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SSRN 2024年

作者： Li, Leiji Xiao, Fei Li, Zhu He, Shiyu Liu, Yang Cai, Xiaorong Hou, Ruihang Zhou, Ying Zuo, Shungui Zhang, Chongfeng Gao, Jinzhong Shen, Xiaopeng Zhu, Tianchi Gu, Yijia Jin, Xuejun State Key Lab of Metal Matrix Composite School of Materials Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China National Key Laboratory of Aerospace Mechanism Aerospace System Engineering Shanghai 3805 Jin Du Road Shanghai201108 China Department of Materials Science and Engineering Missouri University of Science and Technology RollaMO65409 United States National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy School of Materials Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Department of Materials Science and Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Osaka Suita565-0871 Japan Shanghai Key Laboratory of Materials Laser Processing and Modification China

Fusion-based metal additive manufacturing (AM) is a technology that builds objects layer by layer, with rapid solidification referring to the quick transition of material from liquid to solid. During this process, solute trapping, a nonequilibrium effect characterized by velocity-dependent partition coefficient, significantly influences microstructure kinetics, morphology, and phase formation. This study utilizes the latest quantitative phase field (PF) model to systematically investigate the solute trapping, solute drag, and their effects on pattern formation during rapid solidification at velocities relevant to AM, in both one and two dimensions. Under different temperature gradients and pulling velocities, we observed that the growth mode transits from planar to cellular to dendritic, then back to cellular, and finally to planar, which is consistent with classic solidification theory. A solidification microstructure selection map is constructed based on PF simulations. Furthermore, we compared primary dendritic/cell spacing calculated by the PF model with theoretical models. The simulated morphologies and arm spacing showed good agreement with experimental observations for Al-4Si and Ti-20Nb alloys during rapid solidification. These results demonstrate the potential of the PF model for predicting and controlling microstructure formation under rapid solidification conditions in the melt pool of AM processes. © 2024, The Authors. All rights reserved.

关键词： Morphology

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Group-wise inhibition based feature regularization for robust classification

arXiv

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Preliminary results for the multi-robot, multi-partner, multi-mission, planetary exploration analogue campaign on mount etna

Preliminary results for the multi-robot, multi-partner, mult...

引用

IAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Wedler, Armin Möller, Marcus G Schuster, Martin J Durner, Maximilian Brunner, Sebastian G Lehner, Peter Lehner, Hannah Dömel, Andreas Vayugundla, Mallikarjuna Steidle, Florian Sakagami, Ryo Meyer, Lukas Smisek, Michal Störzl, Wolfgang Schmitz, Nicole Vodermayer, Bernhard Prince, Andre F Staudinger, Emanuel Hellerer, Matthias Lichtenheldt, Roy Dietz, Enrico Braun, Christian Rebele, Bernhard Boerdijk, Wout Giubilato, Riccardo Reill, Josef Kuhne, Moritz Lee, Jongseok Vilacampa, Alejandro F Bargen, Info V Schröder, Susanne Frohmann, Sven Seel, Fabian Triebel, Rudolph Lii, Neal Y Bischoff, Esther Kille, Sean Wormnes, Kjetil Pereira, Aaron Carey, William Rossi, Angelo P Thomsen, Laurenz Graber, Thorsten Kruger, Thomas Kyr, Peter Börner, Anko Bussmann, Kristin Paar, Gerhard Bauer, Arnold Völk, Stefan Kimpe, Andreas Rauer, Heike Höbers, Heinz-Wilhelm Bals, Johann Hohmann, Sören Asfour, Tamim Foing, Bernhard Albu-Schäffer, Alin Olimpiu Institute of Robotics and Mechatronics Mönchener Str. 20 Webling82234 Germany Institute of Optical Sensor Systems Rutherfordstr. 2 Berlin12489 Germany Institute of System Dynamics and Control Mönchener Str. 20 Weßling82234 Germany Institute of Planetary Research Rutherfordstr. 2 Berlin12489 Germany Mobile Rocket Base MönchenerStr. 20Webl 82234 Germany Joanneum Research Steyrergasse 17 GrazA-8010 Austria Solenix Engineering GmbH Spreestr. 3 Darmstadt64295 Germany ESTEC Noordwijk Netherlands University of Leiden Netherlands Institute of Control Systems Kaiserstr. 12 Karlsruhe76131 Germany Kaiserstr. 12 Karlsruhe76131 Germany Jacobs University Bremen Planetary Science Campus Ring 1 Bremen28759 Germany

ISBN: (纸本)9781713842965

This paper was initially intended to report on the outcome of the twice postponed demonstration mission of the ARCHES project. Due to the global COVID pandemic, it has been postponed from 2020, then 2021, to 2022. Nevertheless, the development of our concepts and integration has progressed rapidly, and some of the preliminary results are worthwhile to share with the community to drive the dialog on robotics planetary exploration strategies. This paper includes an overview of the planned 4-week campaign, as well as the vision and relevance of the mission towards the planned official space missions. Furthermore, the cooperative aspect of the robotic teams, the scientific motivation, the sub task achievements are summarised. © 2021 International Astronautical Federation, IAF. All rights reserved.

