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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding Intelligent robots

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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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Recent Advancements in Solid State Transformer-based EV Fast Charging Stations

Recent Advancements in Solid State Transformer-based EV Fast...

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International Conference on Power Electronics and Drive systems (PEDS)

作者： Ruvini De Seram Anindita Golder Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech University Oshawa ON Canada Ontario Tech-Automotive Center of Excellence (ACE) Ontario Tech University Oshawa ON Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

Growing deployments of ultra fast charging stations (XFCS) are putting a stress on the existing line-frequency transformer (LFT)-based medium voltage (MV) grid due to the high charging demands of exponentially growing electric vehicles (EV). The conventional LFT occupies a large space in the XFCS installation which introduces challenges in facilitating the XFCS infrastructure. As a result, solid-state transformer (SST)-based XFCS are being developed which can be directly connected to the MV grid and several advantages over LFT-based systems such as compactness, intelligence and availability of the DC link to connect renewable energy sources and storage system can be realized. As such, a detailed analysis of current developments in SST-based XFCS is presented and compared with one another in this paper. The challenges associated with the configurations are examined and direction for future research is identified.

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A performance comparison of graph analytic methods for supporting enterprise architecture model maintenance

A performance comparison of graph analytic methods for suppo...

引用

2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Satellite-based InSAR Geodesy and Collocation with GNSS

Satellite-based InSAR Geodesy and Collocation with GNSS

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2023 International Conference on ENTERprise Information systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information systems and Technologies, HCist 2023

作者： Papco, Juraj Bakon, Matus Kubica, Lukas Belicova, Gabika Droscak, Branislav Ferianc, Martin Rovnak, Martin Ruiz, Antonio M. Sousa, Joaquim J. Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 Bratislava813 62 Slovakia Insar.sk Ltd Konstantinova 3 Presov080 01 Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 Presov080 01 Slovakia Department of Engineering School of Sciences and Technology Universidade de Tras-os-Montes e Alto Douro Quinta de Prados Vila Real5000-801 Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 Bratislava827 45 Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Research Group RNM-282 Microgeodesia Jaen University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC Porto4200-465 Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Geodesy

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Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction

arXiv

引用

arXiv 2021年

作者： van Meer, Max Poot, Maurice Portegies, Jim Oomen, Tom The Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Applied Analysis Group Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unknown structure using input-output data, which directly delivers feedforward signals for a-priori unknown tasks. To this end, inverse systems are regarded as noncausal nonlinear finite impulse response (NFIR) systems, and modeled as a Gaussian Process with a stationary kernel function that imposes properties such as smoothness and periodicity. The approach is validated experimentally on a consumer printer with friction and shown to lead to improved tracking performance with respect to linear feedforward. Copyright © 2021, The Authors. All rights reserved.

关键词： Friction

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Managing Control Software Variability in Cyber-Physical Production systems: The V4rdiac Approach

SSRN

引用

SSRN 2024年

作者： Fadhlillah, Hafiyyan Sayyid Meixner, Kristof Greiner, Sandra Fernández, Antonio Manuel Gutiérrez Rabiser, Rick Christian Doppler Laboratory VaSiCS Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Christian Doppler Laboratory SQI TU Wien Favoritenstraße 9-11 Vienna1040 Austria Information Systems Engineering TU Wien Favoritenstraße 9-11 Vienna1040 Austria Software Engineering Group Institute of Computer Science University of Bern Neubrückstraße 10 Bern3012 Switzerland Department of Computer Languages and Systems Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain

Cyber-Physical Production systems (CPPSs) are highly configurable and versatile production systems that utilize diverse hardware components through control software. Managing control software variants in CPPS development is challenging as variation points in the software stem from multiple engineering disciplines. Furthermore, there is a lack of mechanisms to explicitly describe control software variants as part of the software. In this paper, we present an extension of the Variability for 4diac (V4rdiac) approach and investigate its usefulness in managing control software variants based on multidisciplinary variability in CPPS development and delta modeling. We extend the approach by considering its application engineering activities to produce client-specific control software variants. We also present a novel mechanism to visualize deltas in control software to reduce the effort of implementing and maintaining deltas. We apply V4rdiac in two case studies and conduct an additional user study with four participants from industry to evaluate its usefulness. The evaluation results confirm that the engineers were able to successfully manage the control software variants. The results also show that V4rdiac can be easily learned and increases industrial practitioners’ productivity in managing control software. © 2024, The Authors. All rights reserved.

关键词： Application programs

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A sensitivity framework to assess the impact of weather on electric power networks

A sensitivity framework to assess the impact of weather on e...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Ahmed, Arif McFadden, Fiona Stevens Rayudu, Ramesh Massier, Tobias Robinson Research Institute Smart Power and Renewable Energy Systems Group School of Engineering and Computer Science !!!TUMCREATE 1 CREATE Way #10-02 CREATE Tower Singapore Robinson Research Institute Victoria University of Wellington Smart School of Engineering and Computer Science Wellington New Zealand Victoria University of Wellington Smart Power and Renewable Energy Systems Group School of Engineering and Computer Science Wellington New Zealand TUMCREATE 1 CREATE Way 10-02 CREATE Tower Singapore138602 Singapore

ISBN: (纸本)9781728155081

This manuscript provides an analytical sensitivity framework to assess the impacts of individual weather parameters on conductor temperature and to determine the weather parameters that have greater potential to impact power networks and hence power system studies, which can be applied to any region around the globe. Application to weather parameter ranges possible across all of New Zealand (NZ) shows that the conductor temperature and hence the power network is most sensitive to the wind speed followed by the wind angle, and the solar irradiance, when paired with the ambient temperature. The sensitivity analysis framework is expected to aid utilities in assessing the region-specific impact of weather parameters on their network and hence improving their planning, operation, and analysis. © 2020 IEEE.

关键词： Sensitivity analysis

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The Jade Gateway to Exergaming: How Socio-Cultural Factors Shape Exergaming Among East Asian Older Adults

arXiv

引用

arXiv 2024年

作者： Mogavi, Reza Hadi Son, Juhyung Yang, Simin Wang, Derrick M. Choong, Lydia Alhilal, Ahmad Zhou, Peng Yuan Hui, Pan Nacke, Lennart E. The Department of Systems Design Engineering University of Waterloo Canada The Cheriton School of Computer Science University of Waterloo Canada HCI Games Group Stratford School of Interaction Design and Business University of Waterloo ON Canada Guangzhou China Hong Kong University of Science and Technology Hong Kong Aarhus University Denmark Hong Kong University of Science and Technology University of Helsinki Hong Kong Helsinki Finland Finland

Exergaming, blending exercise and gaming, improves the physical and mental health of older adults. We currently do not fully know the factors that drive older adults to either engage in or abstain from exergaming. Large-scale studies investigating this are still scarce, particularly those studying East Asian older adults. To address this, we interviewed 64 older adults from China, Japan, and South Korea about their attitudes toward exergames. Most participants viewed exergames with a positive inquisitiveness. However, socio-cultural factors can obstruct this curiosity. Our study shows that perceptions of aging, lifestyle, the presence of support networks, and the cultural relevance of game mechanics are the crucial factors influencing their exergame engagement. Thus, we stress the value of socio-cultural sensitivity in game design and urge the HCI community to adopt more diverse design practices. We provide several design suggestions for creating more culturally approachable exergames. Copyright © 2024, The Authors. All rights reserved.

关键词： Blending

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

引用

IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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