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Soft Ball-Based Triboelectric-Electromagnetic Hybrid Nanogenerators for Wave Energy Harvesting

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SSRN

SSRN 2022年

作者： Cao, Changyong Pang, Yaokun Su, Jiaji Fang, Yuhui Wang, Huigang Tan, Yeqiang State Key Laboratory of Bio-Fibers and Eco-Textiles Collaborative Innovation Center of Marine Biobased Fiber and Ecological Textile Technology Institute of Marine Biobased Materials School of Materials Science and Engineering Qingdao University Qingdao266071 China Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States 4D Maker LLC OkemosMI48864 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

Triboelectric nanogenerator (TENG), as an emerging energy harvesting technology, provides a new cost-effective strategy to harvest water wave energy due to the broad selections of triboelectric materials and the high efficiency in low-frequency energy harvesting. Here, we present a novel triboelectric-electromagnetic hybrid nanogenerator (TEHG) consisting of a soft ball-based triboelectric nanogenerator (SB-TENG) and an electromagnetic generator (EMG) for effectively harvesting water wave energy. The moving soft balls are utilized as one triboelectric layer to increase the contact area with the copper electrodes, and a multilayered structural design is employed to maximize the device's available space. The critical parameters that may affect the energy harvesting performance of the SB-TENG are investigated, including the type of the filled liquids, the thickness of the silicone shell, the number of layers, and the properties of the soft balls. Further, we characterize the output performance of the new TEHG under two basic working modes: translational mode and swing mode, at various frequencies, amplitudes, and orientation angles. Under an operating frequency of 1 Hz, the SB-TENG and the EMG can reach a maximum output peak power of 0.5 mW and 8.5 mW, respectively. The TEHG has been demonstrated to be capable of powering dozens of LEDs and driving a digital temperature sensor for long-term monitoring of the water temperature. This study not only provides a novel design and approach to improve the output performance of TENGs, but also presents a great prospect for building a self-powered water sensing system driven by low-frequency water waves. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

Many-body spectral statistics of relativistic quantum billiard systems

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Physical Review Research 2023年第1期5卷 013050-013050页

作者： Xianzhang Chen Zhen-Qi Chen Liang Huang Celso Grebogi Ying-Cheng Lai Lanzhou Center for Theoretical Physics and Key Laboratory for Magnetism and Magnetic Materials of MOE Lanzhou University Lanzhou Gansu 730000 China Université de Strasbourg CNRS Institut de Physique et Chimie des Matériaux de Strasbourg UMR 7504 F-67000 Strasbourg France Institute for Complex Systems and Mathematical Biology Kingdom College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

In the field of quantum chaos, spectral statistics is one of the most extensively investigated characteristics. Despite a large body of existing literature, the effects of many-body interactions on the spectral statistics of relativistic quantum systems remain poorly understood. Treating electron-electron interactions through the one-orbital mean-field Hubbard model, we address this fundamental issue using graphene billiards with the geometric shape of a circular sector as prototypical systems. Our approach is to consider the two characteristically different cases where the statistics are Poisson and Gaussian orthogonal ensemble (GOE) so the corresponding classical dynamics are typically integrable and chaotic, respectively, and to systematically investigate how the statistics change as the Hubbard interaction strength increases from zero. We find that, for energies near the Dirac point, the Hubbard interactions have a significant effect on the spectral statistics. Regardless of the type of spectral statistics to begin with, increasing the Hubbard interaction strength up to a critical value causes the statistics to approach GOE, rendering more applicable the random matrix theory. As the interaction strength increases beyond the critical value, the statistics evolve toward Poisson, due to the emergence of an energy gap rendering the quasiparticles massive. We also find that the energy levels above and below the Dirac point can exhibit different statistics, and the many-body interactions have little effect on the statistics for levels far from the Dirac point. These results reveal the intriguing interplay between many-body interactions and the spectral statistics, which we develop a physical picture to understand.

关键词： Quantum chaos Graphene Quantum billiards Quantum chaotic systems Quantum many-body systems Hubbard model

SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

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arXiv 2021年

作者： Liang, Xin Zhao, Kai Di, Sheng Li, Sihuan Underwood, Robert Gok, Ali M. Tian, Jiannan Deng, Junjing Calhoun, Jon C. Tao, Dingwen Chen, Zizhong Cappello, Franck Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Department of Computer Science & Engineering University of California Riverside RiversideCA92521 United States Facebook Inc. Menlo ParkCA94025 United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL60439 United States Cerebras Systems SunnyvaleCA94085 United States Department of Computer Science Washington State University PullmanWA99164 United States School of Computing Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Today’s scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compressor has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized/optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we develop a novel modular, composable compression framework (namely SZ3), which involves three significant contributions. (1) SZ3 features a modular abstraction for the prediction-based compression framework such that the new compression modules can be plugged in easily. (2) SZ3 supports multi algorithm predictors and can automatically select the best-fit predictor for each data block based on the designed error estimation criterion. (3) SZ3 allows users to easily compose different compression pipelines on demand, such that both compression quality and performance can be significantly improved for their specific datasets and requirements. (4) In addition, we evaluate several lossy compressors composed from SZ3 using the real-world datasets. Specifically, we leverage SZ3 to improve the compression quality and performance for different use-cases, including GAMESS quantum chemistry dataset and Advanced Photon Source (APS) instrument dataset. Experiments show that our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion compared with the state-of-the-art approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressors

