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Optimizing dust accumulation quantification on photovoltaic panels using deep learning visual models with hyperparameter optimization

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Renewable Energy 2025年 251卷

作者： Tahir, Muhammad Faizan Lamichhane, Samyam Tzes, Anthony Fang, Yi El-Fouly, Tarek H.M. Umar, Shayan VRI for Sustainable Energy Technology Storage and Utilization New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates Embedded AI and Robotics Lab New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates Advanced Power and Energy Center EECS Department Khalifa University Abu Dhabi United Arab Emirates

The increasing integration of solar photovoltaic (PV) systems is driven by their cost-effectiveness and sustainability. Nonetheless, dust accumulation significantly reduces PV performance, especially in arid regions like the UAE. This study investigates multiple deep learning architectures, deep residual neural network (DRNN), vision transformer, ResNet-50, and EfficientNet-B7 for accurate dust quantification on PV panels. A diverse indoor imaging dataset is generated with varying zoom lengths (18–200 mm) and dust concentrations (1g–400g). Preprocessing techniques, such as silver line removal, enhance image quality, while model performance is evaluated using mean absolute error (MAE), mean squared error (MSE), and loop error coefficient. DRNN demonstrates superior accuracy in the indoor imaging dataset and is subsequently evaluated on two additional datasets: outdoor drone-captured images (from 4 to 30m heights) and a combined indoor-outdoor dataset. Its performance is further improved through hyperparameter optimization techniques such as Bayesian optimization, particle swarm optimization, genetic algorithm and hyperband. Bayesian optimization excels in indoor and outdoor datasets, while hyperband efficiently balances resources for the combined dataset, enhancing dust estimation and PV maintenance planning. © 2025 Elsevier Ltd

关键词： Particle swarm optimization (PSO)

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Microhardness and Wear Rate Analysis on Laser Cladded Composites of AZ91D Alloy with SiC by Grey Technique

Microhardness and Wear Rate Analysis on Laser Cladded Compos...

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International Conference on Mechanical Engineering: Researches and Evolutionary Challenges, ICMech-REC 2023

作者： Karthick, L. Loganathan, Ganesh Babu Jagadish, C.A. Somasundaram, B. Kandavalli, Sumanth Ratna Chithambaranathan, P. Department of Mechanical Engineering Hindusthan College of Engineering and Technology Coimbatore641032 India Department of Robotics and Automation Rajalakshmi Engineering College Chennai602105 India School of Mechanical Engineering REVA University Bengaluru India Department of Mechanical Engineering Tandon School of Engineering New York University 6 Metro Tech Center BrooklynNY11201 United States Department of Mechanical Engineering Rajalakshmi Engineering College Thandalam Chennai602105 India

ISBN: (纸本)9789819722488

Owing to increments in heavy vehicles for transporting the materials and reducing the vehicle weight, magnesium alloys play the mandatory role in various fabricating industries. The Mg alloy alone does not satisfy their necessities without adding the strengthening particles;therefore, this investigation stated the silicon carbide (SiC) was the reinforcing into AZ91D Mg alloy to compose the coating metal matrix composites through the laser cladding process. This method is unique when appropriated processing parameters are chosen and compared to laser melting process;laser cladding is the significant one. The laser cladding processing parameters are scanning speed (3–5 m/s), SiC proportions (10–30%) and laser output power (0.6–0.8 kW), respectively. Then the composed coating AZ91D Mg alloy with SiC specimen is accounted for experimentation of microhardness and wear rates. The microhardness and wear rates are improved at increasing of weight percentage from 20 to 30%, maximum of scanning speed at 5 m/s and 0.7 kW of laser output power parameters. The grey relational multi-task optimization method was utilized to recognize the single responses from various laser cladding processing parameters. Finally, the SEM with worn surfaces was taken on the optimal specimen composite. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Silicon carbide

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Optomechanical dissipative solitons (vol 600, pg 75, 2021)

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NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China

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Exploring quantitative measures in metacognition of emotion

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Scientific Reports 2024年第1期14卷 1-10页

作者： Lee, Hsing-Hao Liu, Gabrielle Kaili-May Chen, Yi-Chuan Yeh, Su-Ling Department of Psychology New York University New York NY United States Department of Psychology National Taiwan University Taipei Taiwan Department of Computer Science Yale University New Haven CT United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Department of Mathematics Massachusetts Institute of Technology Cambridge MA United States Department of Medicine MacKay Medical College Taipei Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan National Humanities Center Research Triangle Park NC United States