关键词： robotics

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Analyzing the Effect of Undermining on Suture Forces During Simulated Skin Flap Surgeries with a Three-Dimensional Finite Element Method

SSRN

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SSRN 2024年

作者： Guo, Wenzhangzhi Lau, Allison Tsz Kwan Davies, Joel C. Forte, Vito Grinspun, Eitan Kahrs, Lueder Alexander Medical Computer Vision and Robotics Lab University of Toronto TorontoON Canada Department of Computer Science University of Toronto 40 St George St TorontoM5S 2E4 Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention The Hospital for Sick Children 170 Elizabeth St TorontoM5G 1E8 Canada Department of Otolaryngology - Head and Neck Surgery Sinai Health System University of Toronto 600 University Ave TorontoM5G 1X5 Canada Department of Otolaryngology - Head and Neck Surgery University of Toronto 600 University Ave TorontoM5G 1X5 Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga 3359 Mississauga Rd MississaugaL5L 1C6 Canada Institute of Biomedical Engineering University of Toronto 164 College St TorontoM5S 3G9 Canada

Skin flaps are common procedures used by surgeons to cover an excised area during the reconstruction of a defect. It is often a challenging task for a surgeon to come up with the most optimal design for a patient. In this paper, we set up a simulation system based on the finite element method for one of the most common flap types - the rhomboid flap. Instead of using the standard 2D planner patch, we constructed a 3D patch with multiple layers. This allowed us to investigate the impact of different undermining areas and depths. We compared the suture forces for each case and identified vertices with the largest suture force. The shape of the final suture line is also visualized for each case, which is an important clue when deciding on the most optimal skin flap orientation according to medical textbooks. We found that under the optimal undermining setup, the maximum suture force is around 0.7N for top of the undermined layer and 1.0N for bottom of the undermined layer. When measuring difference in final suture line shape, the maximum normalized Hausdorff distance is 0.099, which suggests that different undermining region can have significant impact on the shape of the suture line, especially in the tail region. After analyzing the suture force plots, we provided recommendations on the most optimal undermining region for rhomboid flaps. © 2024, The Authors. All rights reserved.

关键词： Finite element method

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Improved dynamic window approach with ellipse equations for autonomous navigation of unmanned surface vehicle

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Journal of institute of Control, robotics and systems 2020年第8期26卷 624-629页

作者： Kim, Hyogon Yun, Sung-Jo Choi, Young-Ho Ryu, Jae-Kwan Won, Byong-Jae Suh, Jin-Ho Korea Institute of Robotics and Technology Convergence Korea Republic of Unmanned/Intelligent Robotic Systems R&D Lab. LIG Nex1 Korea Republic of Department. of Mechanical System Engineering Pukyoung National University Korea Republic of

Autonomous navigation technology for Unmanned Surface Vehicles (USVs) has seen many recent advancements. In order for a USV to operate autonomously, the path to move from its current location to its destination must be planned, and local path planning must be applied for tracking the path and avoiding obstacles. The Dynamic Window Approach (DWA) is a well-known navigation algorithm used for local path planning. The DWA algorithm derives the linear velocity and angular velocity by evaluating the object function defined as destination direction, velocity, and distance from the obstacle. However, because DWA does not consider the velocity and direction of obstacles, USVs may be in danger when avoiding high-speed obstacles. In this paper, we propose an improved DWA algorithm that considers the velocity and direction of obstacles. Whereas the existing DWA algorithm defines the obstacle recognition area as a circle, the proposed DWA algorithm defines the obstacle recognition area as an ellipse and reflects the speed of the obstacle in the ellipse. In this study, we implemented a simulation to compare the existing DWA algorithm with the improved DWA algorithm proposed here. The results confirm that the proposed DWA algorithm can avoid dynamic obstacles more safely than the existing DWA algorithm. © ICROS 2020.

