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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control Distributed model predictive control Emergency braking configuration

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ReFlow6D: Refraction-Guided Transparent Object 6D Pose Estimation via Intermediate Representation Learning

arXiv

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arXiv 2024年

作者： Gupta, Hrishikesh Thalhammer, Stefan Weibel, Jean-Baptiste Haberl, Alexander Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department UAS Technikum Vienna TU Wien Austria

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estimation that harnesses the refractive-intermediate representation. Unlike conventional approaches, our method leverages a feature space impervious to changes in RGB image space and independent of depth information. Drawing inspiration from image matting, we model the deformation of the light path through transparent objects, yielding a unique object-specific intermediate representation guided by light refraction that is independent of the environment in which objects are observed. By integrating these intermediate features into the pose estimation network, we show that ReFlow6D achieves precise 6D pose estimation of transparent objects, using only RGB images as input. Our method further introduces a novel transparent object compositing loss, fostering the generation of superior refractive-intermediate features. Empirical evaluations show that our approach significantly outperforms state-of-the-art methods on TOD and Trans32K-6D datasets. Robot grasping experiments further demonstrate that ReFlow6D’s pose estimation accuracy effectively translates to real-world robotics task. The source code is available at: https://***/StoicGilgamesh/ReFlow6D and https://***/StoicGilgamesh/matting *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Light refraction

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Consistency Evaluation of IoT Smart Meter’s Metering Accuracy considering Ambient Temperature and Load Current

Consistency Evaluation of IoT Smart Meter’s Metering Accura...

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system Reliability and Safety engineering (SRSE), International Conference on

作者： Weiming Liu Cen Chen Zhifei Wang Xiaosheng Zhang Guofu Zhai Wei Zheng Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China

The energy meter is a widely used digital-analog electronic system. However, metering accuracy is inevitably influenced by factors such as ambient temperature, load current, and component tolerance. This paper introduces a method utilizing digital twin models to analyze the consistency of metering accuracy in a new single-phase smart meter prototype. An electro-thermal coupling model incorporating a temperature compensation algorithm is established, and the metering accuracy is solved using a surrogate model. By employing the Monte Carlo method, a batch of product samples is generated to determine the impact of varying ambient temperature and load current conditions on the metering accuracy’s consistency within the maximum operating range. The results of this study provides a valuable foundation for robust design and reliability optimization of the energy meter.

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Developing a Window control Algorithm Based on Reinforcement Learning for Indoor PM2.5 Mitigation 5th

Developing a Window Control Algorithm Based on Reinforcement...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： An, Yuting Xia, Tongling You, Ruoyu Lai, Dayi Liu, Junjie Chen, Chun Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China Department of Building Services Engineering The Hong Kong Polytechnic University China School of Design Shanghai Jiao Tong University Shanghai China Tianjin Key Lab of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9789811998218

Smart control of window is a means of effectively reducing concentrations of indoor PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) in naturally ventilated residential buildings without air cleaning devices. This study aimed to develop a reinforcement learning (RL) approach to automatically control window behavior in real time for mitigation of indoor PM2.5 pollution. The method trains the window controller using deep Q-network (DQN) in a specific apartment for a month. The trained controller can be employed to control window behavior to mitigate indoor PM2.5 in that apartment. The input data for the controller are the real-time indoor and outdoor PM2.5 concentrations with 1-min resolution. Simulation was conducted in a real apartment in Tianjin. The results show that, the RL algorithm reduced the average indoor PM2.5 concentration by 9.11% when compared with the I/O ratio algorithm and by 7.40% when compared with real window behavior. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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High-Entropy Oxide of (BiZrMoWCeLa)O2 as a Novel Catalyst for Vanadium Redox Flow Batteries

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ACS Applied Materials and Interfaces 2024年第8期16卷 10019-10032页

作者： Demeku, Aknachew Mebreku Kabtamu, Daniel Manaye Chen, Guan-Cheng Ou, Yun-Ting Huang, Zih-Jhong Hsu, Ning-Yih Ku, Hung-Hsien Wang, Yao-Ming Wang, Chen-Hao Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Chemistry Debre Berhan University Debre Berhan000000 Ethiopia Chemistry Division National Atomic Research Institute Taoyuan325207 Taiwan Maritime Innovation & Industry Promotion Department Metal Industries Research & Development Centre Kaohsiung811160 Taiwan Research Center National Cheng Kung University Tainan701401 Taiwan Center of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan

