检索结果-内蒙古大学图书馆

Flexible nanogenerators for intelligent robotics: design, manufacturing, and applications

intERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第2期7卷 022012-022012页

作者： Zhao, Hongfa Ruan, Liguang Wang, Zihan Shu, Mingrui Lyu, Chuqiao Lakshan, Bulathsinghala Rameesh Ishara, Dharmasena Wu, Changsheng Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU England Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore RISC V Int Open Source Lab Beijing 518055 Peoples R China

The embodied artificial intelligence (EAI) is driving a significant transformation in robotics, enhancing their autonomy, efficiency and evolution ability. In this rapidly evolving technological landscape, robots need numerous sensors to realize high levels of perception, precision, safety, adaptability, and intelligence. Triboelectric and piezoelectric sensors address these needs by providing high sensitivity, flexibility, and the capability of self-powered sensing, leveraging the revolutionary nature of nanogenerators to convert mechanical energy into electrical energy on basis of Maxwell's displacement current. These sensors surpass externally powered passive sensors by offering continuous operation, reduced maintenance, and the capability to function in remote or harsh environments. The integration of EAI with advanced nanogenerators sensors could position robotics to perform autonomously, efficiently, and safely, paving the way for innovative applications in various domains such as industrial automation, environmental monitoring, healthcare, and smart homes. In this paper, the fundamental theories, design, manufacturing, and applications of nanogenerators are comprehensively reviewed as a foundation of the advanced sensors for intelligent robotics in the new era, with three major application fields: sensing (including human-robot interaction, exteroceptive sensing and proprioceptive sensing), computing and actuating. Perspectives are addressed for nanogenerators systems in future development. Fundamentals of TENGs and PENGs are *** design of materials and structures for nanogenerators is *** techniques for nanogenerators are *** for robotic sensing, computing, and actuating systems are demonstrated.

关键词： triboelectric/piezoelectric nanogenerators robotics manufacturing

来源：评论

学校读者我要写书评

暂无评论

Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in Vehicular Networks

引用

IEEE intERNET OF THINGS JOURNAL 2023年第3期10卷 2667-2679页

作者： Zhang, Hengxi Lu, Chengyue Tang, Huaze Wei, Xiaoli Liang, Le Cheng, Ling Ding, Wenbo Han, Zhu Tsinghua Univ Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity O(n(N)) with n denoting the dimension of action space and N denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to O(n(2)) while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

关键词： Joint resource allocation mean-field game (MFG) theory multiagent reinforcement learning (MARL) vehicular networks

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net 96

Heterogeneous Mean-Field Multi-Agent Reinforcement Learning ...

引用

IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Zhang, Hengxi Tang, Huaze Hu, Yuanquan Wei, Xiaoli Wu, Chenye Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781665454681

The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAVs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.

关键词： SAGI-Net heterogeneous mean field MARL communication routing selection computational complexity

来源：评论

学校读者我要写书评

暂无评论

HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control 21

HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control

引用

ACM international Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Yang, Xu Yin, Jihong Wang, Zihan Song, Ziwu Song, Jian Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst RISC Int Open Source Lab Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781450384612

The development of the human-machine interface (HMI) is endeavored to find effective approaches to interact with machines by applying emerging technologies. Triboelectric nanogenerator (TENG) can convert mechanical stimuli to electricity, which not only shows great potential in sensing but also is widely used in various HMI applications. This paper proposed a TENG-based hexagon-fractal touchpad (HTPad) system using two channels to realize 18 sliding patterns from 3 different modes and a signal recognition module. A one-dimensional convolution neural network (1D CNN) model is proposed for the recognition of the sliding direction signal with 96.5% accuracy, and handwriting digit signals collected by the touchpad can be recognized with a modified model with 99% accuracy. The proposed TENG-based hexagon-fractal touchpad is easy to fabricate, scalable, and with high sensitivity. Furthermore, the recognition model can serve as a unified platform for different *** tasks with little computational cost, which reveals great potential in HMI applications.

