检索结果-内蒙古大学图书馆

CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020)

作者： Z. Chen J. Zhou S. Wang P. Zhang C. Zhang S. Huang Beihang University School of Automation Science and Electrical Engineering Beijing China Case Western Reseerve University The Department of Mechanical and Aerospace Engineering Cleveland U.S.A. Beihang University Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beijing China China Academy of Space Technology Beijing Institute of Spacecraft Environment Engineering Beijing China

In this paper, a noval type of data glove is designed based on the principle of parallelogram, which aims to detect gestures in real time. In addition to the flexion and extension angles, this glove can detect the adduction and abduction angles of each finger. Because the overall structure of this data glove is optimized according to the linkage mechanism among joints of human hand. The data glove has the advantages of simple structure, convenient wearing, light weight and good comfort. The glove is composed of four parts: framework, bearings, connecting rods and potentiometers. The framework and connecting rods are respectively made of photosensitive resin and laser cut carbon fiber board. Potentiometers are installed on the bearings to detect the angle of rotation. In this paper, kinematic modeling of human hand is carried out. When knowing the rotation angles of each joint of human hand, the posture of the hand can be calculated by the model. Finally, the data glove is tested on the wearer's hand. Angles of each joint measured by the data glove is compared with the actual angles to verify the accuracy and feasibility of this design.

关键词： resins data gloves gesture recognition laser beam cutting biomechanics potentiometers rods (structures) kinematics

来源：评论

学校读者我要写书评

暂无评论

A study on modelling and analysis of 6-DOF industrial robot using ADAMS

引用

AIP Conference Proceedings 2023年第1期2680卷

作者： Amit Talli Ayub Ahmed Janvekar Ahmed A. Hussien Isam Qasem Pramodkumar S. Kataraki Mazlan Mohamed Hugeng Hugeng Automation and Robotics KLE Technological University Hubli – 580031 INDIA School of Mechanical Engineering VIT University Chennai Tamil Nadu - 600127 INDIA Department of Mechanical Engineering Al-Huson University College Al-Balqa Applied University Al-Huson Irbid JORDAN School of Mechanical Engineering Reva University Rukmini Knowledge Park Kattigenahalli Yelahanka Bengaluru – 560064 Karnataka INDIA Advanced Material Research Cluster Faculty of Bioengineering and Technology Jeli Campus Universiti Malaysia Kelantan Jeli 17600 MALAYSIA Electrical Engineering Department Universitas Tarumanagara Jl. Letjen S. Parman No. 1 Jakarta 11440 INDONESIA

In this paper, the modeling of an industrial robot is presented. The simulation of an industrial robot is carried out using the ADAMS multibody system. The computer-aided design model of an industrial robot is imported into ADAMS. The imported model in ADAMS is prepared for the simulation by adding markers or frames to the rigid bodies of an industrial robot. The relationship between the rigid bodies is established by applying constraints in ADAMS. The STEP function from the function builder is used to move the rigid bodies of an industrial robot. The STEP function is an expression that uses the initial and final conditions to create a smooth motion. The joint limits of an industrial robot are specified in the STEP function to trace the path of an end-effector, which represents the workspace of an industrial robot. Furthermore, the pose of an industrial robot using STEP expression is investigated in the postprocessor.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Zygomatic bone registration based on a modified student's mixture model method 26

Zygomatic bone registration based on a modified student's mi...

