024 IEEE.

关键词： Greenhouses

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Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2014-2025页

作者： Xingtang Wu Mingkun Yang Wenbo Lian Min Zhou Hongwei Wang Hairong Dong School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Research Center of Railway Safety Assessment Beijing Jiaotong UniversityBeijing 100044China

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

关键词： Delay propagation double layer network high speed rail network max-plus algebra

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