检索结果-内蒙古大学图书馆

AIAA Guidance, Navigation, and Control Conference, 2017

作者： Tardioli, Luca Franzini, Giovanni Pollini, Lorenzo Innocenti, Mario Automation Robotics and Autonomous Systems Laboratory Department of Information Engineering University of Pisa Pisa56122 Italy

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

The paper presents a visibility augmentation method for a classical proportional navigation guidance law that helps the pursuer maintaining visual contact with the target, and also allows to recover loss of line-of-sight. The concept of visibility is formally introduced in a modified pursuit-evasion differential game, and it appears in the Lagrangian term of the players’ cost functions. Target visibility is then quantified by means of a visibility function, which adds an augmentation term to the proportional navigation structure. Numerical simulations are used for validation. A further simplification of the guidance law is also presented, using the same arguments that lead to the proportional navigation guidance simplest expression, i.e. considering the players on a nominal collision course. The new simplified law is validated in simulation, and compared against its original expression. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Visibility

来源：评论

学校读者我要写书评

暂无评论

Parallel Learning:a Perspective and a Framework

引用

IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 389-395页

作者： Li Li Yilun Lin Nanning Zheng Fei-Yue Wang Department of Automation TNListTsinghua University IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Institute of Artificial Intelligence and Robotics(IAIR) Xi'an Jiaotong University Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

The development of machine learning in complex system is hindered by two problems *** first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven *** second problem is the lack of a general theory which can be used to analyze and implement a complex learning *** this paper,we proposed a general methods that can address both two *** combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by *** a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.

关键词： Descriptive learning machine learning parallel learning parallel systems predictive learning prescriptive learning

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality based Visualization of CAM Instructions towards Industry 4.0 paradigm: a CNC Bending Machine case study

引用

Procedia CIRP 2018年 70卷 368-373页

作者： Dimitris Mourtzis Vasilios Zogopoulos Ioannis Katagis Panagiotis Lagios Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras Greece Gizelis Robotics Robot system integration Schimatari Viotias 32009 Kormatzini Area Greece

Skilled shop-floor technicians are playing an important role in modern Industry 4.0 manufacturing environments, especially in small and customized manufacturing systems. Moving towards Industry 4.0 business models, it is essential to develop advanced solutions that will reinforce the position of technicians in manufacturing, by implementing key enabling technologies. Augmented Reality (AR) has arisen as a key enabling technology, which will enable the transfer of information from digitalized design systems and databases towards human operators, especially including production and maintenance information. Reinforced by the development of advanced communication technologies that support high speed data transfer, such as 5G, and networks that combine low latency with increased availability, such as tactile internet, AR proposes a way for agile information distribution and increased shop-floor status awareness. This study presents a mobile application for visualizing Computer Aided Manufacturing (CAM) instructions for bending processes using Augmented Reality. The application integrates step-by-step process instructions, highly usable menus and advanced visualization that includes safety zones, indications, and text instructions for the operators. The developed framework is applied and validated in a CNC bending machine in a real world scenario provided by a CNC machines manufacturer.

关键词： Augmented Reality Computer-Aided Manufacturing Production Monitoring

来源：评论

学校读者我要写书评

暂无评论

Saturated Continuous Twisting Algorithm 15

Saturated Continuous Twisting Algorithm

引用

15th International Workshop on Variable Structure systems, VSS 2018

作者： Golkani, M.A. Fridman, L.M. Koch, S. Reichhartinger, M. Horn, M. Faculty of Electrical and Information Engineering Graz University of Technology Institute of Automation and Control Graz Austria Department of Control Engineering and Robotics Division of Electrical Engineering Engineering Faculty National Autonomous University of Mexico Mexico D.F. Mexico Institute of Automation and Control Graz University of Technology Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz Austria

ISBN: (纸本)9781538664391

In this paper, a second-order system affected by perturbations is considered. A feedback control law adopting the continuous twisting algorithm is designed such that a saturated and continuous control signal is introduced to the system. In the case that a state variable is not measurable, estimate information obtained through a super-twisting observer is incorporated into the design. For the closed-loops (with and without observer), global finite-time stability properties are established. The feasibility and effectiveness of the strategy is shown employing numerical simulations. © 2018 IEEE.

