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A hybrid wind and raindrop energy harvesting operating on Savonius turbine

NANO ENERGY 2024年 127卷

作者： Sou, Kit Wa Lin, Zenan Jin, Yuchao Ping, Siqi Ding, Wenbo Wang, Jiyu Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

In offshore environments, abundant wind and rainfall resources await development, representing significant untapped energy potential for powering internet of Things (IoT) devices. Triboelectric Nanogenerators (TENGs) are particularly suited for capturing continuous motion in offshore environments, presenting a reliable solution for sustainable energy generation in IoT applications. Herein, we propose a wind-droplet hybrid generator (WDHG) tailored for adaptive environmental energy harvesting, explicitly targeting the demanding conditions prevalent in offshore environments. The WDHG combines the efficiency of a helical Savonius TENG with the versatility of an interdigitated electrode TENG, enabling simultaneous wind and rain energy harvesting. The helical Savonius TENG exhibits superior aerodynamic performance, producing an output power ranging from 0.39 to 2.10 mW with wind speeds increasing from 0.75 to 6.84 m/s. Additionally, the droplet TENG generates a power output of 35.90 mW/m2 under rainfall at 70 mm/min. Optimizations include not only the comparative analysis of helical Savonius turbines and the variation in pole-pair numbers but also adjustments in fluorinated ethylene propylene (FEP) film thickness and electrode gap length for interdigitated electrode, alongside the development of a tailored power management circuit (PMC) featuring dual bridge rectifiers, a buck circuit module, and a smart load switch. The PMC enables continuous operation of a low-powered digital thermohygrometer under conditions of 4.5 m/s wind speed, and drives a 20-second wake BLE server under 7.2 m/s wind speeds. integration of the WDHG with offshore wind and raindrop extends energy harvesting capabilities, exhibiting a great potential of providing reliable power source for IoT devices.

关键词： Wind energy harvesting Droplet energy harvesting Power management circuit Triboelectric nanogenerator

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CircusTent: A Tool for Measuring the Performance of Atomic Memory Operations on Emerging Architectures 8th

CircusTent: A Tool for Measuring the Performance of Atomic M...

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8th Workshop on openSHMEM and Related Technologies (openSHMEM)

作者： Williams, Brody Leidel, John D. Wang, Xi Donofrio, David Chen, Yong Texas Tech Univ Lubbock TX 79409 USA Tact Comp Labs Muenster TX USA RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9783031048883;9783031048876

Endeavors to engineer the next generation of exascale platforms have resulted in a fundamental shift in system architectures. Orthogonal to what was once considered conventional wisdom, high performance systems designed today are characterized by heterogeneous architectures wherein distinct components are carefully combined in order to optimize system performance and energy efficiency. One unintended consequence of this new paradigm is an increasingly complex memory hierarchy that frequently spans multiple devices and may be composed of disparate memory types. Unfortunately, the effect on performance of this new memory model is not well understood. Moreover, a quantifiable, system-agnostic methodology capable of assessing the performance of the diverse memory subsystems within emerging architectures has yet to be introduced. The CircusTent benchmark suite has been introduced to fill this void by measuring system performance with respect to atomic memory operations using established parallel programming models. However, a detailed description and evaluation of CircusTent in a distributed memory environment, critical to both current and future system architectures, has yet to be produced. In this work, we rectify this shortcoming by introducing CircusTent implementations based on the openSHMEM and MPI programming models and evaluating these implementations across a variety of platforms. We then detail our conclusions and characterize our observations regarding the effect of different system interconnects, memory hierarchies, and instruction set architectures on system performance.

关键词： Benchmark Atomic memory operations openSHMEM MPI Performance

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Poster Abstract: TENG-enabled Self-powered Human-machine interfaces for the Metaverse 22

Poster Abstract: TENG-enabled Self-powered Human-machine Int...

