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Realization of a quadrupole topological insulator phase in a gyromagnetic photonic crystal

National science Review 2024年第11期11卷 47-53页

作者： Peiheng Zhou Gui-Geng Liu Zihao Wang Shuwei Li Qindong Xie Yunpeng Zhang Subhaskar Mandal Xiang Xi Zhen Gao Longjiang Deng Baile Zhang National Engineering Research Center of Electromagnetic Radiation Control Materials Key Laboratory of Multi-spectral Absorbing Materials and Structures of Ministry of EducationUniversity of Electronic Science and Technology of China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University School of Electrical Engineering and Intelligentization Dongguan University of Technology Department of Electronic and Electrical Engineering Southern University of Science and Technology Centre for Disruptive Photonic Technologies The Photonics InstituteNanyang Technological University

The field of topological photonics was initiated with the realization of a Chern insulator phase in a gyromagnetic photonic crystal(Ph C) with broken time-reversal symmetry(T), hosting chiral edge states that are topologically protected propagating modes. Along a separate line of research, a quadrupole topological insulator was the first higher-order topological phase supporting localized corner states, but has been so far limited to T-invariant systems, as T is a key ingredient in early models. Here we report the realization of a quadrupole topological insulator phase in a gyromagnetic Ph C, as a consequence of topological phase transition from the previously demonstrated Chern insulator phase. The phase transition has been demonstrated with microwave measurements, which characterize the evolution from propagating chiral edge states to localized corner states. We also demonstrate the migration of topological boundary states into the continuum, when the gyromagnetic Ph C is magnetically tuned. These results extend the quadrupole topological insulator phase into T-broken systems, and integrate topologically protected propagating and localized modes in a magnetically tunable photonic crystal platform.

关键词： quadrupole topological insulator time-reversal symmetry breaking corner states topological phase transition

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Intelligent Channel Impulse Response Feature Prediction in Underwater Acoustic Networks 15

Intelligent Channel Impulse Response Feature Prediction in U...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yun, Sinwoong Lee, Jemin Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we develop an intelligent channel im-pulse response (CIR) feature prediction algorithm in underwater networks. To this end, we first extract the major features, i.e., CIR values and tap distances, from raw CIR. Then, we implement a feature prediction module by adopting the time-series forecasting learning algorithm. Through the simulation results, we verify the prediction accuracy of the proposed algorithm with the normalized mean square error (NMSE) loss curve. © 2024 IEEE.

关键词： Prediction models

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Recovery Strategy of Wind Turbine for Frequency Regulation Considering Wind Condition 3

Recovery Strategy of Wind Turbine for Frequency Regulation C...

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3rd International Conference on Energy, Power and electrical Technology, ICEPET 2024

作者： Xin, Zhipeng Zhang, Feng Sun, Kexu Cornélusse, Bertrand Shandong University School of Electrical Engineering Jinan China University of Liege Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9798350352658

The traditional recovery strategy of wind turbines after frequency regulation generally sets the wind speed unchanged during the frequency recovery period, during which while the wind speed has random volatility and has an impact on the overall wind power. For this reason, a staged frequency recovery strategy for wind turbines adapted to the random change of wind speed is proposed. Specifically, a high level of power support is provided by the wind power at the early stage to inhibit the rapid fall of frequency, and the wind speed is regarded as constant due to the relatively short control time. At the middle stage, wind power carries out continuous frequency support, continuously feeding back random wind speed information and iteratively updating control parameters, and then cooperates with synchronous machine frequency response for smooth action. The last stage is the wind turbine speed recovery phase, divided into several time intervals, and the speed recovery parameters are adjusted to adapt to the random wind speed changes, and weaken the frequency secondary drop caused by the wind power withdrawal. Finally, the effectiveness of the proposed strategy is verified in simulation part. © 2024 IEEE.

关键词： Windmill

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Near-Field Integrated Sensing and Communication: Performance Analysis and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 6353-6366页

作者： Qu, Kaiqian Guo, Shuaishuai Saeed, Nasir Ye, Jia Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China United Arab Emirates University Department of Electrical and Communication Engineering Al Ain United Arab Emirates Chongqing University School of Electrical Engineering Chongqing400044 China

This paper explores the potential near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. Subsequently, we analyzed the performance degradation caused by traditional far-field beamforming in the near field for both communication and sensing. We formulate the transceiver NFBF design problem as maximizing the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring the required communication quality-of-service (QoS) and total power constraint. We decompose it into two subproblems and solve them using the generalized Rayleigh entropy theory and the Semi-Definite Relaxation (SDR) technique. Additionally, we prove the attainability of the optimal solution for SDR. Additionally, a low-complexity design scheme is proposed as an alternative to the SDR approach for obtaining transmit beamforming. The simulation results validate the effectiveness of the proposed NFBF scheme, demonstrating its capability to manage co-angle interference and enhance both communication and sensing performance. © 2020 IEEE.

