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Causes of Winter Persistent Extreme Cold Events in Northeastern China

Advances in Atmospheric Sciences 2025年第4期42卷 780-793页

作者： Ming YANG Qingjiu GAO Tim LI State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Department of Atmospheric Sciences University of Hawaii at ManoaHonoluluHI 96822USA

Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data *** extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and *** the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak *** upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific *** the latter,the cooling invades the northern boundary first and then penetrates into the entire *** upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric *** addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.

关键词： extreme cold events low-frequency ISO mode Rossby wave train composite analysis downward propagation potential vorticity

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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and technology

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IEEE Transactions on information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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Normalized LMS adaptive filter algorithm: principles and verilog HDL implementation 4

Normalized LMS adaptive filter algorithm: principles and ver...

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4th International Conference on Electronic information and Communication Engineering, EICE 2025

作者： Wang, Haoxu Li, Bingjie Liu, Ming Yang, Dezhen Chen, Zhijie Liu, Xu Wan, Peiyuan Beijing Embedded System Key Lab School of Information Science and Technology Beijing University of Technology Beijing China National Key Laboratory of Infrared Detection Technologies Beijing China

This article offers a detailed exploration of the Normalized Least Mean Squares (NLMS) algorithm, highlighting its enhanced adaptive filtering capabilities and robust performance in managing input signal correlations. It delves into the foundational principles of the NLMS algorithm and outlines the essential modules required for its implementation in Verilog HDL. The implementation and simulation of the NLMS adaptive filter are studied. The simulation outcomes indicate that the convergence rate of the algorithm is superior to that of the conventional LMS algorithm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Adaptive filtering

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A Throughput and Priority Optimization Strategy for High Density Healthcare IoT

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Intelligent and Converged networks 2025年第1期6卷 41-52页

作者： Su, Zhenlang Ren, Junyu Huang, Yeheng Liao, Yang Qin, Tuanfa School of Computer Electronic and Information Guangxi University Nanning530004 China School of Physics and Electronic Information Guangxi Minzu University Nanning530006 China School of Computer Electronic and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China

In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4. © 2020 Tsinghua University Press.

关键词： Particle swarm optimization (PSO)

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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Generic Software Design for Radar Basic Data Inspection

Generic Software Design for Radar Basic Data Inspection

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2024 Advanced Fiber Laser Conference, AFL 2024

作者： Chen, HongLi Fu, JianChuan Liu, RenJie Ding, Chao Wang, Mingyang Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology No. 51 North Muxidi Xicheng District Beijing100038 China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

A general data inspection software based on the basic data of multi-source radar was designed, which was the central part of the data pre-processing. In this paper, software requirements analysis, overall architecture design, workflow design and detailed function design were carried out. Detailed functions included radar basic data loading, data analysis and recombination, data inspection requirement analysis, preprocessing model loading and data inspection. Data checks included normalization, completeness, correctness, consistency, current status, and satisfaction. Eventually, the software can present the results of the data inspection in the form of visualizations or reports. This software provided more general radar data review and filtering services. © 2025 SPIE.

关键词： Inspection Data modeling Radar Design Databases Data processing Statistical analysis Data acquisition Data storage Reliability

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Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution

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IEEE Access 2025年 13卷 73124-73134页

作者： Yang, Xiang Fang, Chen Xie, Xiaolan Dong, Minggang Guilin University of Technology College of Computer Science and Engineering Guangxi Guilin China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin China Guilin University of Technology College of Physics and Electronic Information Engineering Guangxi Guilin China

Super-resolution (SR) imaging involves converting low-resolution images into corresponding high-resolution data in specific scenarios. Recent advances in intelligent image reconstruction algorithms have been accompanied by impressive visual outcomes in SR generative adversarial networks (GANs). However, existing research has methodological limitations owing to the constraints posed by the model architecture and loss guidance used for optimization. Consequently, unnatural artifacts often appear in the complex backgrounds of images, leading to unappealing visual results. To address these problems, we propose an enhanced U-Net, known as the handwritten W-Net (HW-Net) architecture, as well as a loss function that uses high-frequency components for guidance. The former improves the best approximate representation of image regions, whereas the latter guides the generation of SR images with more detailed features. We experimentally implemented a biased strategy called parameter transfer, which is suitable for hybrid parameter models. This approach facilitates the blending of weight parameters between similar but different models, significantly improving the peak signal-to-noise ratio and structural similarity index measure metrics without sacrificing the learned perceptual image patch similarity. Across nine SR benchmark datasets, the proposed combinations, with and without parameter transfer, demonstrated excellent performance across multiple evaluation metrics, surpassing the state-of-the-art SRGAN. The code is available at https://***/DaHuaSen/H2SR. © 2013 IEEE.

关键词： Image enhancement

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 163-179页

作者： Yang, Gaoxiang Li, Xingrong Xiong, Yuan He, Meng Zhang, Lei Jiang, Chongya Yao, Xia Zhu, Yan Cao, Weixing Cheng, Tao National Engineering and Technology Center for Information Agriculture MOE Engineering Research Center of Smart Agriculture MARA Key Laboratory of Crop System Analysis and Decision Making Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural University One Weigang Jiangsu Nanjing210095 China College of Agronomy Henan Agricultural University Henan Zhengzhou450046 China

Spatially explicit information on crop distribution over large areas and long timespans is essential for optimizing agricultural spatial allocation and promoting food security. Despite the emergence of numerous remote sensing-based approaches for crop type mapping in recent years, the generation of long-term and high-quality crop type maps still remains challenging due to the poor spatiotemporal scalability of existing algorithms in the absence of ground labels or satellite imagery sources. In this study, we proposed a knowledge-guided machine learning (KGML) approach for extracting year-to-year training data and producing long-term winter wheat products in China by integrating multi-source remote sensing and environmental datasets. Based on the crop development patterns, critical phenological domains were first retrieved by spectral or polarization variation characteristics, and then the corresponding spectral signatures were combined to strengthen the differentiation between crop types. Consequently, annual training samples were extracted and refined automatically from the candidate crop pixels and employed to train the machine learning classifier with harmonic features, thus producing winter wheat maps over China year by year. Based on the long-term dataset, the spatiotemporal dynamics of winter wheat planting areas at the national scale from 2000 to 2023 were revealed by spatial and trend analyses, and the driving forces were further quantified. With the KGML, the first long-term winter wheat products at 30-m spatial resolution were produced over China (ChinaWheat30L). Independent validation suggested that the overall accuracy and F1-score of ChinaWheat30L were 0.929 and 0.906, respectively, with weak variations across years. The mapped areas of winter wheat aligned well with agricultural statistics at provincial and municipal levels (R2 = 0.93 and 0.84). Furthermore, the ChinaWheat30L exhibited minimal classification bias across various landscapes and demonstra

关键词： Photomapping

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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