关键词： Triboelectric sensor deep learning human machine interface

来源：评论

学校读者我要写书评

暂无评论

A triboelectric gait sensor system for human activity recognition and user identification

引用

NANO ENERGY 2023年 112卷

作者： Li, Jiarong Xie, Zixuan Wang, Zihan Lin, Zenan Lu, Chengyue Zhao, Zihao Jin, Yuchao Yin, Jihong Mu, Shilong Zhang, Chaobo Gui, Weihua Liang, Xiaojun Wang, Jiyu Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Cent S Univ Sch Automat Changsha 410083 Peoples R China

Floor-based sensing systems to monitor human activities and identify users are essential for smart homes and intelligent buildings. A low-cost, easy-to-fabricate, and flexible gait sensor system based on triboelectric nanogenerator (TENG) is presented in this paper, which can transform gait movements, even in the low-frequency form, into electrical impulses without an external power source. To realize this, a TENG-based gait sensor unit with an optimized structure design is proposed to enhance the sensing sensitivity. A sensing insole module is formed by arranging the sensor units according to the foot pressure distribution. The sensor distribution is then explored and improved by comparative studies of gait recognition performance, which increases the recognition efficiency and the possible application in edge computing scenarios. Furthermore, a deep learning network is developed based on long short-term memory (LSTM) and residual units to extract deep features from multichannel time-series gait data to boost recognition performance. Experimental results demonstrate that the proposed gait sensor system can be utilized for human activity recognition and user identification with accuracies of 97.9 % and 99.4 %, respectively. Finally, a gait-sensing-based fitness exercise monitoring system is constructed that can estimate calorie expenditure and distinguish between standard and non-standard fitness activities with an accuracy of 97.2 %. This work can be extended to various application scenarios such as security surveillance, health monitoring, and intelligent control, which provides a new ubiquitous self-powered sensing solution for the internet of Things (IoT).

关键词： Triboelectric nanogenerator Self-powered sensor Gait recognition system Deep learning network Activity monitoring User identification

来源：评论

学校读者我要写书评

暂无评论

TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification 21

TriboGait: A deep learning enabled triboelectric gait sensor...

引用

ACM international Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Li, Jiarong Wang, Zihan Zhao, Zihao Jin, Yuchao Yin, Jihong Huang, Shao-Lun Wang, Jiyu Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst RISC V Int Open Source Lab Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781450384612

Accurately and continuously measuring and collecting data on human gait is critical for human activity recognition and individual identification, enabling various applications in smart homes/buildings, including security authentication, personal healthcare, and intelligent automation. Many sensing technologies have been investigated by researchers recently, such as camera-based, laser-based, and mobile approaches, which have limitations in particular sensing situations, such as environments with fewer privacy concerns, line-of-sight, and the use of wearables, etc. On the other hand, the floor with the embedded sensor is stable and robust to different circumstances, enabling non-intrusive gait recognition and human identification. Therefore, a triboelectric nanogenerator (TENG)-based gait sensor system installed on the floor is proposed in this paper. Our approach has many advantages in comparison to the existing gait recognition systems, including low cost, simple fabrication, lightweight, and high durability. The TENG-based sensors can be simply embedded into a smart carpet to discern mechanical motions through electrical signals. Furthermore, a deep learning model, deep residual bidirectional long short-term memory network with dense layers (Residual Dense-BiLSTM), is proposed for multichannel floor-based gait recognition. By utilizing this model to analyze the electrical outputs, our system can accurately detect various human activities and distinguish different individuals' walking patterns, with a recognition rate over 98% and 97%, respectively. We conclude that the proposed deep learning enabled triboelectric gait sensor system has broad applications in security and healthcare.

关键词： floor sensor deep learning activity recognition human identification

来源：评论

学校读者我要写书评

暂无评论

Reprint of: Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications

引用

DIGITAL SIGNAL PROCESSING 2022年 125卷

作者： Wang, Zihan Li, Jiarong Jin, Yuchao Wang, Jiyu Yang, Fang Li, Gang Ni, Xiaoyue Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA Tsinghua Univ RISC V Int Open Source Lab Beijing 518055 Peoples R China

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as "cross-sensing " and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Wearable technologies Ubiquitous sensing Human-machine interaction Health and safety Security

来源：评论

学校读者我要写书评

暂无评论

Triboelectric-nanogenerator-enabled mechanical modulation for infrared wireless communications

引用

ENERGY & ENVIRONMENTAL SCIENCE 2022年第7期15卷 2983-2991页

作者： Wang, Zihan Jin, Yuchao Lu, Chengyue Wang, Jiyu Song, Ziwu Yang, Xu Cao, Yidan Zi, Yunlong Wang, Zhong Lin Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Inst Mat Res Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shun Hing Inst Adv Engn Hong Kong Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Technol Beijing 100049 Peoples R China CUSTech Inst Technol Wenzhou 325024 Zhejiang Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Optical wireless communications (OWC), featuring a wide optical bandwidth and robust resistance to radio-frequency interference (RFI), can benefit the development of the internet of Things (IoT). Making IoT devices ubiquitous calls for decentralized and on-demand power sources for wireless communications. However, existing visible-light-based self-powered OWC systems are complex on the receiver side, they are easily affected by ambient light, and they can disturb dark environments. In this paper, we realized self-powered infrared (IR) wireless communications via integrating a triboelectric nanogenerator (TENG) and an IR transmitter. With customized mechanical modulation protocols, both the frequency and amplitude features of the IR signal, enabled by different mechanical motion or mechanical structures of TENG devices, can be leveraged for conveying information. The concept of this TENG-enabled wireless infrared communications framework and the results from this study may pave the way for a new generation of self-powered communications in a sustainable manner.

