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IEEE Conference on Decision and control

作者： Dan Wang Wei Chen Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

A new notion of phase of multi-input multi-output (MIMO) systems was recently defined and studied, leading to new understandings in various fronts including a formulation of small phase theorem, a performance criterion named $\mathcal{H}_{\infty}$ phase sector, and a sectored real lemma, etc. In this paper, we define a new notion of $\mathcal{H}_{2}^{T}$ -dissipativity and show the connection between the phase of a multivariable linear time-invariant (LTI) system and the $\mathcal{H}_{2}^{T}$ -dissipativity. The $\mathcal{H}_{2}^{T}$ -dissipativity, roughly speaking, is dissipativity restricted to the time-domain $\mathcal{H}_{2}$ space which consists of $\mathcal{L}_{2}$ signals with only positive frequency components. In addition, by exploiting the newly defined $\mathcal{H}_{2}^{T}{-}$ dissipativity, we also study the phase of a feedback system and provide a physical interpretation of the sectored real lemma.

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Fault Detection Method of Photovoltaic System Based on Power Change of Array

Fault Detection Method of Photovoltaic System Based on Power...

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2021年第四届算法、计算和人工智能国际会议

作者： Chao Tan Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast University

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant ***, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power,and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified.

关键词： Photovoltaic System(PVS) Fault detection algorithm Electrical data Sensorless Power characteristics

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Subgroup State Prediction under Different Noise Levels Using MODWT and XGBoost

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Journal of Healthcare engineering 2023年第1期2023卷

作者： Zhao, Xin Nie, Xiaokai School of Mathematics Southeast University Nanjing211189 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518057 China

In medical states prediction, the observations of different individuals are generally assumed to follow an identical distribution, whereas precision medicine has a rigorous requirement for accurate subgroup analysis. In this research, an aggregated method is proposed by means of combining the results generated from different subgroup models and is compared with the original method for different denoising levels as well as the prediction gaps. The results using real data demonstrate the effectiveness of the aggregated method exhibiting superior performance such as 0.95 in AUC, 0.87 in F1, and 0.82 in sensitivity, particularly for the denoising level that is set to be 2. With respect to the variable importance, it is shown that some variables such as heart rate and lactate arterial become more important when the denoising level increases. © 2023 Xin Zhao and Xiaokai Nie.

关键词： Forecasting

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An Improved Faster R-CNN for Railway Fastening System Detection 21

An Improved Faster R-CNN for Railway Fastening System Detect...

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Peng, Xinfeng Tong, Shuzhen Lu, Xiaobo Wei, Yun School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Beijing Mass Transit Railway Operation Corporation Limited Beijing100044 China

ISBN: (纸本)9781450389853

In the automatic railway anomaly inspection technology based on image processing and deep learning, an effective algorithm used for high-precision detection of the fastening system is very important, especially in turnout sections. It is challenging because the background of the turnout sections is complicated with various types of targets. This paper improved the Faster R-CNN model, used multi-scale feature map fusion for small targets. And modified predefined anchor to generate region proposals, added attention module to make the network focus on meaningful feature. Besides, this paper used cross-entropy function and SmoothL1 loss function for training and labeled 1200 image samples as dataset. Compared with the original Faster R-CNN model, the experimental results (AP) of the improved model in this paper increased from 96.3% to 98.9%, which effectively reduced the fault detection and missed detection and improved the accuracy of location.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Deep learning

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Secure Consensus of Two-Time-Scale Markov Jump Multi-agent systems with Partially Unknown Transition Rates Against Hybrid Cyber-Attacks

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Neural Processing Letters 2023年第5期55卷 6089-6108页

作者： Zhang, Guanzheng Wang, Jing Li, Feng Shen, Hao Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China School of Electrical and Information Engineering Anhui University of Technology Ma’anshan China

This paper addresses the secure consensus control problem for a class of continuous-time two-time-scale Markov jump multi-agent systems under hybrid cyber-attacks, which suffers from both the denial-of-service attacks and deception attacks. Firstly, to make the constructed model fit the practice better, the dynamic of each agent is represented by a two-time-scale system and the transition rate matrix of the Markov chain is assumed to be partially unknown. Then, a secure consensus protocol is designed to deal with the hybrid cyber-attacks and guarantee the average consensus can be achieved with a prescribed $$H_{\infty }$$ performance. Based on the stochastic analysis approach and Lyapunov stability method, some sufficient conditions are given in the form of linear matrix inequalities. Furthermore, the desired secure consensus protocol gains can be obtained by solving a convex optimization problem. Finally, an illustrative example is utilized to demonstrate the effectiveness of the proposed method.