关键词： Unmanned surface vehicles

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Localization and Surface Characterization by Zhurong Mars Rover at Utopia Planitia

Research Square

引用

Research Square 2021年

作者： Ding, Liang Zhou, Ruyi Yu, Tianyi Gao, Haibo Yang, Huaiguang Li, Jian Yuan, Ye Liu, Chuankai Wang, Jia Zhao, Yuyan Wang, Zhengyin Wang, Xiyu Bao, Gang Deng, Zongquan Huang, Lan Li, Nan Cui, Xiaofeng He, Ximing Jia, Yang Yuan, Baofeng Liu, Guangjun Zhang, Hui Zhao, Rui Zhang, Zuoyu Cheng, Ziqing Wu, Fan Xu, Qian Lu, Hao Richter, Lutz Liu, Zhen Niu, Fuliang Qi, Huanan Li, Shu Feng, Wenhao Yang, Chaojie Chen, Baichao Dang, Zhaolong Zhang, Mingming Li, Lichun Wang, Xiaoxue Huang, Zhao Zhang, Jitao Xing, Hongjun Wang, Guanyu Niu, Lizhou Xu, Peng Wan, Wenhui Di, Kaichang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Beijing Aerospace Control Center Beijing100094 China Center for Lunar and Planetary Sciences Institute of Geochemistry Chinese Academy of Science Guiyang550081 China CAS Center for Excellence in Comparative Planetology Hefei230026 China University of Chinese Academy of Sciences Beijing100049 China Beijing Institute of Spacecraft System Engineering Beijing100094 China Department of Aerospace Engineering Ryerson University TorontoONM5B 2K3 Canada Hauptstrasse 1e Eching Germany State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

China’s first Mars rover, Zhurong, has successfully touched down on the southern Utopia Planitia of Mars at 109.925° E, 25.066° N, and since performed cooperative multiscale investigations with the Tianwen-1 orbiter. Here we present primary localization and surface characterization results based on complementary data of the first 60 sols. The Zhurong rover has traversed 450.9 m southwards over a flat surface with mild wheel slippage (less than 0.2 in slip ratio). The encountered crescent-shaped sand dune indicates a NE-SW local wind direction, consistent with larger-range remote-sensing observations. Soil parameter analysis based on terramechanics indicates that the topsoil has high bearing strength and cohesion, and its equivalent stiffness and internal friction angle are ~1390-5872 kPa∙m-n and ~21°-34° respectively. Rocks observed strewn with dense pits, or showing layered and flaky structures, are presumed to be involved in physical weathering like severe wind erosion and potential chemical weathering processes. These preliminary observations suggest great potential of in-situ investigations by the scientific payload suite of the Zhurong rover in obtaining new clues of the region’s aeolian and aqueous history. Cooperative investigations using the related payloads on both the rover and the obiter could peek into the habitability evolution of the northern lowlands on Mars. © 2021, CC BY.

关键词： Sols

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On 3D flow pattern behind a wall-mounted circular cylinder of finite-length

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AIP Conference Proceedings 2021年第1期2323卷

作者： Václav Uruba Pavel Procházka 1Institute of Thermomechanics of the Czech Academy of Sciences Dolejškova 5 182 00 Praha 8 Czech Republic 2Faculty of Mechanical Engineering Department of Power System Engineering Universitní 8 Plzeň Czech Republic

The wake behind a wall-mounted finite-length circular cylinder of the aspect ratio 2 stuck out of a wall was studied experimentally using stereo PIV technique. The cylinder was mounted normal to a ground plane and it was subjected to a cross-flow with thin boundary layer developed on the wall, the Reynolds number based on inflow velocity and cylinder diameter was 9.7 thousands. The time-averaged velocity and streamwise vorticity fields showed the development of the mean flow structure consisting of the tip vortex pair, the extent of the near-wake recirculation zone with back-flow, the downwash phenomenon and base vortex structures close to the wall.

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A numerical solution to the two-dimensional nonlinear degenerate heat conduction equation with a source

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AIP Conference Proceedings 2020年第1期2315卷

作者： A. L. Kazakov L. F. Spevak O. A. Nefedova 1Institute of Engineering Science Ural Branch of the Russian Academy of Sciences 34 Komsomolskaya St. Ekaterinburg 620049 Russia 2Matrosov Institute for System Dynamics and Control Theory Siberian Branch of the Russian Academy of Sciences 134 lermontov St. Irkutsk 664033 Russia

The paper develops an algorithm for solving the two-dimensional nonlinear degenerate parabolic heat conduction equation with a source depending on required function, with a specified law of heat wave front motion. The algorithm based on the boundary element method is implemented in the form of a program. To verify it, we use exact solutions the construction of which is reducible to solving a Cauchy problem for ordinary differential equations with a singularity before the highest derivative. The solutions to the Cauchy problem are constructed in the form of power series with recurrently computed coefficients (with a proof of the statement providing its convergence) and by the boundary element method.

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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