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22nd ACM/IEEE international Conference on Information Processing in Sensor Networks (IPSN)

作者： Wang, Haoyang Man, Fanhang Wang, Zihan Liu, Yuxuan Chen, Xinlei Dingt, Wenbo Tsinghua Univ Shenzhen Int Grad Sch RISC V Int Open Source Lab Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China

ISBN: (纸本)9798400701184

Human-machine interface (HMI) of high degrees of freedom (DoF) is one of the most critical bases of the metaverse. The ideal HMI for the metaverse should be cheap, robust, customizable, and ergonomically friendly. In light of this, we propose a triboelectric nanogenerator (TENG)-based sensing system. We developed a low-cost, soft, light, and customizable TENG sensor to collect data from the human body. We then used an artificial neural network (ANN) to obtain the corresponding human motion from collected sensory data. The effectiveness of the proposed system is demonstrated with experiments of a working prototype.

关键词： Triboelectric nanogenerator Human-machine interface Metaverse

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M³ARL: MOMENT-EMBEDDED MEAN-FIELD MULTI-AGENT REINFORCEMENT LEARNING FOR CONTINUOUS ACTION SPACE 49

M<SUP>3</SUP>ARL: MOMENT-EMBEDDED MEAN-FIELD MULTI-AGENT REI...

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49th IEEE international Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tang, Huaze Hu, Yuanquan Zhao, Fanfan Yan, Junji Dong, Ting Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Inst Data & Informat Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Mean-field theory offers a promising solution to the scalability issues encountered in multi-agent reinforcement learning (MARL) within large-scale systems. However, most existing MARL algorithms based on mean-field theory are typically constrained to discrete action space. In continuous action space, the conventional mean-field approximation of meanfield theory breaks down since the representation of mean-field action is not well-defined. In this paper, we propose Moment-Embedded Mean-Field Multi-Agent Reinforcement Learning (M3ARL) for continuous action space, embedding mean-field action into multi-order moments. Specifically, we analyze the mean-field approximation on Wasserstein space and derive the approximated form of Q-function. Furthermore, to derive a learn-able form of Q-function, we apply the cylindrical function and propose the moment-embedded representations. Based on the representations and proximal policy optimization (PPO) algorithm, we propose a novel algorithm, M(3)APPO. Finally, we validate the efficacy of the proposed algorithm through experiments of predator-prey environment on continuous action space scenarios.

关键词： Deep Learning Techniques Multi-Agent Reinforcement Learning Mean Field Theory

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Planar Magnetic Actuation for Soft and Rigid Robots Using a Scalable Electromagnet Array

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9264-9270页

作者： Li, Xiaosa Lu, Chengyue Song, Ziwu Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China RISC Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Magnetic actuation system manipulates micro soft or rigid robots by a controllable magnetic field to move them freely in the narrow or enclosed space, which has demonstrated its huge potential in medical interventional surgery and drug delivery. However, the limited working space of paired or area-centered electromagnets restricts its practical applications. In this paper, we propose a convenient coils drive scheme for the scalable electromagnet array, and present an efficient planar magnetic actuation system with a spacious workspace. During the actuation process, our system activates selectively the effective electromagnets neighboring to the magnetic robot by coil selectors, and generates an alternating magnetic field with sufficient gradients to guide the robot's orientation and position. For the soft magnetic pipe, our system can push it to perform the continuous deflections around the stand columns on the plane. For the rigid magnetic cube, the designed magnetic-quadrupole structure allows it to receive various forces from different directions, and achieve a stable displacement in the heterogeneous magnetic field.

关键词： Motion and path planning software-hardware integration for robot systems telerobotics and teleoperation

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Scheduling UAV Swarm with Attention-based Graph Reinforcement Learning for Ground-to-air Heterogeneous Data Communication

Scheduling UAV Swarm with Attention-based Graph Reinforcemen...

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

作者： Ren, Jiyuan Xu, Yanggang Li, Zuxin Hong, Chaopeng Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tsinghua Univ Shenzhen Key Lab Ecol Remediat & Carbon Sequestra Inst Environm & Ecol Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Tsinghua Univ Shenzhen Int Grad Sch Pengcheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

In disaster scenarios, unmanned aerial vehicles (UAVs) can serve as mobile base stations because of their maneuverability and synergy. However, due to constrained UAV communication capabilities and limited battery life, UAV base stations resource allocation for mobile sensors in a data-heterogeneous environment is a significant challenge when optimizing communication quality. To address this, we propose AGUZero, an attention-based graph reinforcement learning (RL) framework. Inspired by MuZero [27], AGUZero is designed to handle dynamic and uncontrollable environments based on Monte Carlo Tree Search (MCTS). Additionally, to tackle data heterogeneity, AGUZero represents the states using heterogeneous sub-graphs and employs an attention-based model to capture relationships among UAVs and sensors. The experimental results show that AGUZero outperforms other baseline models consistently when either the number of UAVs or the number of sensors is varying. AGUZero improves the data transmission ratio by 11.03% and 10.35% in the two cases respectively.

