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IEEE International Conference on Industrial Technology (ICIT)

作者： Zixing Tao Weihua Cao Chao Gan School of Future Technology China University of Geosciences (Wuhan) Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

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.

关键词： Deep learning Sensitivity Atmospheric modeling Time series analysis Predictive models Air quality Transformers Environmental factors Spatiotemporal phenomena Long short term memory

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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

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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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan 430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural network architecture search Multi-objective optimization Weight sharing

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intelligent Monitoring Based on Integrated Temporal Dictionary Learning with Isometric Mapping for Geological Drilling Process

Intelligent Monitoring Based on Integrated Temporal Dictiona...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Ma, Tianyu Du, Sheng Fan, Haipeng Huang, Cheng School of Future Technology China University of Geosciences Wuhan 430074 China School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China

ISBN: (纸本)9798331521950

Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process. © 2025 IEEE.

关键词： Geological drilling process integrated temporal dictionary learning with isometric mapping process monitoring temporal smoothing

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Future Technology China University of Geosciences Wuhan 430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： deep geo-logical drilling process fuzzy C-means improved dung beetle optimization Rate of penetration support vector regression

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Adaptive Cooperative Estimation and Guidance Against Unknown Maneuvering Target

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Tianrun Chen, Yang-Yang Li, Shihua Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Advanced Ocean Institute of Southeast University Nantong226000 China

A critical challenge in missile guidance is the frequent inaccessibility of essential target information by on-board seekers. This paper studies the three-dimensional cooperative guidance issue of multiple missiles simultaneously attacking an unknown maneuvering target. Different from most existing research, the pitch and yaw lead angles of a maneuvering target are entirely unknown, while the position and velocity information is only accessible to neighboring missiles. An adaptive distributed estimator is designed to estimate the target information over a directed communication network, generating a transmitted target with similar maneuverability to the actual target for each missile. Boundedness of the estimation errors are guaranteed within a prescribed time. Subsequently, a modified guidance law that integrates proportional navigation (PN) and coordination components is developed for simultaneously tracking the trajectories of the transmitted targets. The superiority of the developed adaptive cooperative estimation and guidance approach is confirmed through simulations. © 1965-2011 IEEE.

关键词： adaptive estimate Cooperative guidance maneuvering target prescribed-time three-dimensional

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BeltLineNet: A Shape-Prior-Guided Lightweight Network for Real-Time Deviation Detection in Circular Pipe Conveyors

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IEEE Sensors Journal 2025年

作者： Zhao, Long Su, Jinhui Zhong, Yusheng Xie, Weiwei Su, Jinya Chen, Xisong Chen, Congyan Li, Shihua Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Fujian Longking Intelligent Conveying Engineering Co. Ltd Longyan China

Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring safe and efficient operation. However, existing methods, primarily developed for flat-belt systems, heavily rely on distinct belt-background semantics, making them unsuitable for circular conveyors where indistinct target features, motion blur, and occlusions pose significant challenges. To address these issues, we reformulate the deviation detection task as an object detection problem, proposing BeltLineNet, a lightweight, real-time deviation detection network specifically designed for circular pipe conveyors. Our method integrates explicit feature learning with a coarse-to-fine global feature fusion mechanism, enhancing belt line representation under complex conditions. Additionally, a shape-prior loss strategy is introduced to improve supervision during training, ensuring more accurate detection of elongated targets. The model is also optimized for real-time deployment through layer-adaptive pruning, achieving a balance between accuracy and computational efficiency. Extensive comparative experiments involving 8 state-of-the-art (SOTA) feature extraction networks and 6 feature enhancement strategies on a self-collected dataset under industrial conditions demonstrate that BeltLineNet surpasses SOTA feature extraction and fusion networks, improving average precision (AP) by 8.23% before pruning and 6.33% after pruning. © 2001-2012 IEEE.

关键词： Belt conveyors

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Improving Performance of Repetitive control for Nonlinear systems via Improved Disturbance Compensation

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IEEE Transactions on Industrial Informatics 2025年

作者： Tian, Shengnan Wang, Yibing Zhang, Manli Li, Yang Lu, Chengda Liu, Kang-Zhi She, Jinhua Wu, Min 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 Ministry of Education Wuhan430074 China Chiba University Department of Electrical and Electronic Engineering Chiba263-8522 Japan Tokyo University of Technology School of Engineering Hachioji192-0982 Japan

In practice, repetitive control (RC) is a type of learning control that exhibits good tracking performance. However, existing nonlinear RC methods lack analysis and design of the learning property, which results in limited performance. This study addresses the learning-enhanced issue. First, a new fuzzy Lyapunov candidate is constructed for stability analysis, which contains an integral term associated with the membership function and a double integral term. The design of the learning-dependent term integrates the nonlinear membership function information, which enhances the learning ability. Second, an additional first-order low-pass filter is incorporated into the conventional equivalent input disturbance estimator. The new filter acts as an integrator that adjusts the bandwidth of the disturbance compensation and gradually eliminates the disturbances in the output error. Third, a recursive optimization algorithm is used to design the controllers. Experimental comparisons on motor drive systems demonstrate the effectiveness and superiority of the method. © 2005-2012 IEEE.

关键词： Membership functions

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A Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Co. Ltd Jiangxi Nanchang330000 China

Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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