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arXiv 2022年

作者： Gong, Hailong Li, Zirui Lu, Chao Du, Guodong Gong, Jianwei School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany Institute of Dynamic System and Control ETH Zurich Sonneggstrasse 3 Zurich8092 Switzerland

Trajectory prediction is a fundamental problem and challenge for autonomous vehicles. Early works mainly focused on designing complicated architectures for deep-learning-based prediction models in normal-illumination environments, which fail in dealing with low-light conditions. This paper proposes a novel approach for trajectory prediction in low-illumination scenarios by leveraging multi-stream information fusion, which flexibly integrates image, optical flow, and object trajectory information. The image channel employs Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) networks to extract temporal information from the camera. The optical flow channel is applied to capture the pattern of relative motion between adjacent camera frames and modelled by Spatial-Temporal Graph Convolutional Network (ST-GCN). The trajectory channel is used to recognize high-level interactions between vehicles. Finally, information from all the three channels is effectively fused in the prediction module to generate future trajectories of surrounding vehicles in low-illumination conditions. The proposed multi-channel graph convolutional approach is validated on HEV-I and newly generated Dark-HEV-I, egocentric vision datasets that primarily focus on urban intersection scenarios. The results demonstrate that our method outperforms the baselines, in standard and low-illumination scenarios. Additionally, our approach is generic and applicable to scenarios with different types of perception data. The source code of the proposed approach is available at https://***/TommyGong08/MSIF. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Optimal control of plate motion and camber in a reversing rolling mill ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11962-11967页

作者： C. Pietschnig A. Ettl U. Knechtelsdorfer A. Steinboeck A. Kugi Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry Automation and Control Institute (ACIN) TU Wien Vienna Austria Automation and Control Institute (ACIN) TU Wien Vienna Austria

In plate rolling, reversing roughing mills are commonly used as a first processing step after casting. They are typically equipped with edger rolls for width reduction. During a rolling pass, lateral asymmetries like temperature gradients or thickness inhomogeneities can cause two major problems. The plate may rotate in the rolling gap and thus move in lateral direction. Another problem is that the plate may leave the mill stand with a cambered shape. In the worst case, these problems entail collisions with the mill stand or other equipment along the roller table. It is an essential control task to avoid such problems. In general, the exit thickness profile and the motion of the plate are controlled by adjusting the roll gap height. The latter has also an influence on the contour shape but, for active control of the plate width and contour shape, the use of edger rolls is more common. This is especially true if the roll gap adjustment is self-retaining, meaning that it cannot be adjusted during a rolling pass. In this work, such a roughing mill and its edger rolls are considered. A mathematical model for the motion and the camber of the plate is derived. Based on this model, a linear quadratic regulator (LQR) for both the plate motion and the resulting camber is developed. It uses the lateral forces of the edgers as control inputs. In a cascaded control structure, these forces are regulated by a subordinate admittance controller. The developed control system is validated in simulation studies.

关键词： Heavy-plate hot rolling roughing mill with edger rolls steel industry model-based control camber control optimal control Riccati equation

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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The software solution of the overload capacity of a three-phase asynchronous motor

The software solution of the overload capacity of a three-ph...

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2020 International Conference on Information Science and Communications Technologies, ICISCT 2020

作者： Gazieva, Rano Aynakulov, Sharafidin Nigmatov, Aziz Rakhmankulova, Barna Khafizov, Otabek Ziyaeva, Sholpan Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department of Automation and Control of Thechnology Process in Production Tashkent Uzbekistan Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department of Information Technology Tashkent Uzbekistan

ISBN: (纸本)9781728199696

This article devoted to make characteristic of electrical engine on base of program platform PASCAL. In this paper consider and construct the mechanical characteristic of an induction motor, and calculate the main parameters of the electric motor. In the program compiled for this task, the voltage of loss of the starting ability with the rated torque on the shaft is calculated, the relationship between the slip and the motor torque is determined. © 2020 IEEE.

关键词： Induction motors

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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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Repetitive process based design of PD-type iterative learning control laws for batch processes with time-delays

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IFAC-PapersOnLine 2023年第2期56卷 1901-1906页

作者： Robert Maniarski Wojciech Paszke Eric Rogers Hongfeng Tao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 P. R. China

This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on the stability theory for repetitive processes, a class of 2D systems. A numerical example illustrates the new design and demonstrates that the design has advantages compared to the existing alternatives.

关键词： Iterative learning control repetitive processes batch processes linear matrix inequalities systems with delays

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Quantification of C02 emission reductions from energy efficiency actions and solar photovoltaic generation at the Federal institute of Education, Science, and Technology of Goiás

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2021年 19卷 45-50页

作者： Vidal, M.A. Faria, A.F. Stach, A.H.M. Netou, D.P. Pinto, L.S. Pinlio, A.G. Domingues, E.G. Master’s Program in Technology of Sustainable Process Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil Electiical Engineering Control and Automation Engineering Program Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil NeXT - Nucleus of Experimental and Technological Studies Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil

In 2016. die Brazilian Agency of Electric Energy-launched a public call for research and development projects to reduce barriers to implementing energy efficiency projects and own electricity generation. One objective of this public call was to encourage pilot projects at the Brazilian public higher education institutions. In this context, the Federal institute of Education. Science, and Technology of Goias has approved the research project, entided "Energy efficiency and mini generation ai the Federal institute of Education. Science, and Technology - IFG." The project consists of implementing energy efficiency actions and installing photovoltaic distributed generation (mini generation) in twelve of the fourteen IFG campuses. The main objective of this work is to quantify the C0;emission reductions from die project through the methodologies used by the United Nations Framework Climate Change Convention for projects under the Clean Development Mechanism The emission reductions over the project lifetime was estimated at approximately 37,400 iC’O- equivalent, evidencing the environmental benefit to be provided by the referred project. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Improved LMI-based conditions for designing of PD-type ILC laws for linear batch processes over two-dimensional setting

Improved LMI-based conditions for designing of PD-type ILC l...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Shoulin Hao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9781665476881

This paper considers the problem of designing of iterative learning control (ILC) laws for linear batch processes. Unlike the majority of existing results about ILC law design for linear batch processes over repetitive/two-dimensional setting where Lyapunov theory is applied, this study is focused on formulating the ILC law design procedures by transforming it into an equivalent problem of (structural) stability analysis for a linear Roesser model for two-dimensional (2D) systems. Then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, suitable PD-type ILC laws are derived by the application of the linear matrix inequality (LMI) approach. Finally, a numerical example is given to show the validity of the proposed design procedure and some advantages are emphasized when compared to the existing alternatives.

关键词： State feedback Uncertainty Two dimensional displays Batch production systems Stability analysis Structural engineering Numerical models

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Deep Graph Network for process Soft Sensor Development

Deep Graph Network for Process Soft Sensor Development

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Mingwei Jia Yun Dai Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665402460

In the (bio)chemical processes, traditional hardware sensors are difficult to directly measure the quality of critical products due to their time-varying, non-linear, and dynamic characteristics. This makes process soft sensor modeling methods important. Since the process variables can be regarded as natural graph data, this work introduces graphs in the soft sensor modeling area. A soft sensor model based on the graph neural network (GNN) is proposed. The model can learn the topological structure of graph data between each unit variable. Moreover, it characterizes variable relationships from the spatial and temporal dimensions to the output prediction by introducing the spatial-temporal convolutional layer. The effectiveness and advantages of the GNN-based soft sensor model are verified using a simulated fermentation process.

关键词： Soft sensors Computational modeling Predictive models Sensor phenomena and characterization Data models Product design Hardware

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