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Electric Vehicle Routing Problem with Capacitated Charging Stations and Dynamic Electricity Prices

IEEE Internet of Things Journal 2025年

作者： He, Zhiye Tian, Yuning Miao, Haoyu Yao, Canqi Yang, Zaiyue Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities School of System Design and Intelligent Manufacturing Shenzhen518055 China The Hong Kong Polytechnic University Department of Logistics and Maritime Studies Hong Kong Hong Kong

With rapid electrification of transportation systems in recent years, the electric vehicle routing problem (EVRP) has received increasing attention. In order to serve a set of valuable transportation tasks using a fleet of electric vehicles, EVRP intends to jointly determine their routes and the charging schedules, with the aim of optimizing service reward and charging cost under state of charge (SOC) constraints of battery. EVRP is a difficult nonlinear integer program, and due to its intrinsic NP-hardness early EVRP studies often oversimplify the charging process and assume unlimited capacity of charging stations and constant electricity price. The contribution of this paper lies in twofold. First, to better characterize the practical charging process, this paper introduces a new graph model that can take the charging station capacity and dynamic electricity price into full consideration. Second, we design a series of transformation techniques, including reformulation SOC dynamics, Lagrangian relaxation, exact linear programming relaxation, etc, which eventually convert the original EVRP into an approximate linear program. Extensive simulations across 14 instances demonstrate that, the proposed approach is capable of solving large-scale instances over 500 nodes, and is 100 times faster than traditional analytic methods while achieving very close optimality. While comparing with the popular heuristic method ALNS (Adaptive Large Neighborhood Search), our method is several times faster and obtains much better optimality. © 2014 IEEE.

关键词： Charging stations

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Distributed Time-Varying Optimization with Equality Constraints

Distributed Time-Varying Optimization with Equality Constrai...

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International Conference on control and Automation (ICCA)

作者： Zheng Yang Ji Ma Xiang Xu Department of Automation Xiamen University Xiamen Fujian China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In this paper, we consider the distributed time-varying optimization problem with coupled equality constraints over a connected undirected network. To address this issue, we design a novel distributed constraint optimization algorithm, and establish its ISS stability with external disturbances and tracking errors as the input and state, respectively. Moreover, the obtained result includes distributed constrained optimization with static objective functions as a special case. In comparison to existing relevant works, the proposed algorithm demonstrates exponential convergence for cases involving static objective functions. Finally, the theoretical results are validated via a numerical example.

关键词： Constraint optimization Automation Linear programming Trajectory Optimization Numerical stability Convergence

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High-order Fully Actuated Approach for Output Position control of Two Mass systems based on Extended State Observer

High-order Fully Actuated Approach for Output Position Contr...

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Fully Actuated System theory and Applications (CFASTA), Conference on

作者： Ping Li Guang-Ren Duan Bi Zhang Yuzhong Wang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper proposes a high-order fully actuated (HOFA) control approach for the output position control of two-mass systems (TMSs) based on extended state observer (ESO). TMSs belongs to a class of underactuated mechanical systems, which can be converted into an HOFA model using variable elimination. Based on the feature of HOFA model, it is extremely simple to solve the position control law such that a desired tracking error system can be achieved. To handle the control issues of high-order derivatives and unknown disturbances, a linear ESO is employed for state estimation and active disturbance rejection control, where the lumped disturbance is treated as an extended system state. The theoretical stability of control system is analyzed with the help of sliding manifold. Comparative simulations are performed to demonstrate the effectiveness of the proposed approach.

关键词： Robust control Uncertain systems Simulation Position control Observers control systems Stability analysis

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

GeoReF: Geometric Alignment Across Shape Variation for Categ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Linfang Zheng Tze Ho Elden Tse Chen Wang Yinghan Sun Hua Chen Ales Leonardis Wei Zhang Hyung Jin Chang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China School of Computer Science. University of Birmigsham UK Department of Computer Science the University of Hong Kong China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress to-wards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrep-ancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach in-tegrates an HS-Iayer and learnable affine transformations, which aims to enhance the extraction and alignment of Geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges di-verse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive ex-periments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. 1 1 Project page: https://***/***

关键词： Point cloud compression Computer vision Shape Soft sensors Pose estimation Computer architecture Predictive models

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

arXiv

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arXiv 2024年

作者： Zheng, Linfang Tse, Tze Ho Elden Wang, Chen Sun, Yinghan Chen, Hua Leonardis, Aleš Zhang, Wei Chang, Hyung Jin Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Computer Science The University of Hong Kong Hong Kong School of Computer Science University of Birmingham United Kingdom

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. © 2024, CC BY-NC-SA.

关键词： Metadata

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Modeling, simulation and experiment of an origami robot with a coupled bar-hinge mechanism

Modeling, simulation and experiment of an origami robot with...

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Fully Actuated System theory and Applications (CFASTA), Conference on

作者： Junpeng Mai Hanzhao Shao Shaojie Liu Yuanzhuo Zhang Liangming Chen Huijuan Feng School of System Design and Intelligent Manufacturing and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen P. R. China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen P. R. China Department of Mechnical and Energy Engineering Southern University of Science and Technology Shenzhen P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper aims to analyze the motion behavior of an origami robot with a coupled bar-hinge mechanism. By disassembling the planar six-hinge mechanism into two planar four-hinge mechanisms with coupled motion, this paper derives the kinematics of the planar six-hinge mechanism based on the algebraic differential kinematics of the planar four-hinge motions. The rotational angle change and the moving step length of the origami robot are further calculated. Finally, the theoretical motion model is verified with the motion simulation data of the planar six-hinge mechanism origami robot in SolidWorks and the experimental data of the physical model. This verification proves the feasibility of the theoretical analysis, simulation model, and physical model.

