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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Distributed Time-Varying Constrained Convex Optimization: Finite-/Fixed-Time Convergence

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IEEE Transactions on control of Network Systems 2025年

作者： Guo, Ge Zhou, Zeng-Di Zhang, Renyongkang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University School of Control Engineering Qinhuangdao066004 China Northeastern University College of Information Science and Engineering Shenyang110819 China

This paper investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite- and fixed-time convergent solutions free from initialization. A nonsmooth optimization algorithm for state consensus achieving within a finite or fixed time is presented, by designing a projection-based log-barrier penalty cost function to meet the constraints and introducing integral sliding mode subsystems to guarantee zero- gradient-sum. With the use of the projection idea, the penalized functions are always well-defined (i.e., satisfying the logarithmic definition) for any system states, which avoids initializing of certain parameters. An adaptive gain scheme without any extra global information is presented. The time-varying zero- gradient-sum method here is feasible for cost functions with nonidentical Hessian matrixes, and applicable to finite or fixed time optimal consensus tracking. The effectiveness and superiority of our algorithms are verified with numerical simulations. © 2014 IEEE.

关键词： Cost functions

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Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction

Transductive Transfer Broad Learning Soft Sensor for Multimo...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jialiang Zhu Hongying Deng Danya Xu Tao Yang Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

In chemical processes, reliable soft sensors are generally established by enough labeled data. However, in most multimode processes, the collection of sufficient labeled data is difficult due to the high cost and complexity. In this work, transductive transfer broad learning (TTBL) is proposed for multimode quality prediction. By transferring the useful information from the related domain, unlabeled data in the prediction domain is utilized for modeling. First, the data feature is extracted by the feature and enhancement nodes. The similarity information of current and related domain data is captured by the $k$ nearest-neighbor graph. Then, label information in the related domain can be transferred and similar information in the same domain can be retained by the manifold regularization framework. Finally, the output weight can be effectively calculated by the ridge regression algorithm. Experimental results on continuous stirred tank reactor datasets show the superiority of TTBL, compared with several common methods.

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Improved Spacing Policy-Based Predefined-Time Cooperative control of Vehicles

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Qian Guo, Ge Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

This paper studies the problem of cooperative lateral-longitudinal control for a collection of vehicles. To eliminate the limitation of zero initial spacing errors, a novel spacing policy is introduced. Then, a fast vehicular cooperative control method is proposed based on the new spacing policy, which can ensure internal stability, string stability and traffic flow stability. The algorithm is derived via the use of sliding mode control with a predefined-time terminal sliding mode surface. The efficiency and feasibility of the algorithm are demonstrated by numerical simulations. © 1967-2012 IEEE.

关键词： Autonomous vehicles

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization.

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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Multimodal process Monitoring Based on Dynamic Extended K-means-VAE-RF

Multimodal Process Monitoring Based on Dynamic Extended K-me...

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第35届中国控制与决策会议

作者： Shiwei Su Kaixiang Peng Xueyi Zhang Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing

With the development of customization and flexibility of manufacturing industry,intelligent manufacturing process gradually presents multimodal characteristics,which are reflected in the strong coupling,nonlinear,multi working conditions and other characteristics of the *** this paper,a multi-modal process monitoring method based on dynamic expansion is *** view of the dynamic characteristics of process data,the process data is dynamically expanded through the amplification matrix,and then the multi-modal process data is modal divided through the improved K-means *** divided multi-modal process data is monitored using Variational Autoencoder,and relevant statistics and control limits are ***,the multi-modal process monitoring is constructed by introducing the method of random forest for modal *** validity of the algorithm is verified by real data of steel rolling process.

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Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

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IET Cyber-Systems and Robotics 2022年第1期4卷 38-50页

作者： Junsuo Qu Chenxue Tang Yuan Zhang Kai Zhou Abolfazl Razi School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent ProcessXi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Communication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Informatics Computing and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

关键词： machine learning pedestrian identification robustness visual surveillance visual tracking

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Model-Free Data-Driven Prescribed-TimePerformance control for Underactuated ASV

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Ying Lu, Wenlong Guo, Ge Dalian Maritime University Marine Electrical Engineering College Dalian116026 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

In this paper, a model-free data-driven prescribedtime-performance (DDPTP) algorithm is developed for the underactuated autonomous surface vehicle (ASV). By defining a leader to provide the dynamic reference signal, the proposed DDPTP algorithm can achieve high-precision target tracking tasks while ensuring collision avoidance and connectivity between the leader and follower. A prescribed-time-performance function (PTPF) is designed for the ASV, featuring an adaptive shape and customizable convergence time. By using both real-time and historical data, a data-driven neural predictor (DDNP) is developed to estimate system uncertainties including unmodeled dynamics, unknown environmental disturbances and unknown input gains. An adaptive control law is employed based on the knowledge obtained from the DDNP, without relying on prior information about the motion model parameters. A switching dynamic event-triggered mechanism (SDETM) is introduced, which can switch between fixed-threshold triggering mechanism and dynamic event-triggered mechanism (DETM). Through this SDETM, not only are communication resources conserved, but frequent updates of the control signal are also mitigated. The effectiveness of the proposed algorithm is validated through verification simulations. © 1967-2012 IEEE.

关键词： Model reference adaptive control

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Dynamic Fault Detection Based on Variational Bayesian Mixture Probabilistic Principal Component Analysis 11

Dynamic Fault Detection Based on Variational Bayesian Mixtur...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Huang, Chenghong Chai, Yi Wei, Chihang Zhu, Zheren Chongqing University College of automation Chongqing400044 China Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

It is essential to achieve real-time fault detection of the industrial process to reduce the occurrence of accidents during the industrial process. However, there are some problems in the actual monitoring process, such as the instability of sensor measurement, losing packet while transferring data, which causes problems like missing data and outliers while sampling from actual monitoring data. In addition, the monitoring data are interrelated in time series. Thus, a fault detection method based on DMPPCA model is proposed in this study. DMPPCA can not only consider the above problems at the same time, but also determine the dimension of latent variables automatically. Finally, the effect of proposed DMPPCA model was validated by a three-phase flow facility. © 2022 IEEE.

关键词： Fault detection

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