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Localization of Chiral Edge States by the Non-Hermitian Skin Effect

Physical Review Letters 2024年第11期132卷 113802-113802页

作者： Gui-Geng Liu (刘癸庚) Subhaskar Mandal Peiheng Zhou Xiang Xi Rimi Banerjee Yuan-Hang Hu Minggui Wei Maoren Wang Qiang Wang Zhen Gao Hongsheng Chen Yihao Yang Yidong Chong Baile Zhang Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan 523808 China School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing Jiangsu 210093 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen 518055 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang ZJU-UIUC Institute Zhejiang University Hangzhou 310027 China Centre for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore

Quantum Hall systems host chiral edge states extending along the one-dimensional boundary of any two-dimensional sample. In solid state materials, the edge states serve as perfectly robust transport channels that produce a quantized Hall conductance; due to their chirality, and the topological protection by the Chern number of the bulk band structure, they cannot be spatially localized by defects or disorder. Here, we show experimentally that the chiral edge states of a lossy quantum Hall system can be localized. In a gyromagnetic photonic crystal exhibiting the quantum Hall topological phase, an appropriately structured loss configuration imparts the edge states’ complex energy spectrum with a feature known as point-gap winding. This intrinsically non-Hermitian topological invariant is distinct from the Chern number invariant of the bulk (which remains intact) and induces mode localization via the “non-Hermitian skin effect.” The interplay of the two topological phenomena—the Chern number and point-gap winding—gives rise to a non-Hermitian generalization of the paradigmatic Chern-type bulk-boundary correspondence principle. Compared to previous realizations of the non-Hermitian skin effect, the skin modes in this system have superior robustness against local defects and disorders.

关键词： Edge states Photonic crystals Quantum Hall effect Chern insulators Non-Hermitian systems

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Forty Plus Years of Model Reduction and Still Learning

Forty Plus Years of Model Reduction and Still Learning

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IEEE Conference on Decision and control

作者： A. Astolff C. L. Beck D. Bhattacharjee Y. Kawano A. Moreschini H. Sandberg J. M. A. Scherpen Department of Electrical and Electronic Engineering Imperial College London United Kingdom Dipartimento di Ingegneria Civile e Ingegneria Informatica Università di Roma “Tor Vergata” Italy Univ. of Illinois at Urbana-Champaign Illinois USA Graduate School of Advanced Science and Engineering Hiroshima University Japan KTH Royal Institute of Technology Stockholm Sweden Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The approximation of complex dynamical systems models by reduced order models has been considered an important research problem for over four decades, not only in the field of control, but also in economics, image processing, circuit analysis, statistical mechanics, aircraft structures, and more recently in hybrid energy systems, to name just a sampling of fields. In this paper, we provide an overview of the development of balanced truncation and interpolation approaches for reducing linear and non-linear dynamical systems models for the purpose of control analysis and design.

关键词： Economics Analytical models Interpolation Biological system modeling Atmospheric modeling Image processing Reduced order systems Integrated circuit modeling Dynamical systems Circuit analysis

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YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

arXiv

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arXiv 2024年

作者： Tang, Xiaoyu Chen, Xingming Cheng, Jintao Wu, Jin Fan, Rui Zhang, Chengxi Zhou, Zebo The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China The School of Physics South China Normal University Guangdong Guangzhou510000 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The School of Aeronautics Astronautics University of Electronic Science and Technology of China and Aircraft swarm intelligent sensing and cooperative control Key Laboratory of Sichuan Province Chengdu610097 China The National Laboratory on Adaptive Optics Chengdu610209 China

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network’s feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Evaluation of Robustness Metrics for Defense of Machine Learning systems

Evaluation of Robustness Metrics for Defense of Machine Lear...

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International Conference on Military Communications and Information systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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Machine Learning and Intelligent Network System based Covid 19 Health Care System

Machine Learning and Intelligent Network System based Covid ...

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International Conference on Power, Energy, control and Transmission systems (ICPECTS)

作者： Amjath Ali Jakul Basha K. Saravanan T Mangayarkarasi C Balarengadurai RCC - ENGG Department Lecturer Computer Engineering Electrical and Electronic Section Department of Engineering Sultanate of Oman University of Technology and Applied Sciences Ibra Department of Information Technology R.M.D Engineering College Chennai Tamil Nadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (纸本)9781665462761

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency.

关键词： COVID-19 Micromechanical devices Biomedical equipment Technological innovation Medical services Production Hardware

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System identification beyond the Nyquist frequency: A kernel-regularized approach

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control engineering Practice 2025年 164卷

作者： Max van Haren Roy S. Smith Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Groene Loper 5 Eindhoven 5612 AE The Netherlands Automatic Control Laboratory ETH Zürich Zürich 8092 Switzerland Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft 2628 CN The Netherlands

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate output measurements and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.

