检索结果-内蒙古大学图书馆

Terms Development of Additive Manufacturing

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2022年

作者： Tamir, Tariku Sinshaw Jiang, Jingchao Xiong, Gang Shen, Zhen Lodhi, Ehtisham Ali, Hub Wan, Li Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266000 China Technology Co. Ltd Foshan528225 China Institute of automation China

Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development. © 2022, CC BY.

关键词： 3D printers

A Survey of Robot Manipulation in Contact

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Suomalainen, Markku Karayiannidis, Yiannis Kyrki, Ville Center of Ubiquitous Computing Faculty of Information Technology and Electrical Engineering University of Oulu Finland Systems and Control Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Intelligent Robotics research group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Helsinki Finland

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of 1) performing tasks that always require contact and 2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks. © 2021, CC BY.

关键词： Surveys

Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses

学校读者我要写书评

暂无评论

IEEE Transactions on Vehicular Technology 2025年

作者： Kang, Yingkai Wen, Jinbo Kang, Jiawen Zhang, Tao Du, Hongyang Niyato, Dusit Yu, Rong Xie, Shengli Guangdong University of Technology School of Automation Guangzhou510006 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science 639798 Singapore

The vehicular metaverse is envisioned as a blended immersive domain that promises to bring revolutionary changes to the automotive industry. As a core component of vehicular metaverses, Vehicle Twins (VTs) are digital twins that cover the entire life cycle of vehicles, providing immersive virtual services for Vehicular Metaverse Users (VMUs). Vehicles with limited resources offload the computationally intensive tasks of constructing and updating VTs to edge servers and migrate VTs between these servers, ensuring seamless and immersive experiences for VMUs. However, the high mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. To address these issues, we propose a secure and reliable VT migration framework in vehicular metaverses. Specifically, we design a two-layer trust evaluation model to comprehensively evaluate the reputation value of edge servers in the network communication and interaction layers. Then, we model the VT migration problem as a partially observable Markov decision process and design a hybrid-Generative Diffusion Model (GDM) algorithm based on deep reinforcement learning to generate optimal migration decisions by taking hybrid actions (i.e., continuous actions and discrete actions). Numerical results demonstrate that the hybrid-GDM algorithm outperforms the baseline algorithms, showing strong adaptability in various settings and highlighting the potential of the hybrid-GDM algorithm for addressing various optimization issues in vehicular metaverses. © 1967-2012 IEEE.

关键词： deep reinforcement learning generative diffusion models reputation twin migration Vehicular metaverse

Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings. © 2022, CC BY.

关键词： Reinforcement learning

Line Spacing Effect on the Giant Magnetoimpedance Behavior of Microribbon Meanders: A Comparison of Theory and Experiment

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Yang, Zhen Wang, Zhenbao Liu, Mengyu Meng, Ziqin Sun, Xuecheng Huang, Yong Guo, Yongbin School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guangxi Guilin541004 China Education Department of Guangxi Zhuang Autonomous Region Guangxi Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China Microelectronic Research & Development Center School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Xidian-Wuhu Research Institute Anhui Wuhu241000 China Key Laboratory of UWB & THz of Shandong Academy of Sciences Institute of Automation Qilu University of Technology Shandong Academy of Sciences Jinan250014 China

Microribbon meanders based on giant magnetoimpedance (GMI) effect have become a research hot spot due to their higher sensitivity and spatial resolution. However, further optimization of the line spacing is required to improve the performance of meanders for sensor application. The model of GMI effect of microribbon meanders is established. The effect of variable line spacing (Ls) on GMI behavior in meanders are analyzed systematically. Comparison of theory and experiment indicates that the smaller line spacing increases the spatial resolution of the meanders and improves its performance. The line spacing is roughly negatively correlated with GMI ratio. This behavior of the GMI ratio is dominated by the overall negative contribution of the mutual inductance. The superior line spacing is found in the identical sensing area. Our findings will contribute to the design of high-performance micropatterned meander GMI sensors and the establishment of ribbon-based magnetic-sensitive biosensing system. © 2023, The Authors. All rights reserved.