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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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Distilling Object Detectors with Efficient Logit Mimicking and Mask-Guided Feature Imitation

SSRN

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SSRN 2023年

作者： Lu, Xin Cao, Yichao Chen, Shikun Li, Weixuan Zhou, Xin Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Nanjing Enbo Technology Co. Ltd. Nanjing210007 China

Knowledge distillation (KD) is a promising approach to learning compact models for object detection with information inherited from intricate teacher networks. In this paper, we raise some shortcomings of existing KD methods for object detectors, e.g., ignoring knowledge selection, coarse feature imitation mask, etc. To address these issues, we present a novel KD framework to train efficient object detectors via Logit Mimicking and Feature Imitation (LMFI). First, we put forward a novel logit mimicking method for classification and localization heads. On the one hand, it first proposes to mimic the classification logits of one-category object detectors. On the other hand, we exploit the localization knowledge from teacher predictions and ground truths, which dynamically guides the learning of student's regression outputs by stages. Second, an adaptive positive teacher selection (APTS) strategy is designed to obtain high-quality teacher samples during distillation, which reduces the transmission of inferior knowledge. Moreover, we heuristically introduce a soft metric and fine-grained mask to reconcile the discrepancy between teacher and student features in a position-wise manner. Extensive experiments show that LMFI outperforms the state-of-the-art KD frameworks for object detection. It can significantly boost the performance of various detectors on different benchmarks, e.g., 2.83% and 2.58% MR-2 reduction of Cascade R-CNN on the S and All sets of CityPersons, and an improvement of ResNet-50 based ATSS from 36.7% to 40.5% mAP on the COCO benchmark. © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Robust Clock Synchronization Scheme for Wireless Sensor Networks against DoS Attacks

A Robust Clock Synchronization Scheme for Wireless Sensor Ne...

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第40届中国控制会议

作者： Xuxin Zhang Shiyu Chen Bingya Zhao Ya Zhang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the clock synchronization problem of wireless sensor networks with time-varying noises and malicious denial of service（Do S） attacks. A robust and secure clock synchronization scheme based on filter is proposed in intermittent Do S attack case, which is composed of three steps: relative skew estimation, clock skew compensation and software clock reading compensation. Through the clock skew compensation, the synchronization error of the clock skew can be constrained into a bounded range. Through the software clock reading compensation, the possible error of the software clock reading can be compensated and the clock synchronization can be achieved completely. In addition, extensive simulations are conducted which validate that the proposed scheme outperforms the existing ones.

关键词： clock synchronization wireless sensor networks DoS attacks bounded noises filter

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Efficient surrogate-based optimization method and application based on collaborative improvement aggregation strategy

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Kongzhi yu Juece/control and Decision 2025年第6期40卷 1975-1984页

作者： Xiao, Tian-Li Wu, Feng Lin, Cheng-Long School of Management Science and Engineering Anhui University of Technology Ma’anshan243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan243002 China School of Economics and Management Anhui Polytechnic University Wuhu241000 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China

To address the problem that greedy characteristics lead to slow convergence and low accuracy problems of surrogate-based optimization solutions in the later stage, a collaborative improvement aggregation strategy is proposed, which is extended to an efficient surrogate-based optimization method for expensive black-box problems. The proposed strategy integrates probability improvement and mean improvement criteria using Chebyshev decomposition and achieves a balance between global exploration and local search capabilities through random weight coefficients. In addition, the differences between surrogate-based optimization and surrogate-assisted optimization methods are analyzed from the perspective of candidate point sets, further exploring the role of randomness in optimization design. The experimental results show that this method can effectively improve the convergence accuracy of the optimized solution in black-box problems. Compared with similar techniques, the proposed method has higher convergence accuracy and stronger robustness of optimized solutions. © 2025 Northeast University. All rights reserved.

关键词： Tabu search

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Thickness evolution of commercial Li-ion pouch cells with siliconbased composite anodes and NCA cathodes

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Science China(Technological Sciences) 2021年第1期64卷 83-90页

作者： ZHANG XingYu HE Jie ZHOU Jiang CHEN HaoSen SONG WeiLi FANG DaiNing State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China State Key Laboratory of Explosion Science and Technology Institute of Advanced Structure TechnologyBeijing Institute of TechnologyBeijing 100081China Lishen Research Institute Tianjin Lishen Battery Joint-stock Co.Ltd.Tianjin 300384China State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China

It is significant to monitor the thickness change of commercial Li-ion pouch cells that are assembled from Si-based electrodes and layered cathodes since the volume variation is critically linked with kinetic reaction mechanism and chemomechanical degradation. In this work, the single-point and full-field measurements are conducted on the thickness evolution of Si Ox/Cgraphite/lithium nickel-cobalt-aluminum oxide(Si Ox-G/NCA) batteries. It is found that an abnormal overshoot that has been rarely observed in the previous studies appears at the beginning of discharge, which is believed to be the combined results between the rapid expansion of NCA and the contraction of graphite related to the state of charge(SOC). Compared with the results of single-point measurement, the spatial displacement distribution obtained from three-dimensional(3D) scanning suggests the full-field thickness evolution of the entire cell scale, which is more feasible and efficient to provide full-field information for supporting pouch cell design and pack integration.

关键词： thickness change 3D scanning silicon-based anode NCA cathode lithium-ion battery

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