In this study, new fluorite high-entropy oxide (HEO), (BiZrMoWCeLa)O2, nanoparticles were produced using a surfactant-assisted hydrothermal technique followed by calcination and were used as novel catalytic materials for vanadium redox flow batteries (VRFBs). The HEO calcined at 750 °C (HEO-750) demonstrates superior electrocatalytic activity toward V3+/V2+ and VO2+/VO2+ redox couples compared to those of cells assembled with other samples. The charge-discharge tests further confirm that VRFBs using the HEO-750 catalyst demonstrate excellent Coulombic efficiency, voltage efficiency, and energy efficiency of 97.22, 87.47, and 85.04% at a current density of 80 mA cm-2 and 98.10, 74.76, and 73.34% at a higher current density of 160 mA cm-2, respectively. Moreover, with 500 charge-discharge cycles, there is no discernible degradation. These results are attributed to the calcination heat treatment, which induces the formation of a new single-phase fluorite structure, which facilitates the redox reactions of the vanadium redox couples. Furthermore, a high surface area, wettability, and plenty of oxygen vacancies can give more surface electroactive sites, improving the electrochemical performance, the charge transfer of the redox processes, and the stability of the VRFBs’ electrode. This is the first report on the development of fluorite structure HEO nanoparticles in VRFBs, and it opens the door to further research into other HEOs. © 2024 The Authors. Published by American Chemical Society.

关键词： Entropy

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ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

ZS6D: Zero-shot 6D Object Pose Estimation using Vision Trans...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Philipp Ausserlechner David Haberger Stefan Thalhammer Jean-Baptiste Weibel Markus Vincze Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department University of Applied Sciences Technikum Vienna Austria

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC- based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to MegaPose, we improve the Average Recall on all three datasets and compared to OSOP we improve on two datasets. The code is available at https://***/PhilippAuss/ZS6D.

关键词： Training Computer vision Visualization Codes Computational modeling Pose estimation Transformers

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Light-Weight Low-Cost Drive control Modules Design and Mass Production Research in SAR Satellites

Light-Weight Low-Cost Drive Control Modules Design and Mass ...

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China International SAR Symposium (CISS)

作者： Pengfei Liu Xiaoyan Ma Jun Wang Weixin Qian Zhengkang Feng Department of Electrical Control Aerospace System Engineering Shanghai Shanghai China Department of Radar System Shanghai Aerospace Electronic Technology Institute Shanghai China

In order to adapt to the development trend of light-weight and low-cost SAR (Synthetic Aperture Radar) satellites, products of drive control modules, especially drive control modules of SAR antenna deployment mechanism and solar array, are required to be light-weight and low-cost. At the level of product design, the mechanical structure of light-weight drive control module and low-cost hardware circuit design are given. The design scheme mainly involves the electromechanical integration interface of the drive control module and the drive mechanism, and the protection and reinforcement of the industrial components of the hardware circuit. At the level of product production, firstly, the product is designed according to modularization. Secondly, a series of standard systems from production to test are established. Finally, automatic production lines and automatic testing equipment are used for mass production and testing of products. The above design and production optimization measures reduce the weight and cost of drive control module products. The light-weight and low-cost drive control modules is conducive to carrying more payloads on SAR satellites and networking between satellites.

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Development of Measurement Device Applied to Radiometric systems

Development of Measurement Device Applied to Radiometric Sys...

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Smart Power & Internet Energy systems (SPIES), International Conference on

作者： Thair I. A. H. Mustafa Sergio H. L. Cabral Hugo A. D. Almaguer Luiz H. Meyer Ciro Pitz Cleiton Gili Marcel V. T. Fischer Department of Electrical Engineering University of Blumenau Blumenau Brazil Department of Control Automation and Computation Engineering Federal University of Santa Catarina Blumenau Brazil Gili Energy Electronics Presidente Nereu Brazil Department of System Operation CPFL Transmission Porto Alegre Brazil

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This work presents the main stages of the development of a new radiometric system, based on a new measurement device for replacing the digital oscilloscope. Once triggering principles of the commercially available digital oscilloscopes are not adequate for totally guaranteeing the recording of the irradiated electromagnetic field from the circuit breaker chambers, the main topic of this paper. This achievement represents a new generation of radiometric measurement systems applied to the assessment of power circuit breakers. Because of the non-invasive nature of this kind of assessment radiometric measurements are likely to be widely deployed in power systems because it eliminates the need for equipment under analysis in stopping its operation. Moreover, results obtained from the deployment of the new system on power substations are shown.

关键词： Power measurement Measurement units Substations Circuit breakers Oscilloscopes Radiometry Hardware Software Software measurement Transient analysis

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Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs 43

Quality Prediction of Injection Molding Process Using CNN an...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Yibo Li, Dewei Zhou, Yuanqiang Gao, Furong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai201100 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Guangzhou Hkust Fok Ying Tung Research Institute Department of Chemical and Biological Engineering Guangzhou511458 China

ISBN: (纸本)9789887581581

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks (CNN) and Parallel-Cascade Long Short-Term Memory (PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding, overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Convolutional neural networks

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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