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26th International Conference on Industrial Technology, ICIT 2025

作者： Tao, Zixing Cao, Weihua Gan, Chao Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9798331521950

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction. © 2025 IEEE.

关键词： Air quality

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Prediction of transmission spectrum from photonic energy band via autoencoder network

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IEEE Photonics Technology Letters 2025年第13期37卷 737-740页

作者： Mo, Wenqin Zhu, Yaling Yin, Yuanxing Dong, Kaifeng Jin, Fang Song, Junlei China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Optical topological insulators, as an emerging type of photonic material, present substantial benefits for optical communication. The advanced pattern recognition capabilities of deep learning have propelled the inverse design of topological photonic structures into a hot area of research. However, existing methods encounter difficulties with the analysis of intricate band structures. Therefore, we construct a deep learning model capable of handling two-dimensional data of photonic energy band. Utilizing an autoencoder network, our model indirectly trains the photonic energy band and transmission spectrum, by converting them into feature coding. It integrates a transmission spectrum autoencoder and a photonic energy band autoencoder, processing energy band data to predict the corresponding transmission spectrum. The results indicate that compared to convolutional neural network (CNN), our model can accurately predict transmission spectrum, and perform better on both evaluation metrics of RMSE and R2. This work enhances prediction accuracy by leveraging energy band diagrams to constrain optical properties. © 1989-2012 IEEE.

关键词： Topological insulators

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Vision-Based Adaptive Prescribed-Time control of UAV for Uncooperative Target Tracking with Performance Constraint

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Journal of systems Science & complexity 2024年第5期37卷 1956-1977页

作者： SHE Xuehua MA Hui REN Hongru LI Hongyi School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of TechnologyGuangzhou 510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou 510006China College of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing 400715China

This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle(UAV)under the performance constraint and scaled relative velocity constraint,in which the states of the uncooperative target can only be estimated through a vision *** the limited detection range,a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking ***,the scaled relative velocity constraint in the dynamic phase is taken into account,and a time-varying nonlinear transformation is used to solve the constraint problem,which not only overcomes the feasibility condition but also fails to violate the constraint ***,the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are *** is proved that the UAV can follow the uncooperative target at the desired relative position within a prescribed time,thereby improving the applicability of the vision-based tracking *** results have been presented to prove the validity of the proposed control strategy.

关键词： Nonlinear transformation performance constraint prescribed-time tracking uncooperative target vision-based measurement

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Tightly-coupled Lidar-GNSS-Inertial Fusion Odometry and Mapping

Tightly-coupled Lidar-GNSS-Inertial Fusion Odometry and Mapp...

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第43届中国控制会议

作者： Shuwei Yu Jing Li Tianwei Niu Junzheng Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper proposes a tightly-coupled lidar-GNSS-inertial fusion system that achieves accurate state estimation and mapping for robot *** system effectively fuses lidar points,GNSS measurements,and IMU data using the iterated error state Kalman filter algorithm to obtain precise,drift-free,and real-time *** optimize computational efficiency,an incremental tree structure,ikdtree,is employed for managing a local map,and different Kalman gain formulas are utilized to process GNSS and lidar point observations separately to lower the computation ***,a factor graph is introduced to optimize the pose *** selectively introducing keyframes based on the odometry estimation,the graph incorporates odometry,GNSS measurements,and loop closure constraints to optimize all keyframe poses,resulting in a precise trajectory and global ***,extensive experiments are conducted using the KITTI dataset and several real-world scenarios to validate the proposed *** experimental results demonstrate that our method achieves precise localization and mapping across diverse environments.

关键词： State estimation Mapping Sensor fusion Robot navigation

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Optimal Tracking Performance of Networked control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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LRDE:Long-Range Dependency and Salient Feature Enhancement for Co-SOD 41

LRDE:Long-Range Dependency and Salient Feature Enhancement f...

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第41届中国控制会议

作者： LongSheng Wei SiYuan Guo GuanYu Zong Jiu Huang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Different from other salient object detection(SOD) for the task of extracting salient obj ects in a image,collaborative saliency object detection(Co-SOD) targets a set of related image groups,and uses algorithms to find and locate the common collaborative salient objects in related image *** current SOD has achieved remarkable results,but most of the previous researches focus on how to obtain the consistency between images,while the contextual semantic information of images and the features of salient objects are less *** solve these problems,we propose a novel network to capture long-range dependencies on images and enhance(LRDE) the representation of salient ***,In LRDE,we capture the longrange dependencies by the Non-local module to obtain contextual semantic ***,in order to highlight the features of salient objects and enhance the representation of salient objects,we propose the Coordinate Attention module,which embeds the location-attention channel attention and encodes the feature maps as orientation-aware and position-aware attention maps,and It is applied complementary to the input feature map,thereby enhancing the representation of salient *** show that the method outperforms twelve improved methods on four publicly available datasets.

关键词： Co-SOD Long-Range Dependency Feature Enhancement Non-local Module Coordinate Attention Module

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Tea Picking Path Planning Based on Ant Colony Algorithm 41

Tea Picking Path Planning Based on Ant Colony Algorithm

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第41届中国控制会议

作者： Guangjun Wang Yi Zhao Zhi Wang Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to improve the efficiency of tea picking,a scheme combining ant colony algorithm and tea picking technology is proposed to plan the picking *** this scheme,k-means clustering algorithm is used to divide the tea picking regions with irregular *** ant colony algorithm is used to carry out path planning for tea in different picking areas,and the best picking effect is selected according to the simulation *** solve the problem of long searching time of ant colony algorithm,the condition of iteration termination is changed to adaptive value,and the optimal route after the last iteration is recorded as the initial value of the next *** simulation results show that the best effect is to divide two picking *** improved ant colony algorithm reduces the operation time and the planned picking path is shorter,which can enhance the real-time tea picking and improve the efficiency of tea picking.

关键词： Tea plucking Path planning Ant colony algorithm K-means clustering

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Position Attention Network for Object Tracking 41

Position Attention Network for Object Tracking

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第41届中国控制会议

作者： Mingzhong Du Longsheng Wei Shiyu Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking obj *** solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well,we propose a Siamese network-based tracker with position attention network(SiamPA).First,we use Siamese backbone network to extract template and search region ***,we adopt the boxguided object feature selection strategy to avoid similarity calculations for background *** addition,we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region ***,the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the *** contribution,one is to propose a box-guided method for refining object features,and the other is to introduce a position attention network for information *** on three challenging benchmarks including GOT-10 k,UAV123 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.

关键词： Object tracking Siamese network Box-guided Position attention network

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Tea Bud Recognition Based on Machine Learning 41

Tea Bud Recognition Based on Machine Learning

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第41届中国控制会议

作者： Guangjun Wang Zhi Wang Yi Zhao Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Due to the low efficiency and high cost of manual tea picking,as well as the development and application of machine vision and image recognition technology,the mechanization and intelligence of tea picking will become a *** of all,this paper analyzed the advantages and disadvantages of several common target detection methods by using Matlab ***,considering cost,accuracy and real-time performance,the method of combining K-means clustering and image morphology processing is finally selected to extract tea ***,the method is reproduced on STM32 single chip ***,the effect was verified in the actual tea garden,which laid a foundation for the subsequent intelligent picking.

关键词： Machine learning K-means clustering STM32

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