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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Intensive summer course in robotics – Robotcraft

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Applied Computing and Informatics 2018年第1-2期16卷 155-179页

作者： Ferreira, N.M. Fonseca Araujo, André Couceiro, M.S. Portugal, David Department of Electrical Engineering (DEE) Engineering Institute of Coimbra (ISEC) Polytechnic of Coimbra (IPC) Rua Pedro Nunes – Quinta da Nora Coimbra Portugal GECAD - Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation Development of the Engineering Institute of Porto (ISEP) Polytechnic Institute of Porto (IPP) Porto Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Ingeniarius Lda Coimbra Portugal

This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics;learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions. © 2018, N.M. Fonseca Ferreira, André Araujo, M.S. Couceiro and David Portugal.

关键词： International trends in educational robotics Project-based learning ROS

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Spatial health systems when humans move around

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2015年 8700卷 41-69页

作者： Gottfried, Björn Aghajan, Hamid Wong, Kevin Bing-Yung Augusto, Juan Carlos Guesgen, Hans Werner Kirste, Thomas Lawo, Michael Centre for Computing and Communication Technologies University of Bremen Bremen Germany Lab Stanford University Stanford United States iMinds Ghent University Ghent Belgium Research Group on the Development of Intelligent Environments Middlesex University London United Kingdom School of Engineering and Advanced Technology Massey University Palmerston North New Zealand Department of Computer Science University of Rostock Rostock Germany

This chapter outlines spatial health systems and discusses issues regarding their technical implementation and employment. This concerns in particular diseases which manifest themselves in the spatiotemporal behaviours of patients, showing patterns that enable conclusions about their underlying well-being. While a general overview is given, as an example the case of patients suffering from Alzheimer’s disease is examined more carefully in order to treat different aspects detailed enough. Especially, wearable and ambient technologies, activity recognition techniques as well as ethical aspects are discussed. The given literature review ranges from basic methods of Artificial Intelligence research to commercial products which are already available from the industry. © Springer International Publishing Switzerland 2015.

关键词： Wearable technology

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Preface to the Journal of Smart Cities and Society issue 3(2)

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Journal of Smart Cities and Society 2024年第PRE-PRESS期Pre-press卷 81-81页

作者： Augusto, Juan Carlos Department of Computer Science Middlesex University London UK Research Group on Development of Intelligent Environments Middlesex University London UK

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Preface

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Journal of Reliable intelligent environments 2019年第2期5卷 65-65页

作者： Coronato, Antonio Augusto, Juan Carlos Institute for High Performance Computing and Networking National Research Council Naples Italy Research Group on Development of Intelligent Environments Department of Computer Science Middlesex University London United Kingdom

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Proposal of Axial-gap type Magnetic Multiple Spur Gear for In-wheel Motor System of Electric Vehicle

Proposal of Axial-gap type Magnetic Multiple Spur Gear for I...

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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Taiga Kamijo Kohei Aiso Kan Akatsu Yasuaki Aoyama Electrical Engineering and Computer Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

Magnetic gears have the characteristics of maintenance-free, low noise, and low vibration due to non-contact power transmission. By taking advantage of the non-contact power transmission, the motor system can be made smaller and lighter by further increasing the motor speed. Magnetic Multiple Spur Gear (MMSG) has been proposed, the drive system of MMSG and multiple high-speed motors can reduce the size and weight of the drive system by converting the total power of multiple high-speed motors. Applying this drive system to in-wheel motors will increase the available interior space in electric vehicles. However, there is a risk that the high-speed rotor and low-speed rotor contact with each other due to the radial structure in the in-wheel motor system. In this research, an axial-gap MMSG is proposed to solve the problem and to obtain higher torque density, the performances such as torque density and efficiency are evaluated by finite element analysis.

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis.

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Imagining and building wise machines: The centrality of AI metacognition

arXiv

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arXiv 2024年

作者： Johnson, Samuel G.B. Karimi, Amir-Hossein Bengio, Yoshua Chater, Nick Gerstenberg, Tobias Larson, Kate Levine, Sydney Mitchell, Melanie Rahwan, Iyad Schölkopf, Bernhard Grossmann, Igor University of Waterloo Department of Psychology Canada University of Waterloo Department of Electrical and Computer Engineering Canada Université de Montréal Department of Computer Science and Operations Research Canada Warwick Business School Behavioural Science Group United Kingdom Stanford University Department of Psychology United States University of Waterloo Cheriton School of Computer Science Canada Allen Institute for Artificial Intelligence United States Santa Fe Institute United States Max Planck Institute for Human Development Germany Max Planck Institute for Intelligent Systems Germany

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack transparency in their reasoning (explainability), face challenges in communication and commitment (cooperation), and pose risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems—those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive—through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition—the ability to reflect on and regulate one’s thought processes—is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one’s knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values—a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations. © 2024, CC BY.

