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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Xiujun Sun Xudong Zhang Yong Wang Hongqiang Sang Ying Zhou Physical Oceangraphy Laboratory Ocean University of China China Department of Computer Science and Technology Ocean University of China China School of Mechanical Engineering and Tianjin Key Laboratory of Advanced Mechatronic Equipment Technology Tiangong University China Institute for Advanced Ocean Study Ocean University of China China

ISBN: (纸本)9798400707988

Ship detection based on wave gliders plays an important role in tasks such as maritime patrols, target tracking, and maritime traffic control. However, the complex and diverse sea environment, as well as the large differences in the appearance and scale of ships on the sea, have led to traditional detection algorithms experiencing issues such as false detection, missed detection of small targets, and incomplete detection of ship mast areas in ship detection tasks. To alleviate this dilemma, we designed a ship detector named YOLOv7-ship based on YOLOv7-tiny. Firstly, we introduce a parameter-free attention module (SimAM) in the backbone network to search for attention regions in the scene, thereby helping to solve the problem of false detection under complex marine environment interference. Secondly, we designed a mixed upsampling feature fusion method and specifically added a small target detection layer to enhance the detection of small targets. Finally, we introduce Wise-IOU into the loss function, and carry out different gradient gain allocation strategies in stages to solve the problem of incomplete detection of ship mast. The experimental results show that the YOLOv7-ship detector we designed achieves [email protected] 99.4% and 81.1% on the public dataset SeaShips and our wave glider dataset WG-Ships, respectively, while maintaining real-time inference speed and outperforming most mainstream models in performance.

关键词： YOLOv7-tiny attention module ship detection wave glider

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WGAN-GP and LSTM based Prediction Model for Aircraft 4- D Traj ectory

WGAN-GP and LSTM based Prediction Model for Aircraft 4- D Tr...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Lei Zhang Huiping Chen Peiyan Jia Zhihong Tian Xiaojiang Du School of Computer and Information Engineering Henan University Kaifeng China Cyberspace Institute of Advanced Technology Guangdong University Guangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

The rapid growth of air traffic flow has brought the airspace capacity close to saturation and, at the same time, has resulted in great stress for air traffic controllers. The 4- D trajectory-based operation system is an important solution to problems in the current civil aviation field. The system mainly relies on accurate 4-D trajectory prediction technology to share trajectory information among air traffic control, airlines, and aircraft to achieve coordinated decision-making between flight and control. However, due to the complexity of trajectory data processing, the current 4-D trajectory prediction technology cannot meet actual needs. Therefore, a data generation and prediction network model (DGPNM) is proposed. It integrates the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) and long-short-term memory (LSTM) neu-ral networks. With its outstanding performance, the LSTM neural network is utilized in both the generation module and the prediction module. The proposed model generates plenty of sample data to enlarge the train set, so overfitting could be reduced in the process of LSTM training. Experimental results prove that compared with other classical methods, the altitude prediction accuracy in the proposed model far exceeds that in current research results, which improves the prediction accuracy of the 4- D trajectory.

关键词： Support vector machines Wireless communication Atmospheric modeling Computational modeling Neural networks Predictive models Data models

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Using Stacking Ensemble Learning to Predict Multi-Step Wind Speed Based on Wavelet Transformation, a Two-Steps Feature Selection Method, and Neural Networks

SSRN

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SSRN 2024年

作者： Amirteimoury, Faezeh Memarzadeh, Gholamreza Keynia, Farshid Department of Computer Engineering Islamic Azad University Kerman Branch Kerman Iran Department of Electrical Engineering Faculty of Engineering Vali-e-Asr University of Rafsanjan Rafsanjan Iran Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran

Wind energy is gaining attention in power sector. However, the instability of wind speed (WS) negatively affects the incorporation of wind energy into the power grid. Reducing wind power issues requires precise WS forecasting. The current paper introduces an ensemble approach for multi-steps WS forecasting including discrete wavelet transform (DWT) to denoise WS signal and mutual information-interaction gain (MI-IG) to identify the most relevant input features. Moreover, multi-layer perceptron (MLP), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN) are employed to build individual forecasting modules. Finally, stacking ensemble learning combines the outputs from these modules. For the first dataset, the proposed model achieved MSE between 0.05 to 0.59, MAE from 0.19 to 0.6, MAPE from 5.97% to 21.94% and R2 from 0.985 to 0.852 for one-hour ahead to five-hours ahead. © 2024, The Authors. All rights reserved.

关键词： Wind power

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Excitation protocols for nonlinear phononics in bismuth and antimony

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Physical Review Materials 2024年第1期8卷 015202-015202页

作者： Anubhab Haldar Zhengjie Huang Xuedan Ma Pierre Darancet Sahar Sharifzadeh Department of Electrical and Computer Engineering Boston University Boston Massachusetts 02215 USA Center for Nanoscale Materials Argonne National Laboratory Argonne Illinois 60439 USA Northwestern Argonne Institute of Science and Engineering Evanston Illinois 60208 USA Consortium for Advanced Science and Engineering The University of Chicago Chicago Illinois 60637 USA Division of Materials Science and Engineering Boston University Boston Massachusetts 02215 USA

We study the optical generation and control of coherent phonons in elemental bismuth (Bi) and antimony (Sb) using a classical equation of motion informed by first-principles calculations of the potential energy surface and the frequency-dependent macroscopic dielectric function along the zone-centered optical phonon coordinates. Using this approach, we demonstrate that phonons with the largest optomechanical couplings also have the strongest degree of anharmonicity among the zone-centered modes, a result of the broken-symmetry structural ground state of Bi and Sb. We show how this anharmonicity, explaining the light-induced phonon softening observed in experiments, prevents the application of standard phonon-amplification and annihilation protocols. We introduce a simple linearization protocol that extends the use of such protocols to the case of anharmonic phonons in broken-symmetry materials, and demonstrate its efficiency at high displacement amplitudes. Our formalism and results provide a path for improving optical control in nonlinear phononics.