关键词： Electric impedance

Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions

学校读者我要写书评

暂无评论

IEEE Journal on Indoor and Seamless Positioning and Navigati...

IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

Attention Spiking Neural Networks

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yao, Man Zhao, Guangshe Zhang, Hengyu Hu, Yifan Deng, Lei Tian, Yonghong Xu, Bo Li, Guoqi The School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an China Peng Cheng Laboratory China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Center for Brain-Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China Institute for Artificial Intelligence Peking University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

Benefiting from the event-driven nature and sparse spiking communication of the brain, spiking neural networks (SNNs) are becoming a promising energy-efficient alternative to traditional artificial neural networks (ANNs). However, the performance gap between SNNs and ANNs has been a significant hindrance to deploying SNNs ubiquitously for a very long period of time. To leverage the full potential of SNNs, we study the effect of attention mechanisms in SNNs, which makes they can focus on important information. We first present our idea of attention in SNNs with a plug-and-play combined module kit, termed the Multi-dimensional Attention (MA) module. Then, a new attention SNN architecture with end-to-end training called"MA-SNN" is proposed, which infers attention weights along the temporal dimension, channel dimension, as well as spatial dimension separately or simultaneously. Based on the existing neuroscience theories, we exploit the attention weights to optimize membrane potentials, which in turn regulate the spiking response in a data-dependent way. At the cost of negligible additional parameters, MA facilitates vanilla SNNs to achieve sparser spiking activity, better performance, and energy efficiency concurrently. Experiments are conducted in event-based DVS128 Gesture/Gait action recognition and ImageNet-1k image classification. On Gesture/Gait, the spike counts are reduced by 84.9%/81.6%, the task accuracy and energy efficiency are improved by 5.9%/4.7% and 3.4×/3.2×. On ImageNet-1K, we achieve top-1 accuracy of 75.92% and 77.08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs. Compared with counterpart Res-ANN-104, the performance gap becomes -0.95/+0.21 percent and has 31.8×/7.4× better energy efficiency. To our best knowledge, this is for the first time, that the SNN community achieves comparable or even better performance compared with its ANN counterpart in the large-scale dataset. To analyze and support the effectiveness of MA-SNN

关键词： Neural networks

A novel test items evaluation method for mixed-signal system-on-chips based on extra information gain cost

学校读者我要写书评

暂无评论

Measurement 2025年 256卷

作者： Kewei Deng Houjun Wang Pu Pu Taochen Gu Enxiao Liu Zhenyu Zhao School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore A*STAR Institute of High Performance Computing Singapore 138632 Singapore

As system-on-chip (SoC) grow increasingly complex, the number of test items required for accurate characterization continues to rise. However, the high test time necessitates efficient test process optimization. Although several studies have explored in digital chips, where test data are more accessible and scan chains enable efficient defect localization, these methods are often ineffective for analog chips. In mixed-signal SoC tests, advanced optimization algorithms typically rely on the posterior probability knowledge, which is usually unavailable under the stop-on-first-failure mechanism. To overcome these limitations, this paper proposes a novel test items evaluation method by introducing an extra information gain cost (EIGC) index. The proposed method is based on correlation analysis, where each test item is assessed throughout the entire test process without relying on failed chips’ database. It is validated through experiments on the final test and binning process of two mixed-signal SoC designs. Results demonstrate that reordering test sequences and pruning redundant test items based on the EIGC index can achieve a significant reduction in test time without defective chips escaping. To the best of our knowledge, for the first time, such an index is introduced to evaluate the SoC test items under stop-on-first-failure mechanism without requiring posterior probability knowledge.

关键词：

Content-aware Warping for View Synthesis

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Guo, Mantang Hou, Junhui Jin, Jing Liu, Hui Zeng, Huanqiang Lu, Jiwen The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China The School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The School of Information Science and Engineering Huaqiao University Xiamen China The Department of Automation Tsinghua University Beijing China

Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from a set of input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on light field datasets with wide baselines and multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code will be publicly available at https://***/MantangGuo/CW4VS. Copyright © 2022, The Authors. All rights reserved.

关键词： Interpolation