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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Marko, Lukas Kugi, Andreas Steinboeck, Andreas Tu Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute Wien Austria Tu Automation and Control Institute Wien Austria

ISBN: (纸本)9798350335446

In the continuous hot-dip galvanizing process, the final zinc coating thickness is heavily influenced by the distance between the strip surface and the gas wiping dies which blow off excess zinc. To this end, establishing a flat, non-curved strip is a key prerequisite to minimize coating thickness deviations and inhomogeneities. A common way to influence the lateral strip profile is to introduce a controlled plastic deformation of the strip at the guiding rolls in the zinc bath. This motivates a data-based study of the optimal roll intermesh value of the zinc bath rolls to achieve a flat (zero-crossbow) strip profile during the galvanizing process. Based on this analysis, a data-based algorithm for the online prediction of the optimal roll intermesh value at the head end of each strip is developed and implemented in an industrial processing line. The prediction results from 1045 strips demonstrate the feasibility and accuracy of the proposed prediction algorithm. © 2023 IEEE.

关键词： Forecasting

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Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1239-1249页

作者： Ke Li Shunyi Zhao Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi 214122China Department of Chemical and Materials Engineering University of AlbertaEdmonton AB T6G 2G6Canada

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

关键词： filtering estimation error

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General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

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Chinese Journal of Natural Medicines 2025年第2期23卷 129-142页

作者： Shao Li Wei Xiao Institute of TCM-X/MOE Key Laboratory of Bioinformatics Bioinformatics DivisionBNRist/Department of AutomationTsinghua UniversityBeijing 100084China State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Jiangsu Kanion Pharmaceutical Co.Ltd.Lianyungang 222047China

The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain ***,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug *** pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research *** emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM *** approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM *** is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug *** goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

关键词： Network pharmacology Research and development of new traditional Chinese medicine drugs Expert consensus

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Distributed state estimation for linear timeinvariant dynamical systems:A review of theories and algorithms

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Chinese Journal of Aeronautics 2022年第6期35卷 1-17页

作者： Shuaiting HUANG Yuzhe LI Junfeng WU College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China

Distributed state estimation is of paramount importance in many applications involving the large-scale complex systems over spatially deployed networked *** paper provides an overview for analysis of distributed state estimation algorithms for linear time invariant systems.A number of previous works are reviewed and a clear classification of the main approaches in this field are presented,i.e.,Kalman-filter-type methods and Luenberger-observer-type *** design and the stability analysis of these methods are ***,a comprehensive comparison of the existing results is provided in terms of some standard metrics including the graph connectivity,system observability,optimality,time scale and so ***,several important and challenging future research directions are discussed.

关键词： Dynamical systems Distributed estimation Kalman filter Luenberger observer Linear systems Multi agent systems State estimation

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RIS-assisted Channel Prediction Model for Mine V2X Systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

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

ISBN: (纸本)9798331533991

The complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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The EE-COV-SSI Method for the Exact Identification of Model Order in Modal Analysis 10

The EE-COV-SSI Method for the Exact Identification of Model ...

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10th International Conference on Industrial Engineering and Applications, ICIEA 2023

作者： Zhang, Yafei Wang, Youming School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781643684086

The covariance-driven stochastic subspace modal parameter identification method has been widely used in the field of engineering structures. Effective determination of the model order of the structural system is the key to applying this method to identify the modal parameters. It is particularly difficult to determine the model order for unstable systems affected by noise disturbances and computational errors. In order to effectively determine the model order, an exponential eigenvalue entropy incremental covariance-driven stochastic subspace identification (EE-COV-SSI) algorithm is proposed. The condition number of the state matrix is used to determine the degree of perturbation of the response signal to the system stability. Meanwhile, the identification accuracy of the modal parameters is reflected by calculating the modal frequency coefficient of variation. Finally, the method is applied to the modal analysis of a four-story frame structure. The results show that the method can accurately identify the model order and improve the identification accuracy of the modal parameters. © 2023 The authors and IOS Press.

关键词： Modal analysis

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios 5

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

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Luo, Yujia Hu, Qingsong Cheng, Yuanxun Yuan, Shuya Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering 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: (纸本)9798331533991

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. © 2024 IEEE.

关键词： Deep neural networks

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Graph convolutional network soft sensor for process quality prediction

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Journal of process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Empirical Modeling of Unsteady Bulging in Continuous Slab Casters ⁎

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IFAC-PapersOnLine 2025年第1期59卷 19-24页

作者： Julian Landauer Luca Gasparini Andreas Kugi Andreas Steinboeck Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Austria Automation and Control Institute TU Wien Austria

In the continuous casting of steel slabs, a constant mold level is crucial for the quality of the final products. However, mold level fluctuations may be caused by unsteady bulging, i.e., a time-varying bending of the solidified strand shell between the rolls of the roll array due to variations in the strand shell thickness. A control-oriented model of unsteady bulging is needed to design controllers that should suppress unsteady bulging systematically. Most existing models use complex, detailed, nonlinear formulations, typically requiring high computational costs. Therefore, these models are unsuitable for closed-loop simulations or applications in real-time control. As an alternative, this work presents a novel empirical model that allows parameterization based on usually available measurements from industrial continuous slab casters. The model facilitates fast dynamic simulations of unsteady bulging and its results accurately match measurements.

关键词： Continuous slab casting Steel industry Bulging Mold level Empirical model Frequency response Real-time capability

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