关键词： Stability

来源：评论

学校读者我要写书评

暂无评论

PROGRAMS project approach to maintenance management

引用

IFAC-PapersOnLine 2020年第3期53卷 313-318页

作者： M. Surico R. Ricatto A. Merlo I. Németh A. Sardelis M. Villoslada E. Montejo N. Frenkel P. Aivaliotis I.M. de la Pera Celada J. Sidiropoulos A. Eytan A. Papavasileiou F. Aggogeri FIDIA S.p.A. Corso Lombardia 11 San Mauro Torinese Italy CeSI Centro Studi Industriali via Tintoretto 10 Cologno Monzese (MI) 20093 Italy Department of Manufacturing Science and Engineering Budapest University of Technology and Economics 1111 Budapest Műegyetem rkp. 3 Hungary CASP S.A. 64 Michalakopoulou str Athens 11528 Greece Savvy Data Systems S.L. San Sebastian Gipuzkoa 20018 Spain Ideko S.Coop Arriaga Kalea 2 20870 Elgoibar Gipuzkoa Spain Laboratory for Machine Tools and Production Engineering (WZL) Campus-Boulevard 30 52074 Aachen Germany Laboratory for Manufacturing Systems & Automation University of Patras Rion Patras 26504 Greece Aurrenak S. Coop Bitorialanda Kalea 15 01010 Vitoria-Gasteiz Araba Spagna Grobotics Gizelis Robotics Kormatzini Area Schimatari Viotias 32009 Greece We Plus S.p.A Strada Torino 43 10043 Orbassano (TO) Italy Calpak-Cicero Hellas S.A. 9 Sygrou Ave. 11743 Athens Greece Department of Mechanical and Industrial Engineering University of Brescia via Branze 38 25123 Brescia Italy

Maintenance management is a vital part of the business of a production company. It contributes to determining the long-term success of the company because poorly maintained resources can stop production activities, causing delays, loss of profit and even personal injuries. While the predictive maintenance approach is nowadays a mainstay of modern factory management, usually only big companies with dedicated research department can deploy it, since its application involves scientific knowledge that is not available in smaller production environments. This paper describes how the EU project PROGRAMS answers the needs of small and medium companies that wish to apply an Industrial Internet of Things (IIoT) approach to maintenance management.

关键词： Maintenance engineering Preventive maintenance Decision support systems Optimization problems Sensors systems

来源：评论

学校读者我要写书评

暂无评论

Development of a Tetris Playing Robot Controlled by KNR

Development of a Tetris Playing Robot Controlled by KNR

引用

IEEE International Conference on Mechatronics and automation

作者： Ming Wang Yanlu Zhang Huifang Dong Xu Li Junzhi Yu Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system.

关键词： Mobile robots Cameras Robot sensing systems Image color analysis Servomotors Robot kinematics

来源：评论

学校读者我要写书评

暂无评论

Auto-LEE: A novel autonomous lower extremity exoskeleton for walking assistance

Auto-LEE: A novel autonomous lower extremity exoskeleton for...

引用

2018 IEEE International Conference on Information and automation, ICIA 2018

作者： He, Yong Wang, Chao Li, Nan Fu, Ruiqing Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

Wearable exoskeletons have been proven to be efficacious in aiding walking for individuals suffering from lower limb mobility disorder. However, the application of most existing devices is limited to inconvenience of usage, e.g., complicated training and unnatural gait. This paper presents a novel autonomous lower extremity exoskeleton, Auto-LEE, for the purpose of improving the practicality of walking assistive devices as well as simplifying their application process. The developed exoskeleton consists of two robotic legs, and each of them has 5 active degrees-of-freedom (DOFs) to independently control the rotations of hip, knee and ankle joints in the sagittal and coronal planes, which enables the device to possess self-balancing ability and flexible gait. The modular design concept is introduced into the structure and hardware development of Auto-LEE, making it more convenient to be assembled and maintained. In order to validate the self-balancing walking ability, virtual prototype simulation and preliminary experiment on flat terrain are implemented. © 2018 IEEE.

关键词： Exoskeleton (robotics)

来源：评论

学校读者我要写书评

暂无评论

Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette 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 Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab 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 Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

arXiv

引用

arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

引用

IEEE Intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance Intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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：