关键词： System identification Kernel-regularized estimation Multirate Sampled-data systems Frequency response functions

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Data Augmentation and the Improvement of the Performance of Convolutional Neural Networks for Heart Sound Classification

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第4期49卷 1033页

作者： Takezaki, Shumpei Kishida, Kazuya Graduate School of Systems Life Sciences Kyushu University 744 MotookaNishi-kuFukuoka Fukuoka-shi8190395 Japan Department of Electronic Control Engineering National Institute of Technology Kagoshima College 1460-1 ShinkoHayatoKagoshima Kirishima8995102 Japan

The effective management and early diagnosis of cardiovascular diseases (CVDs) are crucial to bring down the mortality associated these diseases. Because detecting CVDs can be a difficult task, especially when no symptoms are present, developing systems that can detect CVDs or heart abnormalities automatically has attracted significant research attention in recent times. For this purpose, several convolutional neural networks (CNNs) learned using heart sound signals (i.e., the phonocardiogram or PCG) have been proposed. Generally, CNNs require a high volume of annotated training data to achieve high performance. However, annotated PCG (i.e., PCGs labelled as abnormal or normal) dataset is not sufficient for training CNNs. To address this issue, the classification performance of CNNs need to be improved, so that they can be trained even on an insufficient PCG database. In this study, we consider two data augmentation (DA) methods: window slicing with spectrogram, which slices a single PCG to generate multiple signals and transforms the signals into spectrogram data;the other is a synthetic spectrogram based generative adversarial network, which generates synthetic data. To demonstrate the validity of the two DA methods, we performed experiments concerning heart sound detection and discussed the results of the experiments in terms of accuracy, sensitivity, and specificity. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Convolution

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Multi-objective Optimization of A CCHP System Model Integrated with P2G for Decarbonization

Multi-objective Optimization of A CCHP System Model Integrat...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Pengyu Wei Olusola Bamisile Dongsheng Cai Jian Li Humphrey Adun Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu China Energy Systems Engineering Department Cyprus International University KKTC Mersin Turkey

To realize the dual carbon policy, the goal of energy saving, and emission reduction, it is important to develop ways to better promote the low carbon economic operation of energy systems. Therefore, this research proposes an optimal operation strategy considering the electric-gas-cooling-heat demand response. Considering the demand response of each participant, an integrated energy system model with photovoltaic, wind energy, and traditional grid as energy supply. Also, electric energy, natural gas, cooling, and heating subsystems with co-production are established. With minimizing the overall system operation cost and optimizing environmental protection, the improved particle swarm optimization algorithm is used to solve the multi-constrained objective optimization problem. Finally, the optimized model is tested. The results from this study show that the energy system under cogeneration can find a balance between economy and environmental friendliness, and provide a reference for the subsequent energy supply configuration.

关键词： Photovoltaic systems Renewable energy sources Wind energy systems operation System integration Demand response Particle swarm optimization

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Net-Zero Energy House-oriented Linear Programming for the Sizing Problem of Photovoltaic Panels and Batteries

arXiv

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arXiv 2024年

作者： Carnerero, A. Daniel Tanaka, Taichi Li, Mengmou Hatanaka, Takeshi Wasa, Yasuaki Hirata, Kenji Ushifusa, Yoshiaki Ida, Takanori Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-0046 Japan Department of Electrical Engineering and Bioscience School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan Department of Electrical and Electronic Engineering School of Engineering University of Toyama Toyama930-8555 Japan Faculty of Economics and Business Administration The University of Kitakyushu Fukuoka802-8577 Japan Graduate School of Economics Kyoto University Kyoto606-8501 Japan

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs)—houses designed to have an annual net energy consumption around zero—by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear programming

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Scalable Multiport DC-DC Converter for Bidirectional EV charging in DC Traction Grids

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IEEE Transactions on Circuits and systems II: Express Briefs 2025年

作者： Lukianov, Mykola Romero-Cadaval, Enrique Kasprowicz, Andrzej Husev, Oleksandr Strzelecki, Ryszard Department of Power Electronics and Electrical Machines Gdañsk University of Technology Gdañsk 80-222 Poland Power Electrical and Electronic Systems R and D Group School of Industrial Engineering University of Extremadura Badajoz 06006 Spain Gdynia Maritime University Faculty of Electrical Engineering Gdynia Poland Warsaw University of Technology Institute of Control and Industrial Electronics Koszykowa 75 Warsaw 00-662 Poland

The limited availability of public Electric Vehicle (EV) charging infrastructure poses a significant challenge to EV adoption in urban areas, that requires significant economic expenses. One potential solution to mitigate the problem is utilizing the existing traction DC grid infrastructure for EV charger connections. Such solution allows exploiting already existing traction substation equipment for both EV charging and powering DC traction grids, potentially reducing charging station installation costs. At the same time, bidirectional EV charger operation allows providing services such as DC grid voltage stabilization and traction transport braking energy recuperation. Therefore, this paper examines a scalable multiport DC-DC converter, suitable for multiterminal EV charging stations, located in the proximity to DC traction networks. The use of a multiport converter allows for sharing electric components across multiple ports, reducing the total number of elements required, compared to conventional solutions, where each EV charger relies on a separate converter. © 2004-2012 IEEE.

关键词： bidirectional EV charger DC-DC converter multiport converter transport infrastructure

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