Metacognition of emotion (meta-emotion) refers to the ability to evaluate and identify one’s emotional feelings. No previous study has defined and measured this construct through objective and quantitative procedures. We established a reliable method to measure meta-emotion. With a two-interval forced-choice procedure, participants selected which of two pictures elicited stronger positive emotion;via the Law of Comparative Judgment, their responses were used to compute individual psychological distances for the emotional responses triggered by the pictures. Then, participants were asked to judge whether a pre-exposed picture induced a stronger positive emotion than the median of that elicited by the whole picture set, followed by a confidence rating. By utilizing each individual’s psychological distance, the correctness of a participant’s emotional experience was quantified by dʹ, and meta-emotion was quantified using meta-dʹ, M-ratio, and M-diff as indices of metacognitive sensitivity and efficiency based on Signal-Detection Theory. Test–retest reliabilities, validated by Spearman correlation, were observed in meta-dʹ, M-ratio, and marginally with M-diff, suggesting the stability of meta-emotion in the current design. This study unveils a validated procedure to quantify meta-emotion, extendable for assessing metacognition of other subjective feelings. Nevertheless, caution is warranted in interpretation, as the measured processes may be influenced by non-metacognitive factors. © 2024, The Author(s).

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Open X-Embodiment: Robotic Learning Datasets and RT-X Models...

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2024 IEEE International Conference on robotics and automation, ICRA 2024

作者： O'Neill, Abby Rehman, Abdul Maddukuri, Abhiram Gupta, Abhishek Padalkar, Abhishek Lee, Abraham Pooley, Acorn Gupta, Agrim Mandlekar, Ajay Jain, Ajinkya Tung, Albert Bewley, Alex Herzog, Alex Irpan, Alex Khazatsky, Alexander Rai, Anant Gupta, Anchit Wang, Andrew Singh, Anikait Garg, Animesh Kembhavi, Aniruddha Xie, Annie Brohan, Anthony Raffin, Antonin Sharma, Archit Yavary, Arefeh Jain, Arhan Balakrishna, Ashwin Wahid, Ayzaan Burgess-Limerick, Ben Kim, Beomjoon Scholkopf, Bernhard Wulfe, Blake Ichter, Brian Lu, Cewu Xu, Charles Le, Charlotte Finn, Chelsea Wang, Chen Xu, Chenfeng Chi, Cheng Huang, Chenguang Chan, Christine Agia, Christopher Pan, Chuer Fu, Chuyuan Devin, Coline Xu, Danfei Morton, Daniel Driess, Danny Chen, Daphne Pathak, Deepak Shah, Dhruv Buchler, Dieter Jayaraman, Dinesh Kalashnikov, Dmitry Sadigh, Dorsa Johns, Edward Foster, Ethan Liu, Fangchen Ceola, Federico Xia, Fei Zhao, Feiyu Stulp, Freek Zhou, Gaoyue Sukhatme, Gaurav S. Salhotra, Gautam Yan, Ge Feng, Gilbert Schiavi, Giulio Berseth, Glen Kahn, Gregory Wang, Guanzhi Su, Hao Fang, Hao-Shu Shi, Haochen Bao, Henghui Ben Amor, Heni Christensen, Henrik I Furuta, Hiroki Walke, Homer Fang, Hongjie Ha, Huy Mordatch, Igor Radosavovic, Ilija Leal, Isabel Liang, Jacky Abou-Chakra, Jad Kim, Jaehyung Drake, Jaimyn Peters, Jan Schneider, Jan Hsu, Jasmine Bohg, Jeannette Bingham, Jeffrey Wu, Jeffrey Gao, Jensen Hu, Jiaheng Wu, Jiajun Wu, Jialin Sun, Jiankai Luo, Jianlan Gu, Jiayuan Tan, Jie Oh, Jihoon Wu, Jimmy Lu, Jingpei Yang, Jingyun Malik, Jitendra Silverio, Joao Hejna, Joey Booher, Jonathan Tompson, Jonathan Yang, Jonathan Salvador, Jordi Lim, Joseph J. Han, Junhyek Wang, Kaiyuan Rao, Kanishka Pertsch, Karl Hausman, Karol Go, Keegan Gopalakrishnan, Keerthana Goldberg, Ken Byrne, Kendra Oslund, Kenneth Kawaharazuka, Kento Black, Kevin Lin, Kevin Zhang, Kevin Ehsani, Kiana Lekkala, Kiran Ellis, Kirsty Rana, Krishan Srinivasan, Krishnan Fang, Kuan Singh, Kunal Pratap Zeng, Kuo-Hao Hatch, Kyle Hsu, Kyle Itti, Laurent Chen, Lawrence Yunliang Pinto, Lerrel Allen Institute for AI United States Arizona State University United States California Institute of Technology United States Carnegie Mellon University United States Columbia University United States EPFL Switzerland ETH Zürich Switzerland Flexiv Robotics United States Georgia Institute of Technology United States German Aerospace Center Germany Google DeepMind United Kingdom Google Research United States IO-AI Tech Imperial College London United Kingdom Intrinsic Llc United States Istituto Italiano di Tecnologia Italy Korea Advanced Institute of Science & Technology Korea Republic of Max Planck Institute Germany Meta AI United States Mila Quebec Canada NVIDIA United States New York University United States Princeton University United States Queensland University of Technology Australia Riken Japan Shanghai Jiao Tong University China Stanford University United States Technische Universität Darmstadt Germany University of Texas at Austin United States University of Tokyo Japan Toyota Research Institute United States University of California Berkeley United States University of California Davis United States University of California San Diego United States University of Edinburgh United Kingdom University of Freiburg Germany University of Illinois Urbana-Champaign United States University of Michigan United States University of Montreal Canada University of Pennsylvania United States University of Southern California United States University of Technology Nuremberg Germany University of Washington United States

ISBN: (纸本)9798350384574

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist"X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is ***. © 2024 IEEE.