关键词： Disaster Response Path Planning Graph Neural Network Model-based Reinforcement Learning

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

HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control

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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

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SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning

SmoothLander: A Quadrotor Landing Control System with Smooth...

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

作者： Zhao, Chenyu Wang, Haoyang Li, Jiaqi Man, Fanhang Mu, Shilong Ding, Wenbo Zhang, Xiaoping Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Pengcheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Shenzhen Peoples R China

ISBN: (纸本)9798400702006

The landing process of the quadrotors can be affected by the disturbance from the ground effect when approaching the landing surface. Such a disturbance significantly increases the chances of collision and jittering of the quadrotors, thereby posing threats to the safety of both the quadrotors and the mounted equipment. In light of this, we propose SmoothLander, an aerodynamics and reinforcement learning-based control system to stabilize the quadrotors under the influence of the ground effect and control noise. First, we design a landing trajectory for the quadrotor in accordance with aerodynamics. Then we design a reinforcement learning-based command generator to effectively optimize the quadrotor's landing behavior. We evaluate our control system through physical feature-based simulation and in-field experiments. The results show that our method can enable the quadrotor to land more smoothly and stably against control noise than the baseline.

关键词： Quadrotor Ground Effect Reinforcement Learning

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FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第4期35卷 592-603页

作者： Yang, Meilin Xu, Jian Ding, Wenbo Liu, Yang Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing 100190 Peoples R China V Int Open Source Lab RISC Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 201210 Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing 100190 Peoples R China

Deep hashing has been widely applied in large-scale data retrieval due to its superior retrieval efficiency and low storage cost. However, data are often scattered in data silos with privacy concerns, so performing centralized data storage and retrieval is not always possible. Leveraging the approach of federated learning (FL) to perform deep hashing is a recent research trend. However, existing frameworks mostly rely on the aggregation of the local deep hashing models, which are trained by performing similarity learning with local skewed data only. Therefore, they cannot work well for non-IID clients in a real federated environment. To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train a shared deep hashing model by leveraging the class-wise prototypical hash codes. Globally, sharing global prototypes with only one prototypical hash code per class assists in learning consistent code distributions across clients while minimizing the cost of communication and privacy. Locally, the use of the global prototypical hash codes are maximized by jointly training a discriminator network and the local hashing network. Extensive experiments on benchmark datasets are conducted to demonstrate that our method can significantly improve the performance of the deep hashing model in the federated environments with non-IID data distributions.

关键词： Federated learning information retrieval deep hashing

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Physical Layer Security Based on 2-D Spatial Position Optimization of Relays in Reflected Indoor VLC System

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2235-2239页

作者： Wu, Kun Dong, Yuhan Yu, Xinchun Song, Jian Zhang, Xiao-Ping Tsinghua Univ Shenzhen Key Lab Ubiquitous Data Enabling RISC V Internat Open Source Lab Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

A novel relay-aided physical layer security scheme is proposed in this letter. The channel model incorporates partial reflection components in the received signal according to the signal power. With this channel model, we formulate three security capacity maximization problems based on three relay-aided scenarios: cooperative jamming, decode-and-forward, and amplify-and-forward. By using an iterative algorithm, we jointly optimize the beamforming vector and two-dimensional spatial positions of relays based on the formulated problems to maximize the sum data rate while defending against eavesdropping. Numerical results show that the sum data rate for legal receivers can be improved by applying the proposed scheme.

关键词： Receivers Relays Security interference Law Signal to noise ratio Vectors Visible light communication trusted cooperative relay physical layer security superposition coding

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