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SSRN 2023年

作者： Zhirabok, Alexey Zuev, Alexander Filaretov, Vladinir Far Eastern Federal University Vladivostok Russia Institute of Marine Technology Problems Vladivostok Russia Institute of Automation and Process Control Vladivostok Russia

The paper considers the problem to construct interval observers for discretetime linear systems under the external disturbances, measurement noises, and unknown parameters. To construct such observers, we use not the original system but its reduced-order model of minimal dimension which does not depend on the disturbances or has minimal sensitivity to them. The observers are designed in such a way to estimate the prescribed function of the original system vector of state. The obtained results are displayed by example of the electric servoactuator. © 2023, The Authors. All rights reserved.

关键词： Linear systems

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Nonlinear Quasi-unknown Input Observer Design using Dissipativity 63

Nonlinear Quasi-unknown Input Observer Design using Dissipat...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Schaum, A. Koch, S. Moreno, J.A. University of Hohenheim Chair of Process Analytics The Computational Science Hub Stuttgart Germany Institute of Automation and Control Graz University of Technology Graz Austria Instituto de Ingeniería Universidad Nacional Autónoma de México Coordinación de Eléetrica y Computación Ciudad de México Mexico

ISBN: (纸本)9798350316339

This paper addresses the design of nonlinear quasi-unknown input observers for systems that can be interpreted as an interconnection of a linear dynamical subsystem with a static nonlinear feedback subject to additive perturbations. As assumed classically an exosystem is considered describing the dynamics of the perturbation, meaning that the dynamical mechanisms giving rise to the perturbation are sufficiently well known but the underlying initial condition is unknown. Extending classical results based on the Sylvester equation, and combining them with well-established dissipativity concepts and design methods, a new approach for quasi-unknown input observer design is obtained. It simplifies previous work on general unknown input observer design by exploiting the structural knowledge about the exosystem, without extending the state dimension. The approach is illustrated with numerical case examples. © 2024 IEEE.

关键词： Nonlinear simulations

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation 9

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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9th IEEE International Conference on Progress in Informatics and Computing, PIC 2024

作者： Das, Sanchita Maity, Gouranga Mandal, Diptarka Kaplun, Dmitrii Voznesensky, Alexander Sarkar, Ram Jadavpur University Dept. of Cse Kolkata India Defence Instituite of Advanced Technology Department of Applied Mathematics Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China St. Petersburg Electrotechnical University 'LETI' Dept. of Automation and Control Process St. Petersburg Russia

ISBN: (纸本)9798350363203

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net. © 2024 IEEE.

关键词： Image segmentation

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Exact Algorithm for Batch Scheduling with Many-to-Many Job-Tool Matching and Job Release Time Constraints

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IEEE Transactions on automation Science and Engineering 2025年 22卷 16499-16511页

作者： Li, Shengchao Liu, Shixin Zhao, Ziyan Zhou, Mengchu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang 110819 China Northeastern University at Qinhuangdao Hebei Key Laboratory of Industrial Software for Advanced Planning and Scheduling of Complex Industrial Processes School of Control Engineering Qinhuangdao 066004 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem is NP hard. To obtain exact solutions, we propose a novel branch-and-bound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled;the branching strategies accelerate solving speed;and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs. © 2004-2012 IEEE.

关键词： Batch scheduling branch-and-bound algorithm heterogenerous tool demand mixed-integer linear programming satellite vibration test system

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Physics-guided graph learning soft sensor for chemical processes

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Chemometrics and Intelligent Laboratory Systems 2024年 249卷

作者： Liu, Yi Jia, Mingwei Xu, Danya Yang, Tao 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 Hsinchu300044 Taiwan

The surge in data-driven soft sensors for industrial processes is evident. However, most of them suffer from the limitation of being black-box models and this will hamper their widespread use. In response to this challenge, this study proposes a physics-guided graph-learning soft sensor that integrates a physical understanding of industrial processes by incorporating graph-based concepts with process physics. The soft sensor first constructs physical information based on causal relationships between variables using the conditional Granger causality test. Subsequently, it autonomously learns the unique sample information of each observation while employing a regularization loss to ensure the sparsity of the learned information. The model employs a two-stream structure for spatiotemporal encoding of both the physical and sample information. The modeling and prediction results on a penicillin fermentation process indicate that, using the proposed method, the knowledge gained from the data aligns with existing prior knowledge. This approach shows promise in filling the gap between data-driven and physics-based modeling in chemical processes. © 2024 Elsevier B.V.

关键词： Graphic methods

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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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The Research on Sheet Metal Part Recognition Technology Based on Improved Mask R-CNN 5

The Research on Sheet Metal Part Recognition Technology Base...

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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Liu, Mingfei Liu, Jun Zhou, Kecheng Wang, Jian School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798331530334

The visual recognition of sheet metal parts is a crucial component of industrial automation sorting, particularly under complex lighting and background interference conditions. To enhance the accuracy of sheet metal part recognition, this paper curates a dataset of sheet metal parts acquired by an intelligent light field image sensor and proposes a visual recognition model, PA-CBAM, based on an improved Mask R-CNN. By improving the original feature pyramid FPN and introducing the Convolutional Block Attention Module (CBAM), the model enhances multi-scale feature fusion and the focus on key features: (1) Bottom up paths and shortcut connections have been added between different layers of the traditional FPN feature pyramid, thereby improving multi-scale object detection capability. (2) CBAM further optimizes the model's channel and spatial attention mechanisms, effectively enhancing the capture of edge details and the suppression of background noise. Experimental results show that the proposed PA-CBAM model significantly improves detection and segmentation performance compared to traditional methods, particularly in terms of edge recognition accuracy for sheet metal parts under complex lighting conditions. © 2024 IEEE.

关键词： Sheet metal

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

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

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Liyi Yu Wen Yu Siyu Cheng Yao Jia Tianyou Chai 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: (数字)9798350356618

ISBN: (纸本)9798350356625

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.

关键词： Fuzzy logic Economics process control Bidirectional control Logic gates Real-time systems Product design Time measurement Quality assessment Qualifications

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Semi-Supervised Caustic Concentration Prediction in Alumina Production with Transformers

Semi-Supervised Caustic Concentration Prediction in Alumina ...

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Annual Conference of Industrial Electronics Society

作者： Suting Gao Wen Yu Tianyou Chai 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: (数字)9781665464543

ISBN: (纸本)9781665464550

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.

关键词： Accuracy Time series analysis Noise Production control Transformers Feature extraction Data models Decoding Data mining Resilience

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Indistinguishability analysis and observer design for size-structured cell populations

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IFAC-PapersOnLine 2023年第2期56卷 581-586页

作者： P. Jerono J.J. Winkin A. Vande Wouwer A. Schaum Digital Process Engineering Group (DPE) Karlsruhe Institute of Technology Germany Department of Mathematics and Institute for Complex Systems (naXys) University of Namur Belgium Systems Estimation Control and Optimization (SECO) University of Mons Belgium Chair of Automation and Control (ACON) Kiel University Germany

The observability property of population balance equations with biomass measurements is addressed within the framework of indistinguishable trajectories. The population balance equation is described by a partial integro-differential equation which is coupled with an ordinary differential equation for the nutrient dynamics. For the semi-discretized model equations, the dynamics of indistinguishable trajectories are derived and evaluated for the special case of equal partitioning at cell division revealing that the observability property is guaranteed as long as the nutrient concentration is not depleted. Based on these results, an observer is designed and tested in simulation to estimate the cell population distribution using biomass measurements in a batch bioreactor for the case of a bi-structured population.

关键词： Observability indistinguishable trajectories cell population dynamics

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