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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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A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet 13

A License Plate Recognition Method Based on YOLOv8-Pose and ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Ji, Honghai Shi, Qirui Fan, Lingling Wang, Li Xiong, Shuangshuang School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

This paper proposes a license plate recognition method based on YOLOv8-Pose and E-LPRNet for complex scenarios such as urban roadside and road inspection. E-LPRNet is a character recognition network formed by modifying the activation function of the backbone network in LPRNet (License Plate Recognition Network). This method also includes a license plate correction feature, which accurately recognizes license plate characters even when the plates are at large angles, thereby increasing the recognition accuracy. A series of experiments using a large license plate dataset verified the effectiveness of this method. © 2024 IEEE.

关键词： Character recognition

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Identification and Prediction of Microvascular Invasion in Hepatocellular Carcinoma Based on Multiple Sequence Fusion Images 43

Identification and Prediction of Microvascular Invasion in H...

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43rd Chinese control Conference, CCC 2024

作者： Lu, Mingguang Zhu, Yuan China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

Hepatocellular carcinoma (HCC) is one of the most difficult diseases to be treated fundamentally in the medical field in today's world, and the recurrence rate of HCC has been increasing year by year in recent years. The study on the identification and prediction of microvascular invasion phenomenon (MVI) in hepatocellular carcinoma is beneficial to the postoperative prevention of hepatocellular carcinoma patients, which can greatly improve the survival probability of patients after surgery. In this study, a new MVI prediction method (MSF-GWO-GPR) was proposed, which combines the multi-sequence image features of imaging histology, sifts out the unimportant features through the T-test method and the dimensionality reduction of Lasso regression, and establishes the multi-sequence Gaussian process regression (GPR) prediction model for data fusion, and combined with the Gray Wolf Optimization (GWO) algorithm to design a suitable optimization objective function to optimize the hyper-parameters in the GPR, in order to get better prediction results. Compared with the existing methods, the data from different sequences of MRI images are selected for fusion in this study, which makes the feature data richer and more complete, and the model applicability is better;moreover, by optimizing the hyperparameters in the model through the GWO, the model can be made more accurate, reduce the risk of misidentified prediction, and improve the accuracy rate. The experimental results show that MSF-GWO-GPR outperforms widely used machine learning methods in terms of accuracy, F1 score, precision, recall and AUC metrics. This may provide a new idea for the identification and prediction of MVI in hepatocellular carcinoma. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Dimensionality reduction

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Research on Geospatial Object Recognition Algorithm in Power Transmission and Transformation engineering Based on FIseg-YOLO 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ouyang, Peng Pi, Yufei Zhang, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

The preliminary work of power transmission and transformation engineering design tasks relies on the precise survey of terrain to ensure the high-quality completion of design tasks. Therefore, developing a technology that can accurately and efficiently survey the construction area has become a hot topic in the field of electrical design research. However, identifying the location and precise boundaries of geospatial targets of different scales and densities from high-resolution natural environment images captured by drones remains a challenge. To address this issue, we improved upon the YOLOv8 algorithm by incorporating the concept of the YOLACT algorithm, proposing a novel instance segmentation algorithm—FIseg-YOLO, specifically designed for recognizing geospatial targets in power transmission and transformation line projects. This algorithm integrates various attention mechanism modules in the backbone network, effectively improving the accuracy of target recognition against complex backgrounds. Simultaneously, we optimized the neck network structure by enhancing the multi-path fusion of the network, achieving multi-level feature fusion between different layers. Additionally, the algorithm adds a multi-scale detection head to the basic network structure to extract shallow information from the input image and combine it with the feature fusion network to detect objects across a broader scale. Experimental validation on the geospatial orthophoto dataset shows that the mAP@.5 of the FIseg-YOLO model reached 67.3%, a 6.6% increase compared to the YOLOv8 base model, indicating a significant improvement in model performance. This improvement is of great significance for enhancing the efficiency of preliminary survey tasks in power transmission and transformation engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Image Recognition of Chinese Herbal Medicine Using Adaptive Gamma Correction Based on Convolutional Neural Network 13

Image Recognition of Chinese Herbal Medicine Using Adaptive ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Ji, Honghai Liu, Xinjing Wang, Li Fan, Lingling Liu, Shida School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

Chinese herbal medicine (CHM) has gained significant attention due to its potential in treating various diseases. CHM image classification is crucial in fields such as smart medical care and medical teaching. However, accurately classifying CHM images has been a challenging task due to their fine-grained variability. To achieve automatic identification and classification of Chinese herbal medicines, we designed a method that combines image processing and deep learning. We established a dataset of CHM images, which includes 10 kinds of common Chinese herbal medicines, with 15,719 images in total. The dataset covers medicinal materials in various natural backgrounds and provides ample samples for training deep learning models. Regarding image processing, we proposed a method based on adaptive gamma correction(AGC) and compared it with CLAHE(Contrast Limited Adaptive Histogram Equalization)and traditional gamma correction(TGC) methods. Subsequently, we employed three pre-training models of convolutional neural networks based on transfer learning, namely GoogLeNet, ResNet-18, and MobileNetV2, to identify CHM images. The results of the verification indicate that the AGC-CNN model outperforms the CLAHE-CNN and TGC-CNN models in terms of classification accuracy and image quality improvement. © 2024 IEEE.

关键词： Contrastive Learning

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Linear active disturbance rejection control of brushless dc motor based on improved gray wolf optimization algorithm 3

Linear active disturbance rejection control of brushless dc ...