引用

26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019

作者： Noori, Seyed Mohammad Reza Mobaraki, Mohammad Ahmadian, Alireza Bayat, Mohammad Bahrami, Naghmeh Tehran University of Medical Sciences Departments of Medical Physics and Biomedical Engineering School of Medicine Tehran Iran Sharif University of Technology Electrical Engineering Department Tehran Iran Research Center for Biomedical Technologies and Robotics Tehran University of Medical Sciences Tehran Iran Craniomaxillofacial Research Center Tehran University of Medical Sciences Tehran Iran Oral and Maxillofacial Surgery Department School of Dentistry Tehran University of Medical Sciences Tehran Iran Department of Tissue Engineering and Applied Cell Sciences School of Advanced Technologies in Medicine Tehran University of Medical Sciences Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156637

Point Set Registration (PSR) of anatomic parts is used in different fields such as Patient Specific Implant (PSI) design in Computer Assisted Surgery (CAS) procedure. We designed a modified rigid PSR method based on student's-t mixture model. The proposed method is compared with Coherent Point Drift (CPD) registration method. Higher convergence speed and the lower error value are the advantages of the suggested algorithm in compare with CPD. In our method, the number of iterations decreases by about 69%, and the final error improvement was about 7% in comparison with CPD. The robustness of the proposed algorithm makes it beneficial to be used in the procedure of designing PSI in both unilateral and bilateral zygomatic bone defects caused by trauma or tumor ablation in the field of maxillofacial. © 2019 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

引用

arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Corrections to “Outlier-Robust Student's-t-Based IMM-VB Localization for Manned Aircraft Using TDOA Measurements” [Jun 20 1646-1658]

引用

IEEE/ASME Transactions on Mechatronics 2020年第4期25卷 2139-2139页

作者： Wonkeun Youn Yulong Huang Hyun Myung Korea Aerospace Research Institute Daejeon South Korea Urban Robotics Laboratory School of Electrical Engineering KI-Robotics KI-AI Korea Advanced Institute of Science and Technology Yuseong-gu South Korea Department of Automation Harbin Engineering University Harbin China

Presents corrections to author information for the above named paper.

关键词： Kalman filters Bayes methods Time of arrival estimation Target tracking

来源：评论

学校读者我要写书评

暂无评论

Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

引用

Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

来源：评论

学校读者我要写书评

暂无评论

Associations between Aβ deposition and neurometabolic alterations in Alzheimer's disease: Insights from hybrid 3D MRSI-PET imaging

引用

Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70332页

作者： Miao Zhang Jialin Hu Yaoyu Zhang Ziyu Meng Yibo Zhao Yudu Li Wen Jin Xiao-Hang Qian Lijun Wang Guanyu Ye Xu-Feng Jiang Ya-Dong Li Jian Ye Huidong Tang Jun Liu Parashkev Nachev Zhi-Pei Liang Biao Li Yao Li Department of Nuclear Medicine Ruijin Hospital School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy Shanghai Jiao Tong University Shanghai China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA. National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Geriatrics Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Medical Center on Aging of Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurology and Institute of Neurology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurovascular Center Changhai Hospital Naval Medical University Shanghai China. Department of Gastroenterology Songjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders State Key Laboratory of Eye Health Shanghai Jiao Tong University School of Medicine Shanghai China. Shanghai Key Laboratory of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China. High-Dimensional Neurology Group Institute of Neurology University College London London UK.

INTRODUCTION:Understanding the neurometabolic changes associated with amyloid-β (Aβ) deposition is important for early Alzheimer's disease (AD) diagnosis, but their spatial relationships remained unexplored due to technical limitations. METHODS:We investigated the relationship between Aβ deposition and neuronal and glial metabolites using high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) (8-min scan, 2 × 3 × 3 mm resolution) and Aβ-positron emission tomography (Aβ-PET) imaging. N-acetylaspartate, myo-inositol, and creatine maps were obtained from 174 participants: 39 controls, 65 mild cognitive impairment (MCI), and 70 AD patients. RESULTS:N-Acetylaspartate levels were negatively correlated with Aβ, while myo-inositol levels were positively correlated globally. Regional associations with Aβ include N-acetylaspartate reductions in frontal cortex, anterior cingulate cortex, and precuneus, and myo-inositol increases in precuneus, lateral temporal, and lateral parietal cortices. Combined MRSI and PET biomarkers achieved the highest diagnostic accuracy for MCI and AD . DISCUSSION:Hybrid high-resolution 3D MRSI and Aβ-PET imaging provides valuable insights into Aβ's impact on neurometabolic changes, improving early AD diagnosis. HIGHLIGHTS:Hybrid 3D magnetic resonance spectroscopic imaging-positron emission tomography (MRSI-PET) imaging reveals Aβ deposition impact on neurometabolism in Alzheimer's disease (AD). N-acetylaspartate (NAA) as a neuronal metabolic marker is negatively associated with Aβ globally and locally. Myo-inositol (mI) as a glial metabolic marker is positively associated with Aβ globally and locally. Combining 3D magnetic resonance spectroscopic imaging (MRSI) and PET biomarkers improves diagnostic accuracy for mild cognitive impairment (MCI) and AD.