关键词： Radio interference

来源：评论

学校读者我要写书评

暂无评论

A platypus-inspired electro-mechanosensory finger for remote control and tactile sensing

引用

NANO ENERGY 2023年 116卷

作者： Mu, Shilong Li, Shoujie Zhao, Hongfa Wang, Zihan Xiao, Xiao Xiao, Xiao Lin, Zenan Song, Ziwu Tang, Huaze Xu, Qinghao Wang, Dongkai Lee, Wang Wei Wu, Changsheng Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Natl Univ Singapore Inst Hlth Innovat & Technol Singapore 117599 Singapore Natl Univ Singapore SIA NUS Digital Aviat Corp Lab Singapore 117602 Singapore Tencent Robot X Shenzhen 518054 Peoples R China Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Advancement in human-robot interaction (HRI) is essential for the development of intelligent robots, but there lack paradigms to integrate remote control and tactile sensing for an ideal HRI. In this study, inspired by the platypus beak sense, we propose a bionic electro-mechanosensory finger (EM-Finger) synergizing triboelectric and visuotactile sensing for remote control and tactile perception. A triboelectric sensor array made of a patterned liquid-metal-polymer conductive (LMPC) layer encodes both touchless and tactile interactions with external objects into voltage signals in the air, and responds to electrical stimuli underwater for amphibious wireless communication. Besides, a three-dimensional finger-shaped visuotactile sensing system with the same LMPC layer as a reflector measures contact-induced deformation through marker detection and tracking methods. A bioinspired bimodal deep learning algorithm implements data fusion of triboelectric and visuotactile signals and achieves the classification of 18 common material types under varying contact forces with an accuracy of 94.4 %. The amphibious wireless communication capability of the triboelectric sensor array enables touchless HRI in the air and underwater, even in the presence of obstacles, while the whole system realizes high resolution tactile sensing. By naturally integrating remote contorl and tactile sensing, the proposed EM-Finger could pave the way for enhanced HRI in machine intelligence.

关键词： Bionic sensor Triboelectric nanogenerator Visuotactile sensor Touchless sensing Tactile sensing Human-robot interaction

来源：评论

学校读者我要写书评

暂无评论

A Highly Sensitive Triboelectric Vibration Sensor for Machinery Condition Monitoring

引用

ADVANCED ENERGY MATERIALS 2022年第37期12卷 2201132-2201132页

作者： Zhao, Hongfa Shu, Mingrui Ai, Zihao Lou, Zirui Sou, Kit Wa Lu, Chengyue Jin, Yuchao Wang, Zihan Wang, Jiyu Wu, Changsheng Cao, Yidan Xu, Xiaomin Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Marine IntelliSense & Computat Southern Marine Sci & Engn Guangdong Lab Guangzho Shenzhen 518055 Peoples R China Natl Univ Singapore Dept Mat Sci & Engn 9 Engn Dr 1 Singapore 117575 Singapore Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Inst Mat Res Shenzhen Geim Graphene Ctr Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Vibration sensors are involved extensively in a variety of applications. Especially in the era of the internet of Things, developing self-powered vibration sensors has become a very meaningful yet challenging problem. This study investigates a highly sensitive self-powered vibration sensor based on the triboelectric nanogenerator (VS-TENG) for machinery condition monitoring. By introducing a stacked structure comprising foamed aluminum, and a fluorinated ethylene propylene film with gold-plated electrode protected by two indium tin oxid layers, The VS-TENG can detect the vibrations with frequencies ranging from 1 to 2000 Hz and can detect vibrations of low amplitude (approximately sub-mu m, by calculation). The output performance and characteristics of the TENG under various vibration frequencies, accelerations, and amplitudes are analyzed systematically. The VS-TENG is successfully used to monitor the operating conditions of mechanical gear systems, reaching a recognition accuracy of 99.78%. Furthermore, it can also be utilized for vibration detection in other areas such as the air compressor, heat gun, hollow tile recognition, etc., which means a solid progress toward the practical applications of TENGs in self-powered vibration detection.

关键词： machinery condition monitoring self-powered sensors triboelectric nanogenerators vibration

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：