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arXiv 2024年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael R. Krishnadas, Raamadaas Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Control and Automation Group Inspire AG Zürich8005 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland ZHAW Centre for Artificial Intelligence Zürich University of Applied Sciences Switzerland

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro

关键词： controllers

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Technological Machines Diagnostics of Grain processing Enterprises as the Object of automation

Technological Machines Diagnostics of Grain Processing Enter...

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Automatics and Informatics (ICAI), International Conference

作者： Palvan I. Kalandarov Shakhnoza R. Ubaydullayeva Nikola N. Nikolov Aziz Hayitov Husen Sharifov Department “Automation and control of technology process in production” «Tashkent institute of irrigation and agricultural mechanization engineers» National Research University Tashkent Uzbekistan Department of Automation Faculty of Computer Science and Automation Technical University of Varna Varna Bulgaria Bukhara Institute of Natural Resource Management of the National Research University «Tashkent Institute of Irrigation and Agricultural Mechanization institute» Tashkent Uzbekistan

The article gives the basic concepts of reliability, performance, durability, considers the issues of changing the technical state of the machine during operation. The models under consideration have the ability to introduce various perturbations in order to simulate physical defects, and numerical simulation on a computer made it possible to obtain solutions in the form of temporary realizations of oscillations for different states of the simulation object. The cooperative use of the full-scale method and mathematical modeling made it possible to reduce the volume of full-scale experiments and apply statistical methods of analysis in the problem of classifying the technical condition by constructing mathematical models of the probability of failure and uptime during their operating time. The content of the main types of machine states is revealed, considerable attention is paid to the issues of identifying and influencing failures and the causes of failures in the machine, the reasons for the loss of performance during operation.

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Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical Systems:An Event-Triggered Scheme

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

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第42届中国控制会议

作者： Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

As an important component in the industry sector,the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability *** propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems（AIECS）.In the day ahead scheduling,a detailed mathematical model for AIECS is presented,upon which a multi-objective optimization considering cost,CO emission and reliability is *** the intra-day operation,an event-triggered mechanism is firstly developed in the real-time operation of AIECS,and it enables the AIECS to be risk-aware just in *** monitoring the difference between the predicted state of charge and the capacity limitations of battery,the AIECS identifies whether it is in hazardous ***,the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed,such that their respective advantages are *** studies demonstrate that the proposed approach guarantees the system safety,and brings good revenue.

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities

Data-Driven Distributionally Robust Planning of Energy Hub C...

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第42届中国控制会议

作者： Xutao Ma Chao Ning Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol,under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology.

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

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

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Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

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

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

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.

关键词： Image segmentation Accuracy Microscopy Computational modeling Biological system modeling Source coding Computer architecture Entropy Robustness Medical diagnostic imaging

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Consensus Robustness under PI Protocols with Undirected Graphs

Consensus Robustness under PI Protocols with Undirected Grap...

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American control Conference (ACC)

作者： Qi Mao Dan Ma Yanling Ding Hui Peng Jie Chen Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

This paper concerns robust consensus problems for continuous-time first-order multi-agent systems with respect to uncertain gain and phase of the agent’s frequency response varying within certain ranges. We consider dynamic output feedback control protocol in the form of proportional-integral (PI) control. The agents can in general be nonminimum phase and unstable systems, which are interconnected by an undirected graph network. We seek to determine the largest ranges of gain and phase variations, referred to as the gain consensus margin (GCM) and the phase consensus margin (PCM), respectively, so that consensus can be achieved robustly within these ranges. Our main results consist of explicit analytical expressions of the GCM and PCM, which demonstrate how the agent’s unstable pole and nonminimum phase zero, as well as the network connectivity may fundamentally confine the gain and phase variation ranges so that consensus can or cannot be maintained.

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Research on Cardiac Medical Image Segmentation Based on an Improved U-Net Model

Research on Cardiac Medical Image Segmentation Based on an I...

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Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuqing Wu Jun Wang Rui Wang Jian Wang Kecheng Zhou Xiaoxin Zhang 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 Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Emergency Zhongyi Northeast International Hospital Co. Ltd. Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computational modeling Modulation Context awareness Robustness Medical diagnosis Cardiovascular diseases Context modeling

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Automatic decision-making system for the desalinization of water for irrigation 1

Automatic decision-making system for the desalinization of w...

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1st International Conference on Energetics, Civil and Agricultural Engineering, ICECAE 2020

作者： Gaziyeva, R. Ozodov, E. Bozorov, E. Nigmatov, A. Department of Automation and Control of Technology Process Tashkent Institute of Irrigation and Agricultural Mechanization Engineers 39 Koriy-Niyoziy Tashkent100000 Uzbekistan

The high salinity of water negatively affects soil fertility up to yield loss. In regions with high salinity, unorthodox methods and devices are used to lower the level of mineralization. This article discusses the use of an automatic decision-making system for a device to lower the level of mineralization by diffusion mixing. Construction able to filter water to irrigation rates up to 3 grams per liter and extent the operating period of reverse osmosis. The article provides a detailed description algorithm of a program for automatic control of the water purification process based on the Atmega 328 processor. © Published under licence by IOP Publishing Ltd.

关键词： Mineralogy

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Incipient Sensor Fault Detection by Directly Monitoring Sliding Window Based Singular Values*

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IFAC-PapersOnLine 2022年第6期55卷 637-642页

作者： Wenqing Zhao Hao Luo Qiang Liu Hongquan Ji Nan Sheng College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao 266590 P. R. China Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 P. R. China

Timely and accurate detection of incipient faults is critical to guarantee the normal operation of industrial processes. Nowadays, complex systems are usually equipped with a large number of sensors, which may be vulnerable to faults due to harsh environments. Statistical process monitoring is commonly used for fault detection purpose. Nevertheless, traditional fault detection methods are not sensitive enough to incipient faults, leading to the occurrence of many missed alarms. In this paper, the incipient fault detection task is achieved by monitoring the changes of sample singular values within a sliding window. Two incipient sensor fault types are considered, i.e. the sensor constant bias fault and sensor precision degradation fault. In addition, the rationale behind this strategy is also theoretically analyzed. Finally, a numerical example and the continuous stirred tank reactor process demonstrate the effectiveness of the proposed method.

关键词： Incipient sensor fault fault detection constant bias precision degradation singular value

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