检索结果-内蒙古大学图书馆

Topologically Protected Wavelength Conversion in a Photonic Valley Hall Insulator

学校读者我要写书评

暂无评论

Topologically Protected Wavelength Conversion in a Photonic ...

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sohn, Byoung-Uk Choi, Ju Won Chen, George F.R. Gao, Hongwei Mitchell, William J. Ng, Doris K.T. Tan, Dawn T.H. Photonics Devices and System Group SUTD-MIT International Design Center Singapore University of Technology and Design Singapore487372 Singapore NanoFab Facility Electrical and Computer Engineering Department University of California Santa Barbara Santa BarbaraCA93106 United States Institute of Microelectronics A*STAR 2 Fusionopolis Way #08-02 Innovis Tower Singapore138634 Singapore

We explore nonlinear effects in a topological photonic valley Hall insulator with a Kagome system at telecommunications wavelengths, we achieve topologically protected -12dB parametric conversion with sub-milliwatt in... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonics

Topologically Protected Wavelength Conversion in a Photonic Valley Hall Insulator

学校读者我要写书评

暂无评论

Topologically Protected Wavelength Conversion in a Photonic ...

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Landforms

Experimental comparison of induction motor and IPMSM with identical stator

学校读者我要写书评

暂无评论

Experimental comparison of induction motor and IPMSM with id...

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kim, Tae-Kil Kim, Jae-Hak Kim, Jung-Hwi Kim, Gyeong-Cheol Lee, Ju-Hun Yeo, Chang-Gi Jung, Sang-Yong Hyundai Motor Company Ep Drivetrain Test Team Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of Hyundai Motor Company Ep Drivetrain Test Team Hwaseong-si Korea Republic of Hyundai Motor Company Inverter Engineering Design Team Hwaseong-si Korea Republic of Hyundai Motor Company Ep System Engineering Design Team Hwaseong-si Korea Republic of Hyundai Motor Company Electrified Propulsion Test Group Hwaseong-si Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon-si Korea Republic of

ISBN: (纸本)9798350316445

With electric vehicle models becoming diverse for each car manufacturer, the commonality of driving components is becoming highly necessary to enhance productivity and reduce cost. In the case of traction motors, a common method is to diversify motors by varying the stacking length according to the required power. Another method is to assemble different type of rotors with an identical stator. By this method, it can be fabricated as an interior permanent magnet synchronous motor (IPMSM) and induction motor (IM). The second method is convenient and can achieve a high component commonality. However, IMs are known for their low power densities and low efficiencies compared with permanent magnet motors. In this study, the performance of two types of motors with identical stator is compared experimentally. To demonstrate advantages and disadvantages of IM compared with IPMSM, which is widely used as a traction motor for electric vehicles, the test results are described focusing on IM. © 2023 IEEE.

关键词： Drag torque Efficiency Idling loss Induction motor IPMSM MTPA MTPV Performance

Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Desu, Aditya He, Xuanli Xu, Qiongkai Lu, Wei School of Computing and Information System University of Melbourne Melbourne Australia Department of Computer Science University College London United Kingdom School of Computing FSE Macquarie University Sydney Australia StatNLP Research Group Singapore University of Technology and Design Singapore

As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of generated data. The challenge of verifying data ownership is further amplified by using Machine Learning as a Service (MLaaS), which often functions as a black-box system. Our work is dedicated to detecting data reuse from even an individual sample. Traditionally, watermarking has been leveraged to detect AI-generated content. However, unlike watermarking techniques that embed additional information as triggers into models or generated content, potentially compromising output quality, our approach identifies latent fingerprints inherently present within the outputs through re-generation. We propose an explainable verification procedure that attributes data ownership through regeneration, and further amplifies these fingerprints in the generative models through iterative data re-generation. This methodology is theoretically grounded and demonstrates viability and robustness using recent advanced text and image generative models. Our methodology is significant as it goes beyond protecting the intellectual property of APIs and addresses important issues such as the spread of misinformation and academic misconduct. It provides a useful tool to ensure the integrity of sources and authorship, expanding its application in different scenarios where authenticity and ownership verification are essential. © 2024, CC BY-NC-ND.

关键词： Authentication

Capacitor Minimization in Analog Synapses for Efficient and Compact Binarized SNNs

学校读者我要写书评

暂无评论

IEEE Transactions on Circuits and Systems for Artificial Int...