关键词： alzheimer's disease amyloid‐β magnetic resonance spectroscopic imaging myo‐inositol n‐acetylaspartate

来源：评论

学校读者我要写书评

暂无评论

Roadmap on soft robotics: multifunctionality, adaptability and growth without borders

引用

Multifunctional Materials 2022年第3期5卷 032001-032001页

作者： Mazzolai, Barbara Mondini, Alessio Del Dottore, Emanuela Margheri, Laura Carpi, Federico Suzumori, Koichi Cianchetti, Matteo Speck, Thomas Smoukov, Stoyan K. Burgert, Ingo Keplinger, Tobias Siqueira, Gilberto De Freitas Vanneste, Felix Goury, Olivier Duriez, Christian Nanayakkara, Thrishantha Vanderborght, Bram Brancart, Joost Terryn, Seppe Rich, Steven I. Liu, Ruiyuan Fukuda, Kenjiro Someya, Takao Calisti, Marcello Laschi, Cecilia Sun, Wenguang Wang, Gang Wen, Li Baines, Robert Patiballa, Sree Kalyan Kramer-Bottiglio, Rebecca Rus, Daniela Fischer, Peer Simmel, Friedrich C Lendlein, Andreas Bioinspired Soft Robotics Laboratory Istituto Italiano di Tecnologia Italy Department of Industrial Engineering University of Florence Via di S. Marta 3 Florence50139 Italy Department of Mechanical Engineering Tokyo Institute of Technology Ookayama Meguro Tokyo152-8550 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56025 Italy Plant Biomechanics Group & Botanic Garden and Cluster of Excellence livMatS @ FIT University of Freiburg Germany Active and Intelligent Materials Laboratory Queen Mary University of London LondonE1 4NS United Kingdom Wood Materials Science Institute for Building Materials ETH Zurich Zurich8093 Switzerland Cellulose and Wood Materials Laboratory Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland University of Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Dyson School of Design Engineering Imperial College London United Kingdom Vrije Universiteit Brussel and imec Brussels Belgium Thin-Film Device Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Electrical and Electronic Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Italy Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom Department of Mechanical Engineering National University of Singapore Singapore School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States CSAIL MIT Schwarzman College of Computing United States Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute of Physical Chemistry

Soft robotics aims at creating systems with improved performance of movement and adaptability in unknown, challenging, environments and with higher level of safety during interactions with humans. This Roadmap on Soft robotics covers selected aspects for the design of soft robots significantly linked to the area of multifunctional materials, as these are considered a fundamental component in the design of soft robots for an improvement of their peculiar abilities, such as morphing, adaptivity and growth. The roadmap includes different approaches for components and systems design, bioinspired materials, methodologies for building soft robots, strategies for the implementation and control of their functionalities and behavior, and examples of soft-bodied systems showing abilities across different environments. For each covered topic, the author(s) describe the current status and research directions, current and future challenges, and perspective advances in science and technology to meet the challenges. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Machine design

来源：评论

学校读者我要写书评

暂无评论

Understanding metric-related pitfalls in image analysis validation

arXiv

引用

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：