Efficient and accurate neural field reconstruction using resistive memory

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arXiv 2024年

作者： Yu, Yifei Wang, Shaocong Zhang, Woyu Zhang, Xinyuan Wu, Xiuzhe He, Yangu Yang, Jichang Zhang, Yue Lin, Ning Wang, Bo Chen, Xi Wang, Songqi Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Hong Kong Institute of Mind The University of Hong Kong Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulties arise from storage inefficiencies in conventional explicit signal representation. Hardware obstacles include the von Neumann bottleneck, which limits data transfer between the CPU and memory, and the limitations of CMOS circuits in supporting parallel processing. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. Software-wise, we employ neural field to implicitly represent signals via neural networks, which is further compressed using low-rank decomposition and structured pruning. Hardware-wise, we design a resistive memory-based computing-in-memory (CIM) platform, featuring a Gaussian Encoder (GE) and an MLP Processing Engine (PE). The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit. We demonstrate the system’s efficacy on a 40nm 256Kb resistive memory-based in-memory computing macro, achieving 31.5×, 35.5×, and 47.2× energy efficiency improvements and 10.8×, 38.8×, and 6.2× parallelism improvements without compromising reconstruction quality in tasks like 3D CT sparse reconstruction, novel view synthesis, and novel view synthesis for dynamic scenes. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal reconstruction

A Multi-objective Computation Offloading Method for Hybrid Workflow Applications in Mobile Edge Computing 9th

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A Multi-objective Computation Offloading Method for Hybrid W...

9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Peng, Kai Zhao, Bohai Qian, Xingda Xu, Xiaolong Zheng, Lixin Leung, Victor C. M. College of Engineering Huaqiao University Quanzhou China Fujian Provincial Academic Engineering Research Center in Industrial Intellectual Techniques and Systems Quanzhou China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9783030485122

Computation offloading has become a promising method to overcome intrinsic defects of portable smart devices, such as low operating speed and low battery capacity. However, it is a challenge to design an optimized strategy as the edge server is resource-constrained and the workflow application has timing constraints. In this paper, we investigated the hybrid workflow application computation offloading issue, which further increases the difficulty. According to the analysis of theory and consideration of time consumption and energy consumption, we establish a multi-objective optimization model to solve the issue. Furthermore, we propose a method based on particle swarm optimization algorithm for multi-objective computation offloading to get the optimal strategy for tasks offloading, which is suitable for all the hybrid workflow applications. Finally, extensive experiments have verified the effectiveness and efficiency of our proposed method. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy utilization

Disentangled adversarial transfer learning for physiological biosignals

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arXiv 2020年

Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the domain inconsistency of biosignals across users or different recording sessions from the same user. We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data in stress status level assessment. We exploit the trade-off between task-related features and person-discriminative information by using both an adversary network and a nuisance network to jointly manipulate and disentangle the learned latent representations by the encoder, which are then input to a discriminative classifier. Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework, and thus show its capabilities to adapt to a broader range of subjects. Finally we highlight that our proposed adversarial transfer learning approach is also applicable to other deep feature learning frameworks. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Universal physiological representation learning with soft-disentangled rateless autoencoders

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arXiv 2020年

作者： Han, Mo Özdenizci, Ozan Koike-Akino, Toshiaki Wang, Ye Erdoğmuş, Deniz Cognitive Systems Laboratory Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States CambridgeMA02139 United States

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

Non-Hermitian scattering symmetry revealed by diffusive channels

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arXiv 2023年

作者： Wang, Dong Chen, Hongsheng Qiu, Cheng-Wei Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining314400 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The Hamiltonian interprets how the system evolves, while the scattering coefficients describe how the system responds to inputs. Recent studies on non-Hermitian physics have revealed many unconventional effects. However, in all cases, the non-Hermiticity such as material loss is only considered for the Hamiltonian, even when studying scattering properties. Another important component-the scattering channel, is always assumed to be lossless and time-reversal symmetric. This assumption hinders the exploration on the more general and fundamental properties of non-Hermitian scattering. Here, we identify a novel kind of scattering channel that obeys time-reversal anti-symmetry. Such diffusive scattering channels overturn the conventional understanding about scattering symmetry by linking the positive and negative frequencies. By probing non-Hermitian systems with the diffusive channels, we reveal a hidden anti-parity-time (APT) scattering symmetry, which is distinct from the APT symmetry of Hamiltonians studied before. The symmetric and symmetry broken scattering phases are observed for the first time as the collapse and revival of temperature oscillation. Our work highlights the overlooked role of scattering channels in the symmetry and phase transition of non-Hermitian systems, thereby gives the diffusion the new life as a signal carrier. Our findings can be utilized to the analysis and control of strongly dissipative phenomena such as heat transfer and charge diffusion. © 2023, CC BY.

关键词： Hamiltonians

Human action performance using deep neuro-fuzzy recurrent attention model

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arXiv 2020年

作者： Bendre, Nihar Ebadi, Nima Prevost, John J. Rad, Paul Secure AI & Autonomy Laboratory Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States Secure AI and Autonomy Laboratory Department of Electrical and Computer Engineering Department of Information Systems Cyber Security University of Texas at San Antonio San AntonioTX78249 United States

A great number of computer vision publications have focused on distinguishing between human action recognition and classification rather than the intensity of actions performed. Indexing the intensity which determines the performance of human actions is a challenging task due to the uncertainty and information deficiency that exists in the video inputs. To remedy this uncertainty, in this paper we coupled fuzzy logic rules with the neural-based action recognition model to rate the intensity of a human action as intense or mild. In our approach, we used a Spatio-Temporal LSTM to generate the weights of the fuzzy-logic model, and then demonstrate through experiments that indexing of the action intensity is possible. We analyzed the integrated model by applying it to videos of human actions with different action intensities and were able to achieve an accuracy of 89.16% on our intensity indexing generated dataset. The integrated model demonstrates the ability of a neuro-fuzzy inference module to effectively estimate the intensity index of human actions. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks