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第41届中国控制会议

作者： Penghao Chen Fei Liu Key Laboratory of Advanced Process Control for Light Industry Ministry of EducationInstitute of AutomationJiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The model reference adaptive control(MRAC) issue is addressed for a class of first-order linear time-invariant system(LTIS) with unknown control gain and asymmetric static output *** output constraints is efficiently handled by incorporating a barrier Lyapunov function(BLF).Completely unknown control gain is handled based on the Nussbaum *** the aid of Lyapunov synthesis method,the adaptive laws of unknown parameters are *** the properties of BLF and Barbalat's Lemma,it is proved that the tracking error converges asymptotically to zero while the output satisfies the *** comparison of the two simulation results is employed to demonstrate the efficacy of the suggested MRAC algorithm.

关键词： Model reference adaptive control Asymmetrical static output constraints Barrier Lyapunov function Unknown control gain Nussbaum function

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Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction 9

Transductive Transfer Broad Learning Soft Sensor for Multimo...

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhu, Jialiang Deng, Hongying Xu, Danya Yang, Tao Liu, Yi 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

ISBN: (纸本)9798350342239

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. © 2023 IEEE.

关键词： Forecasting

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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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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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Research and Analysis of the Method for Measuring the Moisture Content of Cottonmaterials at Ultrahigh Frequencies

Research and Analysis of the Method for Measuring the Moistu...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Turkmenov, Khasan E. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The article provides a theoretical analysis of the ultrahigh frequency (microwave) method of humidity measurement, discusses the problems of instrumentation of the method in question, and proposes a scheme of an experimental installation. Bulk cotton materials were selected as the objects of research. The electromagnetic wave radiation is analyzed during measurement and the evaluation of electrophysical characteristics requires the study of the functional dependence of the amplitude and phase shift on the mass ratio of humidity, mathematical models for various bulk cotton materials describing the joint effect of humidity, material density on the attenuation and phase shift of the passing wave are obtained. © 2023 IEEE.

关键词： Humidity control

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automation of Technological processes for controlling Grain Moisture in the Flowu

Automation of Technological Processes for Controlling Grain ...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Gaziyeva, Rano T. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The paper deals with the problems of grain wounding requires large energy costs, requiring a multi-stage process of grain processing, installation and perfect and reliable automation systems for the storage and processing of raw materials, theoretical and experimental studies and the results of the use of the microwave method in measuring the moisture content of grain and grain materials. The method under consideration is based on the principle of selective absorption of microwave radiation energy by water in materials and its derivatives. By the degree of absorption of radiation, the concentration of moisture is judged. The paper investigates the amplitude and phase methods of the passing wave in free space. The influence of the elastic properties of the material under study on the electrical characteristics of the microwave wave, suggesting fundamentally new ways of measuring the attenuation and phase shift of the electromagnetic wave, including discrete conversion of the signal of the primary converter, which does not require rigid metrological support and the use of a phase shift, is substantiated. Information on the values of the thickness of the material is analyzed in order to correct the influence of this value, consisting of two parameters: the signal of the passing wave, the attenuation and phase shift which characterizes the humidity and the additional signal - the phase wave reflected from the front surface of the materials and carrying information about its thickness. © 2023 IEEE.

关键词： automation

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Partible State Replication for Industrial controller Redundancy

Partible State Replication for Industrial Controller Redunda...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Bjarne Johansson Olof Holmgren Thomas Nolte Alessandro V. Papadopoulos ABB Process Automation Process Control Platform Vasteras Sweden Malardalen University Vasteras Sweden

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Distributed control systems are part of the often invisible backbone of modern society that provides utility services like water and electricity. Their uninterrupted operation is vital, and unplanned stops due to failure can be expensive. Critical devices, like controllers, are often duplicated to minimize the service stop probability, with a secondary controller acting as a backup to the primary. A seamless takeover requires that the backup has the primary's latest state, i.e., the primary has to replicate its state to the backup. While this method ensures high availability, it can be costly due to hardware doubling. This work proposes a state replication solution that doesn't require the backup to store the primary state, separating state storage from the backup function. Our replication approach allows for more flexible controller redundancy deployments since one controller can be a backup for multiple primaries without being saturated by state replication data. Our main contribution is the partible state replication approach, realized with a distributed architecture utilizing a consensus algorithm. A partial connectivity-tolerant consensus algorithm is also an additional contribution.