关键词： First-principles calculations Optical phonons Optoelectronics

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Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Ye, Rongguang Wang, Shuai Zhu, Guangxu Wu, Yik-Chung The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen518172 China

Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region faces poor generalization when applied in other regions due to inter-city data domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization by collaborative privacy-preserving training over distributed datasets from different cities. Unfortunately, it suffers from slow convergence because the data from different cities are with disparate statistical properties. Going beyond existing HFL methods, we propose a Gaussian heterogeneous HFL algorithm (FedGau) to address inter-city data heterogeneity so that convergence can be accelerated. In the proposed FedGau algorithm, both single RGB image and RGB dataset are modelled as Gaussian distributions for aggregation weight design. This approach not only differentiates each RGB image by respective statistical distribution, but also exploits the statistics of dataset from each city in addition to the conventionally considered data volume. With the proposed approach, the convergence is accelerated by 35.5%-40.6% compared to existing state-of-the-art (SOTA) HFL methods. On the other hand, to reduce the involved communication resource, we further introduce a novel performance-aware adaptive resource scheduling (AdapRS) policy. Unlike the traditional static resource scheduling policy that exchanges a fixed number of models between two adjacent aggregations, AdapRS adjusts the number of model aggregation at different levels of HFL so that unnecessary communications are minimized. Extensive experiments demonstrate that AdapRS saves 29.65% communication overhead compared to conventional static resource scheduling policy while maintaining almost the same performance. © 2000-2011 IEEE.

关键词： Gaussian distribution

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High-performance polarization management devices based on thin-film lithium niobate

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Light(Science & Applications) 2022年第5期11卷 868-876页

作者： Zhongjin Lin Yanmei Lin Hao Li Mengyue Xu Mingbo He Wei Ke Heyun Tan Ya Han Zhaohui Li Dawei Wang X.Steve Yao Songnian Fu Siyuan Yu Xinlun Cai State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information TechnologySun Yat-sen University510275 GuangzhouChina Department of Electrical and Computer Engineering The University of British ColumbiaVancouverBC V6T 1Z4Canada Photonics Information Innovation Center and Hebei Provincial Center for Optical Sensing Innovations College of Physics Science and TechnologyHebei University071002 BaodingChina Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of Technology510006 GuangzhouChina

High-speed polarization management is highly desirable for many applications,such as remote sensing,telecommunication,and medical ***,most of the approaches for polarization management rely on bulky optical components that are slow to respond,cumbersome to use,and sometimes with high drive ***,we overcome these limitations by harnessing photonic integrated circuits based on thin-film lithium niobate *** successfully realize a portfolio of thin-film lithium niobate devices for essential polarization management functionalities,including arbitrary polarization generation,fast polarization measurement,polarization scrambling,and automatic polarization *** present devices feature ultra-fast control speeds,low drive voltages,low optical losses and compact *** these devices,we achieve high fidelity polarization generation with a polarization extinction ratio up to 41.9 dB and fast polarization scrambling with a scrambling rate up to 65 Mrad s−1,both of which are best results in integrated *** also demonstrate the endless polarization state tracking operation in our *** demonstrated devices unlock a drastically new level of performance and scales in polarization management devices,leading to a paradigm shift in polarization management.

关键词： polarization lithium film

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Stable high-peak-power fiber supercontinuum generation for adaptive femtosecond biophotonics

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advanced Photonics Nexus 2024年第4期3卷 126-135页

作者： Geng Wang Jindou Shi Rishyashring R.Iyer Janet E.Sorrells Haohua Tu University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and TechnologyUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of Electrical and Computer EngineeringUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of BioengineeringUrbanaIllinoisUnited States

Broad and safe access to ultrafast laser technology has been hindered by the absence of optical fiber-delivered pulses with tunable central wavelength,pulse repetition rate,and pulse width in the picosecond–femtosecond *** address this long-standing obstacle,we developed a reliable accessory for femtosecond ytterbium fiber chirped pulse amplifiers,termed a fiber-optic nonlinear wavelength converter(FNWC),as an adaptive optical source for the emergent field of femtosecond *** accessory empowers the fixed-wavelength laser to produce fiber-delivered∼20 nJ pulses with central wavelength across 950 to 1150 nm,repetition rate across 1 to 10 MHz,and pulse width across 40 to 400 fs,with a long-term stability of>2000 *** a prototypical label-free application in biology and medicine,we demonstrate the utility of FNWC in real-time intravital imaging synergistically integrated with modern machine learning and largescale fluorescence lifetime imaging microscopy.

关键词： femtosecond biophotonics fiber supercontinuum label-free multiphoton microscopy nonlinear optics and imaging self-supervised denoise

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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