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IEEE International Conference on Industrial Technology (ICIT)

作者： Zehui Kuang Fan Mao Xingyu Zhao Xiongbo Wan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Future Technology China University of Geosciences Wuhan China Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis.

关键词： Employee welfare Fault diagnosis Vibrations Industries Adaptation models Wavelet domain Convolution Frequency-domain analysis Feature extraction complexity theory

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference Data models

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Nanxin Huang Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method

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Journal of the Franklin Institute 2025年第7期362卷

作者： Chang, Xu-Kang He, Yong School of Automation China University of Geosciences 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

The issue of H∞ state estimation for neural networks with time-varying delays is investigated in this study. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with two delay-product-type terms is constructed to enhance the consideration of system state, delay, and delay derivative information. Furthermore, for taking into account more effective information, the LKF is augmented with both single and double integral variables. Accordingly, the LKF derivative becomes a higher-order term that exhibits nonlinear characteristics of the time delay. To solve the nonlinear problem, a variable-augmented-based free-weighting-matrices (VAFWMs) approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining less-conservative results. Consequently, two improved H∞ state estimation criteria are derived. Lastly, a numerical example is provided to demonstrate the merits and effectiveness of the presented methods. Meanwhile, to prove the practical viability of the presented methods, this study extends the methods to a real-world quadruple-tank process system. © 2025 The Franklin Institute

关键词： Lyapunov functions

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China 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

ISBN: (纸本)9798331521950

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process

Application of Particle Swarm Optimization Algorithm Based o...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yiheng Chen Fan Yin Xiaochong Chen Yi Ren Jie Hu Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process.

关键词： Job shop scheduling Heuristic algorithms Clustering algorithms Production Traveling salesman problems Stability analysis Robustness complexity theory Particle swarm optimization Convergence

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Kang Yi Xin Liu Wenkai Hu Yupeng Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals; then, the feature dimension is reduced by KPCA; last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data.

关键词： Vibrations Training Time-frequency analysis Sensitivity Pipelines Sensor phenomena and characterization Feature extraction Time-domain analysis Anomaly detection Principal component analysis

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Multi-Agent Coverage control in Non-Convex Annulus Region with Conformal Mapping

arXiv

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arXiv 2025年

作者： Feng, Xun Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

Efficiently fulfilling coverage tasks in non-convex regions has long been a significant challenge for multi-agent systems (MASs). By leveraging conformal mapping, this paper introduces a novel sectorial coverage formulation to transform a non-convex annulus region into a topologically equivalent one. This approach enables the deployment of MASs in a non-star-shaped region while optimizing coverage performance and achieving load balance among sub-regions. It provides a unique perspective on the partitioned sub-regions to highlight the geodesic convex property of the non-star-shaped region. By utilizing the sectorial partition mechanism and the diffeomorphism property of conformal mapping, a decentralized control law is designed to drive MASs towards a desired configuration, which not only optimizes the global coverage cost but also ensures exponential convergence of equitable workload. Moreover, an iterative search algorithm is developed to identify the optimal approximation of multi-agent deployment in the non-star-shaped region. Theoretical analysis is conducted to confirm the asymptotic stability and global convergence with arbitrary small tolerance of the closed-loop system. Finally, numerical simulations demonstrate the practicality of the proposed coverage formulation with conformal mapping. Copyright © 2025, The Authors. All rights reserved.

关键词： Conformal mapping

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