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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Abdulla, P.P. Sharma, Divyasheel Chandrika, K.R. Process Control Platform ABB Process Automation R&D Bangalore India Industrial Software Research ABB Corporate Research Center Bangalore India

ISBN: (纸本)9798350327328

Domain-specific Natural Language processing (NLP) tasks need prepared, ground-truth data. However, practical constraints make this task challenging. Domain engineers, who are the subject matter experts, are required to create high-quality training data. However, their availability is limited because they are busy with their day-to-day high-priority project and organizational tasks. Moreover, the data needed to be annotated to train NLP models may not be available at the outset itself. As the prepared data grows, NLP models should take advantage of the new data and train improved machine learning models. Hence, in this paper, we present KIKO, a domain-specific continuous learning NLP framework that allows domain engineers to perform annotations on one document at a time, and retraining when a large enough collection of documents becomes available. KIKO is made of several tools that pipeline together to process documents from the industrial domain. The pipeline enables parsing, pattern-matching, annotation, training, relationship mining, and continuous learning based retraining of the model by using a weaker form of active learning. © 2023 IEEE.

关键词： Pipelines

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Feedforward control of flat hybrid automata: a behavioral systems theory approach 63

Feedforward control of flat hybrid automata: a behavioral sy...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zahn, Frederik Kleinert, Tobias Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Karlsruhe Germany Rwth Aachen University Chair of Process Control Engineering Aachen Germany

ISBN: (纸本)9798350316339

Differential flatness has been defined in the literature for continuous time dynamical systems and for discrete time systems. We define flatness of automata from the perspective of behavioral systems theory, and synthesize a flatness definition for hybrid automata. These new definitions allow for feedforward trajectory generation for flat hybrid automata. Additionally, we provide necessary and sufficient conditions of hybrid automaton flatness for different types of continuous state constraints and switching conditions, and how to compute whether a hybrid automaton is flat. We illustrate feedforward trajectory generation based on our findings for a robotic manipulator example. © 2024 IEEE.

关键词： Continuous time systems

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Single-channel ultrahigh frequency moisture meter with direct measurement of moisture content of bulk materials

Single-channel ultrahigh frequency moisture meter with direc...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Ismailov, Mirhalil A. Nikolov, Nikola N. Alexandrova, Mariela Tashkent Inst. of Irrigation and Agricultural Mechanization Engineers National Research University Department "Automation and Control of Technology Process in Production" Tashkent Uzbekistan Faculty of Computer Science and Automation Technical University of Varna Department of Automation Varna Bulgaria

ISBN: (纸本)9798350353907

The article discusses the theoretical foundations of the design of a single-channel ultrahigh frequency moisture meter with direct measurement of the moisture content of bulk materials. In accordance with the requirements of the methodology for selecting efficiency criteria, it is necessary to develop structural diagrams of measuring devices. For these purposes, the standard deviation of the random error is determined, characterizing the accuracy. It includes the main components: sensitivity error, zero error and additive component. Mathematical models of structures are constructed and the standard deviation of random errors, which are caused by certain parameters and additive fluctuations, is calculated. A single-parameter ultrahigh-frequency method for determining the moisture content is proposed. This method provides high accuracy of a single-channel ultrahigh frequency moisture meter with direct measurement of the moisture content of bulk materials. The measuring device can be used in the agricultural industry, where humidity is one of the important parameters, starting with harvesting and ending with the release of finished products. © 2024 IEEE.

关键词： Moisture meters

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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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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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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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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-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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Evolving Neural Networks for Bi-Level Human Decision-Making Prediction in Mineral processing 50

Evolving Neural Networks for Bi-Level Human Decision-Making ...

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

作者： Hong, Shengbo Yu, Wen Zhu, Yingqin Chai, Tianyou Chen, Qingda Jia, Yao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Departamento de Control Automático CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9781665464543

This paper presents a novel approach for predicting bi-level human decision-making in mineral processing. This method aims to enhance the adaptability of human decision-making support systems and reduce dependence on expert knowledge. To achieve this, it leverages a combination of evolutionary computation and neural networks. The proposed method utilizes a hierarchical neural network framework optimized through evolutionary computation. This framework takes advantage of the sequential nature of decision-making within the mineral processing system. The effectiveness of the method is validated through experiments using 1,324 real-world data entries from a mineral processing plant in China. The results demonstrate the method’s capability to improve prediction accuracy. © 2024 IEEE.

关键词： Expert systems

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