关键词： Solid modeling Analytical models Prototypes Kinematics Three-dimensional printing Mathematical models Data models

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Distributed Finite-Time Cooperative Localization for Three-Dimensional Sensor Networks

arXiv

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arXiv 2023年

作者： Wu, Jinze Zino, Lorenzo Lin, Zhiyun Rizzo, Alessandro The Department of Electronics and Telecommunications Politecnico di Torino Torino Italy Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China The Institute for Invention Innovation and Entrepreneurship New York University Tandon School of Engineering BrooklynNY United States

This paper addresses the distributed localization problem for a network of sensors placed in a three-dimensional space, in which sensors are able to perform range measurements, i.e., measure the relative distance between them, and exchange information on a network structure. First, we derive a necessary and sufficient condition for node localizability using barycentric coordinates. Then, building on this theoretical result, we design a distributed localizability verification algorithm, in which we propose and employ a novel distributed finite-time algorithm for sum consensus. Finally, we develop a distributed localization algorithm based on conjugate gradient method, and we derive a theoretical guarantee on its performance, which ensures finite-time convergence to the exact position for all localizable nodes. The efficiency of our algorithm compared to the existing ones from the state-of-the-art literature is further demonstrated through numerical simulations. © 2023, CC BY.

关键词： Conjugate gradient method

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Single trial ERP amplitudes reveal the time course of acquiring representations of novel faces in individual participants

arXiv

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arXiv 2020年

作者： Sommer, Werner Stapor, Katarzyna Kończak, Grzegorz Kotowski, Krzysztof Fabian, Piotr Ochab, Jeremi Bereś, Anna Ślusarczyk, Grażyna Humboldt-University at Berlin Department of Psychology Germany Silesian University of Technology Institute of Computer Science Gliwice Poland University of Economics in Katowice Department of Statistics Econometrics and Mathematics Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Algorithmics and Software Gliwice Poland Jagiellonian University Department of Theory of Complex Systems Krakow Poland Jagiellonian University Department of Cognitive Neuroscience and Neuroergonomics Krakow Poland Jagiellonian University Department of Design and Computer Graphics Krakow Poland Institut für Psychologie Humboldt-Universität zu Berlin Rudower Chaussee 18 Berlin12489 Germany Faculty of Automatic Control Electronics and Computer Science Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

The neural correlates of face individuation – the acquisition of memory representations for novel faces – have been studied only in coarse detail and disregarding individual differences between learners. In their seminal study, (Tanaka, Curran, Porterfield, & Collins, 2006) required the identification of a particular novel face across 70 trials and found that the N250 component in the ERP became more negative from the first to the second half of the experiment, where it reached a similar amplitude as a well-known face. We were unable to directly replicate this finding in our study when we used the original spilt of trials. However, when we applied a different split of trials we observed very similar changes in N250 amplitude. Then, we developed and applied a new two-step explorative-confirmative non-parametric method based on permutation testing to determine the time course of face individuation in individual participants based on single trial N250 amplitudes. We show that the assumption of a steep initial increase of N250 amplitude across multiple presentations of the target face, followed by a plateau, yields plausible results in fitting linear trends for most participants. The transition point from initial acquisition to the plateau phase differed strongly between participants and tended to be earlier when performance in target face recognition was better. Hence, face individuation may be accounted for by a biphasic process of early, fast acquisition, followed by a slower, asymptotic consolidation or maintenance phase. The current approach might be fruitfully applied to further investigations into face individuation and their neural correlates. © 2020, CC BY-NC-ND.

关键词： Face recognition

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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design of the structural optimization for the upper limb rehabilitation robot

Design of the structural optimization for the upper limb reh...

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IEEE International Conference on Mechatronics and Automation

作者： Shuxiang Guo Jiange Gao Jian Guo Weijie Zhang Yuye Hu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Biomedical Robot Laboratory Tianjin University of Technology Tianjin China Intelligent Mechanical Systems Engineering Department Kagawa University Takamatsu Japan Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin China

ISBN: (纸本)9781509023974

The exoskeleton robotic rehabilitation training system is a branch of the field of robotics, it is the external mechanical system that is consistent with the similar joints of the human body, and attached to the human body. With the development of exoskeleton rehabilitation robotic system, the biomechanics influence of device should be considered in rehabilitation training. This paper proposes a novel upper limb exoskeleton rehabilitation device based on biomechanics, which can be used in rehabilitation of upper limb for hemi paralysis patients. This system aims at helping hemi paralysis patients recover motor function of upper limb; in addition it is suitable for variety of patients. This system is portable and wearable, which consists of exoskeleton device, hepatic device (PHANTOM Premium), motor controllers and work station (computer). This paper proposes a structural optimization scheme in order to solve some problems of bowl's structure of the rehabilitation robot. The Pro/ENGINEER Wildfire (Pro/E) software is applied to get a three-dimensional modelling, kinematics simulation and dynamic analysis of the rehabilitation robot. From the analysis, the robot end motion simulation curves were obtained to verify the optimization structure reasonable and better motion characteristics. In the future, this structure will have a wide application prospect in the rehabilitation therapy field.

关键词： Robots Exoskeletons Wrist Jacobian matrices Gears Optimization Acceleration

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