关键词： Signal to noise ratio

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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Transforming Agriculture with Machine Learning: A Review of Applications and Future Prospects 6

Transforming Agriculture with Machine Learning: A Review of ...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Akurati, Durga Prasad Devarahatti, Prashanth Konakandla, Sanjay Kumar Satwik, C.H.S.S. Singh, Balraj School of Electronics and Electrical Engineering Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798350369748

Machine learning is a powerful tool for extracting insights and automating rule and connection extraction from many datasets. The current invention simplifies the tedious and error-prone process of learning from empirical data. These methods are promising and theoretical. Their greatest promise lies in their ability to evaluate real-world data. A major agricultural and horticulture initiative is using machine learning to overcome hurdles. This project includes several machine learning methods created and enhanced via study. Complex agricultural and horticultural problems are solved using the above strategies. Creating a software workbench to explore and apply machine learning algorithms on actual datasets is vital to this endeavour. One dairy herd management case study uses machine learning algorithms to develop culling guidelines from a large database of herd data. This case study shows how machine learning is used in this industry. © 2024 IEEE.

关键词： Adversarial machine learning

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control Strategy of Wind Turbines for Their Withdrawl of Frequency Regulation 3

Control Strategy of Wind Turbines for Their Withdrawl of Fre...

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3rd International Conference on Energy, Power and electrical Technology, ICEPET 2024

作者： Gao, Haishu Zhang, Feng Wu, Guanghao Cornelusse, Bertrand Shandong University School of Electrical Engineering Jinan China University of Liege Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9798350352658

The simultaneous withdrawal of multiple WTGs after wind farm participation in frequency regulation will cause a large power plunge, which in turn will cause a serious secondary frequency drop in the system. Moreover, due to the large area of large-scale wind farms, the wind conditions in which each WTG is located vary greatly, and the volatility of wind speed is more complicated, so the timing arrangement of WTG withdrawal becomes more difficult. Aiming at this problem, this research takes the power dips and the output frequency regulation energy into account, at the moment of withdraw of WTG, establishes a wind speed differentiated withdraw control mode for WTG frequency regulation, and based on this, a novel control strategy of wind turbines for their withdraw of frequency regulation is proposed, which staggers the time of speed restoration of the WTG groups at the wind farm level to mitigate the power dips caused by the simultaneous withdraw of multiple WTGs. © 2024 IEEE.

关键词： Windmill

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Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning

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Microsystems & Nanoengineering 2024年第6期10卷 559-571页

作者： Jing Zhang Yong-feng Fu Hao Shen Quan Liu Li-ning Sun Li-guo Chen School of Mechanical and Electrical Engineering Soochow UniversityNo.8 Jixue RoadSuzhou CityJiangsu 215000China School of Computer Science and Technology Soochow UniversityNo.333 Ganjiang East RoadSuzhou CityJiangsu 215006China

Microscopic imaging is a critical tool in scientific research,biomedical studies,and engineering applications,with an urgent need for system miniaturization and rapid,precision autofocus ***,traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this *** response,this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus(DRLAF).The proposed study employs a custom-made liquid lens with a rapid zoom response,which is treated as an“agent.”Raw images are utilized as the“state”,with voltage adjustments representing the“actions.”Deep reinforcement learning is employed to learn the focusing strategy directly from captured images,achieving end-to-end *** contrast to methodologies that rely exclusively on sharpness assessment as a model’s labels or inputs,our approach involved the development of a targeted reward function,which has proven to markedly enhance the performance in microscope autofocus *** explored various action group design methods and improved the microscope autofocus speed to an average of 3.15 time ***,parallel“state”dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples,thereby improving its generalization *** experimental results demonstrate that the proposed liquid lens microscope with DRLAF exhibits high robustness,achieving a 79%increase in speed compared to traditional search algorithms,a 97.2%success rate,and enhanced generalization compared to other deep learning methods.

关键词： Deep hardware generalization

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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