关键词： Electricity Redundancy Decentralized control Consensus algorithm Hardware

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Semi-Supervised Caustic Concentration Prediction in Alumina Production with Transformers 50

Semi-Supervised Caustic Concentration Prediction in Alumina ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Gao, Suting Yu, Wen Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Departamento de Control Automatico CINVESTAVIPN National Polytechnic Institute Mexico City Mexico

ISBN: (纸本)9781665464543

Accurate and real-time prediction of caustic concentration is essential for optimal control in alumina production. However, training effective models is often hampered by the scarcity of labeled assay data. This paper proposes a novel semi-supervised learning approach to address this challenge. We introduce a Transformer-based Auto-Encoder (AE) structure that uses both labeled and unlabeled data for robust caustic concentration prediction. The Transformer encoder extracts key features from various variables and time series data, enabling efficient low-dimensional representation. The decoder then performs a dual task of reconstructing the input data and predicting the caustic concentration. This structure combined with a robust regularization strategy enhances the model’s resilience to data noise and missing values. Consequently, the proposed method achieves accurate caustic concentration prediction using significantly less labeled data. This approach has the potential to significantly improve alumina production control by reducing reliance on expensive and time-consuming labeled data collection. © 2024 IEEE.

关键词： Self-supervised learning

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Real-Time Caustic Ratio control in Alumina Digestion Using Deep Reinforcement Learning 6

Real-Time Caustic Ratio Control in Alumina Digestion Using D...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Yu, Liyi Yu, Wen Cheng, Siyu Jia, Yao Chai, Tianyou Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Cinvestavipn National Polytechnic Institute Departamento de Control Automatico Mexico City Mexico

ISBN: (纸本)9798350356618

Due to the lag time in the alumina digestion process (ALDP), traditional control methods that depend on delayed measurements often struggle to maintain the optimal caustic ratio, which is crucial for achieving optimal performance. This paper proposes a novel intelligent operational control method to address this challenge. Our multi-stage framework employs a fuzzy inference system (FIS) to generate an initial setpoint based on process knowledge. Subsequently, a bi-directional gated recurrent unit (BiGRU) network predicts the endpoint caustic ratio, assessing the validity of the initial setpoint. Finally, a deep deterministic policy gradient (DDPG) adjustor refines the setpoint in real-time based on the predicted and target values. Validated with industrial data, this method significantly improves the qualification rate of the caustic ratio compared to manual operation. It demonstrates the effectiveness of DDPG for real-time setpoint adjustment, leading to improved product quality in the ALDP. © 2024 IEEE.

关键词： Deep reinforcement learning

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Design of Supervisory Model Predictive control for HVAC Systems and Two Zones with Consideration of Energy Efficiency and Thermal Comfort 23

Design of Supervisory Model Predictive Control for HVAC Syst...

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23rd International Conference on control, automation and Systems, ICCAS 2023

作者： Srisurapanon, Panithan Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation for Process Systems Faculty of Engineering Department of Electrical Engineering Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9788993215274

This paper presents the design of supervisory model predictive control for HVAC systems and two zones. The supervisory control (SC) is designed to find the optimal reference temperature of each zone. The design criteria consist of the total operating cost (TOC) and the thermal comfort cost (TCC). Then, the model predictive control (MPC) is designed to track the optimal reference signals. When SC and MPC are designed for two zones simultaneously, they are referred to centralized SC and centralized MPC. On the other hand, when SC and MPC are designed for two zones separately, they referred to decentralized SC and decentralized MPC. We then compare the results between the decentralized control and the centralized control. The trade-off curve shows that the performance of centralized control is better than that of decentralized control. In particular, centralized MPC improves the performance in tracking reference signals. Furthermore, TOC of centralized control is lower than that of decentralized control. It can be concluded that the centralized control is appropriate for two zones in terms of the tracking of reference and the electrical charge. © 2023 ICROS.

关键词： Model predictive control

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