关键词： Artificial intelligence

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Ekrucami architecture-applications in healthcare domain

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2017年 298卷 140-152页

作者： Ramos, Carlos Martinho, Diogo Marreiros, Goreti Villarrubia, Gabriel De Paz, Juan F. Corchado, Juan M. Ko, Hoon Kim, Haeng-Kon Yoe, Hyun GECAD-Research Group Intelligent Engineering and Computing for Advanced Innovation Development Institute of Engineering-Polytechnic Porto Portugal Departamento de Informática y Automática Universidad de Salamanca Plaza de la Merced s/n Salamanca37008 Spain Department of Computer Science and Engineering Sungkyunkwan University Korea Republic of Daegu Catholic University Korea Republic of Suncheon National University Korea Republic of

ISBN: (纸本)9781614998037

Ambient Intelligence is a paradigm in Artificial Intelligence which promotes intelligent computing and responds to the necessities of every person in many different environments whether it is a home, classroom, a shopping, or a hospital. Nowadays we are increasingly seeing and talking about issues such as aging populations or the increase in the number of patients with chronic diseases (among other issues). Looking at the solutions available in this area it is necessary to develop more effective systems that can support people and improve their quality of life. This work is a brief study on EKRUCAmI architecture and its suitability to the healthcare sector. Some healthcare systems will be presented which have been designed to use EKRUCAmI architecture. © 2017 The authors and IOS Press.

关键词： Ambient intelligence

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology & Evolution 2025年 1-12页

作者： Stephen Pringle Jessica C. Fisher Gail E. Austen Jake E. Bicknell Nicolas J. Deere Mohammad S. Farhadinia Charlie J. Gardner Richard A. Griffiths Daniel J. Ingram Matthew J. Struebig Natalie Yoh Zoe G. Davies Martin Dallimer Mark A. Goddard Katherine C. R. Baldock Miranda T. Prendergast-Miller Léni K. Le Goff Emma Hart Simón C. Smith Simon J. Langdale Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Joseph J. Bailey Lindsay F. Banin Cristina Banks-Leite Aliyu S. Barau Reshu Bashyal Adam J. Bates Jon Bielby Serge Wich Christopher D. Williams Petra Bosilj Marc Hanheide Emma R. Bush Simon J. Butler Dan Carpenter Christopher F. Clements Antoine Cully Kendi F. Davies Brett A. Melbourne Michael Dodd Rosie Drinkwater Don A. Driscoll Guillaume Dutilleux Mads Dyrmann David P. Edwards Aisyah Faruk Richard Field Franziska Schrodt Robert J. Fletcher Chris W. Foster Richard Fox Richard M. Francksen Aldina M. A. Franco Alison M. Gainsbury Ioanna Giorgi Giovanni L. Masala Salua Hamaza Matt W. Hayward Marcus Hedblom Thorunn Helgason Sui P. Heon Kevin A. Hughes Edmund R. Hunt George Jackson-Mills Kelly Jowett Timothy H. Keitt Laura N. Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F. de Lima Nick A. Littlewood Harry H. Marshall Lindsay C. Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L. Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Marcus Rowcliffe Rajeev Pillay Simon G. Potts Lan Qie Carl D. Soulsbury Persie Rolley-Parnell Stephen J. Rossiter Heather Rumble Jon P. Sadler Christopher J. Sandom Asiem Sanyal Sarab S. Sethi Adi Shabrani Robert Siddall Robbert P. H. Snep Margaret C. Stanley Philip A. Stephens P. J. Stephenson Matthew Studley Martin Svátek Gilbert Tang Nicholas K. Taylor Kate D. L. Umbers Robert J. Ward Patrick J. C. White Mark J. Whittingham Ibrahim B. Yakubu Syed A. R. Zaidi Anna Zmarz Joeri A. Zwerts Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK Centre for Environmental Policy Imperial College London London UK Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Synthotech Ltd Milner Court Hornbeam Square Harrogate UK Mechanical Engineering Department University College London London UK HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil Department of Engineering Harper Adams University Newport UK Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK Centre for Ecology and Hydrology Penicuik UK Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK Royal Botanic Garden Edinburgh Edinburgh UK School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK Digital Ecology Limited Bristol UK School of Biological Sciences University of Bristol Bristol UK Department of Computing Imperial College London London UK Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milton Keynes UK Palaeogenomics Group Faculty of Veterinary Medicine Ludwig Maximilian University Munich Munich Germany School of Life and Environmental Sciences

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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