IEEE Transactions on Circuits and systems for Artificial Intelligence 2025年第2期2卷 114-125页

作者： Mikail Yayla Ming-Liang Wei Simon Thomann Albi Mema Chia-Lin Yang Jian-Jia Chen Hussam Amrouch Design Automation for Embedded Systems Group TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Department of the Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Technical University of Munich (TUM) Munich Germany TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Computer Science and Information Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute Dortmund Germany

Accelerating the immense workloads of Neural Networks (NNs) is a critical challenge, and analog computing-based approaches present a promising solution. Among these, the Integrate-and-Fire (IF) Spiking Neural Network (SNN) stands out for its potential in efficient neural computation. However, achieving high inference accuracy through precise multiply-accumulate (MAC) operations necessitates a large membrane capacitor—224 to 547 times larger than the synapse array at the 14nm technology node—resulting in prohibitive area costs. Additionally, the large capacitor size introduces higher energy consumption and longer access times. Thus, a key research challenge is maintaining accuracy while mitigating the costs associated with the large membrane capacitor. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14 $\times$ reduction in capacitor size along with a 34% reduction in energy consumption over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.

关键词： Capacitors Circuits Accuracy Capacitance Micromechanical devices Costs Neurons Integrated circuit modeling Spiking neural networks Hardware

Towards Ordinal Data Science

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Stumme, Gerd Dürrschnabel, Dominik Hanika, Tom Knowledge & Data Engineering Group Research Center for Information System Design Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Order is one of the main instruments to measure data. In this paper, we will therefore discuss difthe relationship between objects in (empirical) data. ferent means for measuring and ‘calculating’ with However, compared to methods that use numerical ordinal structures — a specific class of directed properties of objects, the amount of ordinal meth- graphs — and show how to infer knowledge from ods developed is rather small. One reason for this is them. Our aim is to establish Ordinal Data Science the limited availability of computational resources as a fundamentally new research agenda. Besides in the last century that would have been required cross-fertilization with other cornerstone machine for ordinal computations. Another reason — partic- learning and knowledge representation methods, a ularly important for this line of research — is that broad range of disciplines will benefit from this en-order-based methods are often seen as too mathe- deavor, including, psychology, sociology, economics, matically rigorous for applying them to real-world web science, knowledge engineering, *** Codes 03G10, 06A15, 68T27, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： Data Science

OVERCONSTRAINED LOCOMOTION

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Sun, Haoran Huang, Bangchao Zhang, Zishang Xu, Ronghan Huang, Guojing Feng, Shihao Huang, Guangyi Yin, Jiayi Qiu, Nuofan Chen, Hua Zhang, Wei Pan, Jia Wan, Fang Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology China Department of Computer Science The University of Hong Kong Hong Kong School of Design Southern University of Science and Technology China ZJU-University of Illinois Urbana-Champaign Institute Zhejiang University China System Design and Intelligent Manufacturing Southern University of Science and Technology China Design & Learning Research Group Southern University of Science and Technology China

This paper studies the design, control, and learning of a novel robotic limb that produces overconstrained locomotion by employing the Bennett linkage for motion generation, capable of parametric reconfiguration between a reptile- and mammal-inspired morphology within a single quadruped. In contrast to the prevailing focus on planar linkages, this research delves into adopting overconstrained linkages as the limb mechanism. The overconstrained linkages have solid theoretical foundations in advanced kinematics but are under-explored in robotic applications. This study showcases the morphological superiority of Overconstrained Robotic Limbs (ORLs) that can transform into planar or spherical limbs, exemplified using the simplest case of a Bennett linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range of overconstrained locomotion tasks, revealing its superiority in energy efficiency against planar limbs when considering foothold distances and speeds. The results are further verified in overconstrained locomotion policies optimized from Reinforcement Learning (RL). From an evolutionary biology perspective, these findings highlight the mechanism distinctions in limb design between reptiles and mammals and represent the first documented instance of ORLs outperforming planar limb designs in dynamic locomotion. Future studies will focus on deploying the model-based and learning-based overconstrained locomotion skills in the robotic hardware to close the Sim2Real gap for developing evolutionary-inspired, energy-efficient control of novel robotic limbs. © 2023, CC BY-NC-ND.

关键词： Deep reinforcement learning

Experimental comparison of induction motor and IPMSM with identical stator

学校读者我要写书评

暂无评论

Experimental comparison of induction motor and IPMSM with id...

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Tae-Kil Kim Jae-Hak Kim Jung-Hwi Kim Gyeong-Cheol Kim Ju-Hun Lee Chang-Gi Yeo Sang-Yong Jung eP Drivetrain Test Team Hyundai Motor Company Department of Electrical and Computer Engineering Sungkyunkwan University eP Drivetrain Test Team Hyundai Motor Company Hwaseong-si Korea Inverter Engineering Design Team Hyundai Motor Company Hwaseong-si Korea eP System Engineering Design Team Hyundai Motor Company Hwaseong-si Korea Electrified Propulsion Test Group Hyundai Motor Company Hwaseong-si Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon-si Korea

关键词：