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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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IEEE International Conference on Image Processing

作者： Chaowei Han Gaofeng Meng Chunlei Huo State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences School of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Content-based video retrieval aims to retrieve near-duplicate entries from a database of a given query video. It plays an important role in combating video piracy. Robustness to video temporal dynamics is crucial for a representation model in video retrieval, as frames extracted from two copied videos are hardly temporally aligned in actual situations. However, current image retrieval datasets have difficulty in evaluating this robustness. To address this issue, we collect Similar Frame Dataset (SFD), which consists of 32,923 query-target pairs with 128,240 distraction images. The task of SFD is to retrieve the target frame from all items given a query frame. SFD is constructed by sampling frames from Kinetics-700 action classification dataset. An object detection model (Faster R-CNN) and a Multimodal Large Language Model (BLIP2) are used during sampling to select those valid frames. Besides, we propose Adjacent Frames Contrastive Learning (AFCL) framework. In AFCL, adjacent frames are sampled from unlabeled videos as positive pairs. An image representation model with robustness to changing frames can be trained under AFCL framework and achieve the state-of-the-art performance on SFD. The code will be released at https://***/Chuan-shanjia/Similar-Frame-Dataset.

关键词： Codes Databases Scalability Large language models Image retrieval Contrastive learning Object detection

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Polarization-independent resonant lattice Kerker effect in phase-change metasurface

arXiv

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arXiv 2022年

作者： Xiong, Lei Luo, Xiaoqing Ding, Hongwei Lu, Yuanfu Li, Guangyuan School of Information Science and Engineering Yunnan University Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

Resonant lattice Kerker effect in periodic resonators is one of the most interesting generalizations of the Kerker effect that relates to various fascinating functionalities such as scattering management and Huygens metasurfaces. However, so far this effect has been shown to be sensitive to the incident polarization, restricting its applications. Here, we report, for the first time, polarization-independent resonant lattice Kerker effect in metasurfaces composed of periodic Ge2Se2Te5(GST) disks. For such a metasurface of square lattice, the spectrally overlap of the electric dipole and magnetic dipole surface lattice resonances can be realized by choosing an appropriate GST crystalline fraction regardless of the incident polarization. The operation wavelength and the required GST crystalline fraction can be conveniently tuned over large ranges since these parameters scale linearly with the disk size and the lattice period, greatly facilitating the design. Making use of the obtained resonant lattice Kerker effect, we realize a reconfigurable and polarization-independent lattice Huygens' metasurface with a dynamic phase modulation of close to 2π and high transmittance. This work will advance the engineering of the resonant lattice Kerker effect and promote its applications in phase modulation and wavefront control. Copyright © 2022, The Authors. All rights reserved.

关键词： Tellurium compounds

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Design of A Highly Reliable and Low-Power SRAM With Double-Node Upset Recovery for Safety-critical Applications

Design of A Highly Reliable and Low-Power SRAM With Double-N...

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International Test Conference in Asia (ITC-Asia)

作者： Aibin Yan Jing Xiang Zhengfeng Huang Tianming Ni Jie Cui Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits Anhui Polytechnic University Wuhu China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

For high-speed operations, low power consumption and small silicon area, transistors are being scaled aggressively. Meanwhile, circuit reliability is facing greater challenges in advanced technologies. In this paper, a highly reliable and low-power SRAM with double-node-upset (DNU) recovery, namely HRLP16T, is proposed for safety-critical fields. HRLP16T can recover from single-node-upset (SNU) at all the sensitive nodes, and it has eight node pairs recoverable from DNUs. Simulation results demonstrate its advantages in terms of delay and power consumption over typical existing SRAM cell designs.

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A Low Overhead and Double-Node-Upset Self-Recoverable Latch

A Low Overhead and Double-Node-Upset Self-Recoverable Latch

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International Test Conference in Asia (ITC-Asia)

作者： Aibin Yan Fan Xia Tianming Ni Jie Cui Zhengfeng Huang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits Anhui Polytechnic University Wuhu China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

With the rapid advancement of semiconductor technologies, integrated circuits, especially storage elements (e.g., latches) have become increasingly vulnerable to soft errors. In order to effectively tolerate double-node-upsets (DNUs) caused by radiation and reduce the power and area of latches, this paper proposes a DNU self-recoverable latch with low overhead in terms of power and area. The proposed latch mainly comprises seven 2-input C-elements and two inverters to achieve DNU self-recovery. Simulation results show that the proposed latch can recover from all possible DNUs and that it can reduce delay by 45.7%, power by 29.1%, area by 65.9%, and area-power-delay-product by 87.4%, on average, compared to typical existing DNU self-recoverable latches.

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CAD and robotics in Architecture and Construction 1

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1000年

作者： A. Bijl R. Krishnamurti O. Akin C.-C. Chen B. Dave S. Pithavadian G. Schmitt J.-C. Robert J. Weeks U. Flemming R. Coyne T. Glavin Y. E. Kalay A. C. Harfmann L. M. Swerdloff M. Rychener L. Koskela R. Hynynen M. Kallavuo K. Kahkönen J. Salokivi A. H. Bridges A. Polistina W. L. Whittaker E. Bandari B. Motazed Y. Hasegawa C. Abel A. H. Slocum R. Kangari M.-C. Wanner M. Skibniewski P. Derrington C. Hendrickson R. F. Woodbury W. T. Keirouz I. J. Oppenheim D. R. Rehak C. F. Earl N. Kano J. L. Crowley P. J. Drazan H.-R. Oeser N. Tanaka M. Saito K. Arai K. Banno T. Ochi S. Kikuchi T. Ueno J. Maeda T. Yoshida S. Suzuki

ISBN: (数字)9781468474046

ISBN: (纸本)9781850912538

After two decades, data processing has finally, and probably forever, found its niche among civil engineering and construction (CEC) professionnals, through word processors, digitizing tables, management software, and increasingly via drawing software and computer-aided design (CAD), recently, robots have even started invading work sites. What are the main trends of CAD and robotics in the field of architecture and civil enginee ring? What type of R&D effort do university and industrial laboratories undertake to devise the professional software that will be on the market in the next three to five years? These are the issues which will be addressed during this symposium. To this effect, we have planned concurrently an equipment and software show, as well as a twofold conference. Robotic is just starting in the field of civil engineering and construction. A pioneer, the Civil engineering Departement of Carnegie-Mellon University, in the United States, organized the first two international symposia, in 1984 and 1985 in Pittsburgh. This is the third meeting on the subject (this year, however, we have also included CAD). It constitutes the first large international symposium where CAD experts, specialists in architecture and CEC robotics will meet. From this standpoint, it should be an ideal forum for exchanging views and expe riences on a wide range of topics, and we hope it will give rise to novel applications and new syntheses. This symposium is intented for scientists, teachers, students and also for manufacturers and all CEC professionals.

关键词： robotics and Automation science, Humanities and Social sciences, multidisciplinary

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Driving-Torque Self-Adjusted Triboelectric Nanogenerator for Effective Harvesting of Random Wind Energy

SSRN

引用

SSRN 2022年

作者： Wang, Yuqi Li, Xiang Yu, Xin Zhu, Jianyang Shen, Ping Wang, Zhong Lin Cheng, Tinghai Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing101400 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau Macao999078 China School of Materials Science and Engineering Georgia Institute of Technology AtlantaGA30332-0245 United States

Triboelectric nanogenerators (TENGs), as a new energy technology for distributed power, are used widely in the field of the natural environment energy harvesting. Because the natural energy is random and unstable, dynamic matching between TENGs driving-torque and natural environment energy is fundamental for improving the applications of TENGs. Therefore, a driving-torque self-adjusted triboelectric nanogenerator (SA-TENG) for effective harvesting of random wind energy is developed in this paper. When the external wind speed is unstable, the SA-TENG automatically self-adjusted its driving-torque to dynamically match the wind speed and obtain higher output power. Experiments showed that the SA-TENG can adjust its driving-torque in accordance to the wind speed ranging in 5.0–13.2 m/s, and that, the output peak power can reach 7.69 mW. Under the same conditions, in comparison with a normal TENG, the power growth rate and the highest energy conversion efficiency of the SA-TENG were boosted by more than 4.3 and 12.2 times, respectively;values that are also 3.2 and 6.5 times higher, respectively, than those of an electromagnetic generator. Additionally, the SA-TENG can supply power to sensors for monitoring environment, proving its feasibility as a distributed energy source. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

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Gesture recognition based on sparse representation

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International Journal of Wireless and Mobile Computing 2016年第4期11卷 348-356页

作者： Miao, Wei Li, Gongfa Sun, Ying Jiang, Guozhang Kong, Jianyi Liu, Honghai College of Machinery and Automation Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Aiming at the problem that the robustness of gesture recognition is difficult to guarantee, this paper presents a method based on multi-features and sparse representation. Hu invariant moments and HOG features of training samples are extracted in training phase. The K-SVD algorithm is used to train the initial value of dictionary formed by two features so as to obtain two sub-dictionaries. In recognition phase, sparse coefficients of corresponding training dictionary are derived by solving minimum l1-norm. Finally, the overall reconstruction error is calculated to judge the categories of test samples. In experimental simulation, five kinds of grasp gesture are collected to create gesture sample library. After selecting optimal HOG parameters and the weight of two features, the recognition effect of the method is analysed. Compared with the commonly used classification, the results show that the method has better recognition rate and robustness. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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Achieving consensus in spite of stubbornness: time-varying concatenated Friedkin-Johnsen models

Achieving consensus in spite of stubbornness: time-varying c...

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IEEE Conference on Decision and Control

作者： L. Wang C. Bernardo Y. Hong F. Vasca G. Shi C. Altafini Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Group for Research on Automatic Control Engineering University of Sannio Benevento Italy Tongji University Shanghai China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Sydney Division of Automatic Control Linköping University Linköping Sweden

ISBN: (纸本)9781665436601

A concatenated Friedkin-Johnsen (FJ) model is a two time-scale opinion dynamics model in which stubborn agents discuss a sequence of issues. For each issue, a FJ model is adopted, and concatenation refers to the fact that the final opinion of the agents at issue s becomes the initial condition at issue s + 1. In this paper we deal with the case in which the system is open, i.e., the group of interacting agents changes at each issue, and so does their stubbornness. A concatenated FJ model can in this case be represented as an infinite product of stochastic matrices. For such system, we obtain sufficient conditions under which the opinions of the agents converge to consensus.

关键词： Sufficient conditions Conferences Stochastic processes

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Robot Embodied Dynamic Tactile Perception of Liquid in Containers

Robot Embodied Dynamic Tactile Perception of Liquid in Conta...

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IEEE-RAS International Conference on Humanoid Robots

作者： Yingtian Xu Yichen Ma Waner Lin Zhenglong Sun Tianwei Zhang Ziya Wang School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education School of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China Department of Micro-Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Humans perceive objects and environments actively and dynamically using tactile sensing facilitated by the skin. However, due to the gap in sensing capabilities between electronic skin and human skin, it remains a challenge for robots to achieve intricate tactile perception. For example, the task of liquid properties estimation within containers, demands sensing and comprehension of complex dynamic tactile signals during contact. This paper introduces a novel tactile fingertip inspired by human skin enabling both static tactile sensing facilitated by a layer of porous piezoresistive elastomer and dynamic tactile sensing enabled by condenser microphones. A data-driven methodology is employed to analyze liquids through multimodal signals devoid of physical modelling. The proposed robotic system engages in shaking various bottles filled with different volumes of water, allowing the tactile fingertip to detect changes in grasping force caused by shaking and small vibrations caused by the collision of the container with liquid. Five machine learning models trained on these tactile signals are analyzed and compared. Experimental results demonstrate the proficiency of the tactile system in distinguishing liquid fill percentages across bottles of different shapes and capacities with a remarkable accuracy of $\mathbf{9 8 \%}$. This paper holds promise for advancing the embodied intelligence of robots, enhancing their ability to perceive mixed tactile signals dynamically, thereby facilitating tasks such as liquid estimation and other intricate tactile operations in environments like kitchens and hospitals.

关键词： Vibrations Liquids Force Dynamics Piezoelectric transducers Machine learning Containers Robot sensing systems Sensors Robots

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