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3rd International Conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Luo, Peiyue Zhang, Hongxin Tian, Liguo Xie, Ziyang Sun, Miao School 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)9781510683105

In this paper, an improved gray wolf optimization linear active disturbance rejection control (IGWO-LADRC) had been proposed by improving the grey wolf population initialization, convergence factor, and grey wolf position updating strategy of the traditional grey wolf optimization algorithm. The problems of standard gray wolf optimization algorithm in linear active disturbance rejection control parameters tuning, such as low efficiency of optimization search and easy fall into local optimal solution, had been solved. A brushless DC motor control system based on IGWO-LADRC and a brushless DC motor control system based on gray wolf optimization linear active disturbance rejection control (GWO-LADRC) had been built. The comparison had shown that IGWO-LADRC had a fast optimization-seeking speed, high control accuracy, quick response speed, and strong anti-interference ability. © 2024 SPIE.

关键词： Disturbance rejection

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Optimal Strategy for Aircraft Pursuit-evasion Games via Self-play Iteration

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Machine Intelligence Research 2024年第3期21卷 585-596页

作者： Xin Wang Qing-Lai Wei Tao Li Jie Zhang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

In this paper,the pursuit-evasion game with state and control constraints is solved to achieve the Nash equilibrium of both the pursuer and the evader with an iterative self-play *** the condition where the Hamiltonian formed by means of Pontryagin’s maximum principle has the unique solution,it can be proven that the iterative control law converges to the Nash equilibrium ***,the strong nonlinearity of the ordinary differential equations formulated by Pontryagin’s maximum principle makes the control policy difficult to figured *** the system dynamics employed in this manuscript contains a high dimensional state vector with *** practical applications,such as the control of aircraft,the provided overload is ***,in this paper,we consider the optimal strategy of pursuit-evasion games with constant constraint on the control,while some state vectors are restricted by the function of the *** address the challenges,the optimal control problems are transformed into nonlinear programming problems through the direct collocation ***,two numerical cases of the aircraft pursuit-evasion scenario are given to demonstrate the effectiveness of the presented method to obtain the optimal control of both the pursuer and the evader.

关键词： Differential games pursuit-evasion games nonlinear control optimal control Nash equilibrium solution

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Research on operation optimization of rural hybrid renewable energy systems based on Nash bargaining 8

Research on operation optimization of rural hybrid renewable...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhuang, Xin Gao, Qiang Xu, Zhao Chen, Jiaqi Zhang, Zhengyang Liu, Junjie College of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Complex System Control Theory and Applications Tianjin300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The Rural Hybrid Renewable Energy System (RHRES) holds great significance as a prospective energy system development. Ensuring its economic viability and low-carbon operation is crucial in order to fully harness its potential. Consequently, this research paper presents a multi-party cooperation operation model for RHRES, based on the Nash bargaining theory. To effectively differentiate between the various parties involved in carbon trading and guarantee the system's low-carbon operation, a ladder-type multi-party carbon trading model is formulated. The proposed RHRES cooperation operation model, grounded in Nash bargaining, is approached in two steps: determining the transaction volume between parties and resolving the transaction price through the concept of equivalent transformation. To address concerns regarding party privacy, the alternating direction multiplier method is employed for solution. The numerical findings demonstrate that the suggested cooperation operation model substantially reduces RHRES operating costs and carbon emissions, while enhancing the utilization of wind and solar power. Consequently, this model holds significant practical and promotional value. © 2024 SPIE.

关键词： Solar energy

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A Proposal Of An Iterative Learning control Method For Simulating A Flyback Converter Using PSCAD 9

A Proposal Of An Iterative Learning Control Method For Simul...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Cao, Trung Thanh Van, Cuong Trinh Van, Hung Pham Nguyen, Minh Quang Hoang, Anh Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Ha Noi University of Industry Department of Automation and Control Hanoi Viet Nam

ISBN: (纸本)9798331505042

DC-DC converters are essential components in power supply applications, portable electronics, renewable energy systems, and various other industries. An accurate system model is crucial for the development of the controller for a DC-DC converter. This research study focused on the development of an iterative learning control that operates without requiring the system model. The study employs PSCAD simulation software to simulate and analyze the effectiveness of an iterative learning control and a sliding mode controller for flyback converter. Detailed simulation results and comparisons between the two methodologies will be presented. © 2024 IEEE.

关键词： Sliding mode control

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Comparative study of several PID parameter tuning methods 10

Comparative study of several PID parameter tuning methods

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10th International Conference on Mechanical engineering, Materials, and automation Technology, MMEAT 2024

作者： Zhang, Sai Hu, Shaolin Ke, Ye Chen, Zhihan School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132000 China Automation School Guangdong University of Petrochemical Technology Guangdong Maoming52500 China

ISBN: (纸本)9781510682634

PID (Proportional Integral Derivative) control, as a classical control method, is widely used in petrochemical, metal smelting, food production and other industrial fields due to its superior control performance. With the continuous improvement of industrial production process, the process control environment is becoming more and more complex, and the controlled object often has complex characteristics such as multivariate coupling, large time lag, nonlinearity, multifactor interference, etc. Therefore, the research of PID parameter tuning method has been paid more and more attention, and it has become an important research topic in the field of automatic control. This paper reviews and analyzes Z-N empirical formula method, Critical Ratio method, Cohen-Coon method, ISTE method, fuzzy algorithm and adaptive particle swarm optimization (APSO) algorithm several PID parameter tuning methods, and applies the MATLAB software to analyze the effect of different tuning methods on the performance of the system through the simulation of Simulink comparison experiments. In this paper, the second-order system is used as the control object, and the experimental results show that the APSO PID parameter self-tuning is better than the other methods in the experiments of response performance analysis, tracking performance analysis, and robustness analysis in terms of rise time, overshooting amount, stabilization time and robustness, all of them are better than the other methods. © 2024 SPIE.

关键词： MATLAB

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