关键词： Robot learning

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Open X-Embodiment: Robotic Learning Datasets and RT-X Models...

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IEEE International Conference on robotics and automation (ICRA)

作者： Abby O’Neill Abdul Rehman Abhiram Maddukuri Abhishek Gupta Abhishek Padalkar Abraham Lee Acorn Pooley Agrim Gupta Ajay Mandlekar Ajinkya Jain Albert Tung Alex Bewley Alex Herzog Alex Irpan Alexander Khazatsky Anant Rai Anchit Gupta Andrew Wang Anikait Singh Animesh Garg Aniruddha Kembhavi Annie Xie Anthony Brohan Antonin Raffin Archit Sharma Arefeh Yavary Arhan Jain Ashwin Balakrishna Ayzaan Wahid Ben Burgess-Limerick Beomjoon Kim Bernhard Schölkopf Blake Wulfe Brian Ichter Cewu Lu Charles Xu Charlotte Le Chelsea Finn Chen Wang Chenfeng Xu Cheng Chi Chenguang Huang Christine Chan Christopher Agia Chuer Pan Chuyuan Fu Coline Devin Danfei Xu Daniel Morton Danny Driess Daphne Chen Deepak Pathak Dhruv Shah Dieter Büchler Dinesh Jayaraman Dmitry Kalashnikov Dorsa Sadigh Edward Johns Ethan Foster Fangchen Liu Federico Ceola Fei Xia Feiyu Zhao Freek Stulp Gaoyue Zhou Gaurav S. Sukhatme Gautam Salhotra Ge Yan Gilbert Feng Giulio Schiavi Glen Berseth Gregory Kahn Guanzhi Wang Hao Su Hao-Shu Fang Haochen Shi Henghui Bao Heni Ben Amor Henrik I Christensen Hiroki Furuta Homer Walke Hongjie Fang Huy Ha Igor Mordatch Ilija Radosavovic Isabel Leal Jacky Liang Jad Abou-Chakra Jaehyung Kim Jaimyn Drake Jan Peters Jan Schneider Jasmine Hsu Jeannette Bohg Jeffrey Bingham Jeffrey Wu Jensen Gao Jiaheng Hu Jiajun Wu Jialin Wu Jiankai Sun Jianlan Luo Jiayuan Gu Jie Tan Jihoon Oh Jimmy Wu Jingpei Lu Jingyun Yang Jitendra Malik João Silvério Joey Hejna Jonathan Booher Jonathan Tompson Jonathan Yang Jordi Salvador Joseph J. Lim Junhyek Han Kaiyuan Wang Kanishka Rao Karl Pertsch Karol Hausman Keegan Go Keerthana Gopalakrishnan Ken Goldberg Kendra Byrne Kenneth Oslund Kento Kawaharazuka Kevin Black Kevin Lin Kevin Zhang Kiana Ehsani Kiran Lekkala Kirsty Ellis Krishan Rana Krishnan Srinivasan Kuan Fang Kunal Pratap Singh Kuo-Hao Zeng Kyle Hatch Kyle Hsu Laurent Itti Lawrence Yunliang Chen Lerrel Pinto Li Fei-Fei Liam Tan Linxi Jim Fan Lionel Ott Lisa Lee Luca Weihs Magnum Chen Marion Lepert Marius Memmel Masayoshi Tomizuka Masha Itkina Mateo University of California Berkeley University of Edinburgh University of Texas at Austin University of Washington German Aerospace Center Google DeepMind Stanford University NVIDIA Intrinsic LLC New York University Meta AI Georgia Institute of Technology Allen Institute for AI University of California Davis Toyota Research Institute Queensland University of Technology Korea Advanced Institute of Science & Technology Max Planck Institute Shanghai Jiao Tong University Flexiv Robotics Columbia University University of Freiburg Carnegie Mellon University University of Pennsylvania Imperial College London Istituto Italiano di Tecnologia IO-AI TECH University of Southern California University of California San Diego ETH Zürich University of Montreal Mila Quebec California Institute of Technology Arizona State University The University of Tokyo Technische Universität Darmstadt The University of Texas at Austin Google Research Princeton University EPFL University of Illinois Urbana-Champaign University of Michigan RIKEN University of Technology Nuremberg

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is ***.

关键词： Learning systems Adaptation models Computer vision Computational modeling Collaboration Data models Task analysis

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Preface——Special Issue on new Directions in Nonlinear and Distributed Control

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Journal of Systems Science & Complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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