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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng 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

ISBN: (纸本)9798331521950

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. © 2025 IEEE.

关键词： Frequency domain analysis

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A coordinated charging/discharging strategy for EVs with flexible mobility in multi-temporary microgrids

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Sustainable Energy, Grids and Networks 2025年 42卷

作者： Wang, Zhixian Falabretti, Davide Wang, Ying Zhang, Kaifeng Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University NO.2 Sipailou Road Jiangsu Nanjing210096 China Department of Energy Politecnico di Milano Milan20156 Italy

The increasing occurrence of extreme weather events can lead to more major power blackouts, which attracts attention to the utilization of temporary microgrids (TMGs) in the restoration stage. However, single TMGs originating after a blackout usually have weak resilience, because of their limited available resources. Therefore, cooperation between microgrids needs urgently to be studied: to this end, this paper proposes a novel charging/discharging strategy for electric vehicles (EVs) to achieve optimal power coordination between TMGs, by taking advantage of EV's temporal-spatial flexible mobility. First, a new user response willingness model for EVs is established considering move distance and charging compensation, and this model is combined with the EV eligibility assessment based on traveling time to evaluate the EV mobile possibility across TMGs. Then, a suitable resilience factor is proposed to measure the resilience of TMGs considering both the current and future operation conditions. Finally, a novel coordinated EV charging/discharging strategy exploiting the EVs’ mobility is developed based on a mixed integer optimization model, to enhance system resilience and reduce total regulation costs. A case study based on the realistic topology of Milan's urban area is analyzed, comparing the results obtained with the strategy without EV spatial cooperation. As a result, the proposed strategy can enhance the resilience of TMGs by 20.93 % and reduce the total regulation cost which is paid to regulation resources for ensuring the power balance of each TMG by 8.43 %. © 2025 Elsevier Ltd

关键词： Travel time

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

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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Frequency Regulation Reserve Optimization Considering Flexible Resource Complementary Aggregation

Frequency Regulation Reserve Optimization Considering Flexib...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zirui Huang Wenbo Mao Yaping Li Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China China Electric Power Research Institute CO. Ltd Nanjing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This paper proposes a fast-solving strategy that uses the complementarity of flexible resources to aggregate and participate in frequency regulation. Firstly, this paper proposes a frequency regulation performance index which can consider the adjustment rate, response delay time, adjustable capacity and adjustment cost of flexible resources. Then, based on the index, this paper uses the K-means algorithm to cluster the resources with similar performance. Then, this paper proposes an aggregation strategy considering the complementary of multiple flexible resources. Finally, this paper improves the unit combination optimization model, so that flexible resources can participate in the optimization in the form of aggregation, and the ratio of resources can be determined in advance, reducing the number of optimization parameters and improve the efficiency of solution. In this paper, the simulation is carried out on the IEEE-118 node. The results show that the proposed scheme can effectively improve the rate of optimization solution and effectively avoid the curse of dimensionality in the face of a large number of flexible resources.

关键词： Costs Optimization models Face recognition Smart grids Performance analysis Delays Time factors Indexes Optimization Frequency control

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Robust control Analysis With Model Transformation for Interval Type-2 Fuzzy Systems

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IEEE Transactions on Fuzzy Systems 2025年

作者： Yang, Jie Gai, Shaoyan Da, Feipeng Xie, Wenbo Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Shanghai University and Shanghai Artificial Intelligence Laboratory School of Mechatronic Engineering and Automation Shanghai200444 China

To address the problem of robust stability analysis for interval type-2 fuzzy systems (IT2FSs), this paper proposes an innovative analysis approach based on model transformation. Firstly, a classical piecewise linear approximation method is utilized to process the upper boundary membership functions (UMFs) and lower boundary membership functions (LMFs) of the footprint of uncertainty (FOU) in IT2FSs, resulting in linear boundary membership functions (MFs) that are more convenient for analysis, along with the corresponding approximation error functions. Subsequently, a novel error model transformation method is introduced to handle these error terms. By constructing new fuzzy rules and MFs, the boundary error terms are converted into a new fuzzy model, thereby incorporating more information about the error functions into the stability analysis. Based on this model, a robust stability condition in the form of linear matrix inequalities (LMIs) are derived, achieving improved robustness. Finally, the effectiveness of the proposed method is validated through simulations on real-world systems, and its superiority is demonstrated by comparison with existing methods. © 2025 IEEE.

关键词： Membership functions

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Data-Driven Prediction of Coefficient of Friction in Wet Friction Components: A Model Development and Interpretability Analysis

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Journal of Tribology 2025年第7期147卷 074601页

作者： Wu, Jianpeng Zhao, Peng Cui, Jiahao Wang, Liyong Yang, Chengbing Ouyang, Jianping Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China School of Mechanical and Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing100083 China Mechanical Electrical Engineering School Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China

Predicting the coefficient of friction (COF) is essential for enhancing the efficiency and reliability of mechanical systems. Nevertheless, traditional mechanistic models relying on fixed values or fitted curves fail to accurately capture this complexity. To address this issue, this paper proposes a model for predicting the COF of wet friction components using an extreme gradient boosting (XGBoost) algorithm optimized by the sparrow search algorithm (SSA). This model effectively captures the nonlinear relationships among relative speed, pressure, temperature, and COF. As a result, the proposed SSA-XGBoost model exhibits excellent predictive performance with a root mean square error (RMSE) of only 0.063, and 88.3% of the COF predictions have a relative error of less than 1%, significantly outperforming other deep-learning algorithms. Additionally, to enhance the understanding of the COF prediction results for wet friction components, the SHapley Additive exPlanations (SHAP) model is used to explore the influence of relative speed, pressure, and temperature on the predicted COF values. Copyright © 2024 by ASME.

关键词： Deep learning

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Intrinsic-feature-guided 3D Object Detection

arXiv

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arXiv 2025年

作者： Zhang, Wanjing Wang, Chenxing School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://***/zhangwanjingjj/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

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

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. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Improved Synthesis of Nondeterministic Publicly-Known Edit Functions for Opacity Enforcement

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IEEE Transactions on Automatic control 2025年

作者： Liu, Rongjian Hadjicostis, Christoforos N. Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

In this paper, we investigate the synthesis problem of edit functions for opacity enforcement in systems modelled as partially-observed finite-state automata. For better plausible deniability for the edit functions, in cases where the intruder may learn/know their existence, a synthesis problem for nondeterministic edit functions under a publicly-known setting was introduced in the literature, but not yet fully solved. In light of this, we revisit the publicly-known edit mechanism by proposing a sound and complete approach for solving the synthesis problem of nondeterministic publicly-known edit functions. For practical applications, the edit function is assumed to have limited erasure and insertion ability. Our approach is based on a new labelled finite-state structure, and results in wider applicability than previously-proposed in the literature. © 1963-2012 IEEE.

关键词： Discrete event simulation

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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