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Research Progress in Solar Flare Prediction Methods

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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Joint Service Caching and Resource Allocation Over Different Timescales in Satellite Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5649-5664页

作者： Hu, Han Song, Kaifeng Zhan, Cheng Fan, Rongfei Yang, Jian Beijing Institute of Technology School of Information and Electronics Beijing100081 China Southwest University School of Computer and Information Science Chongqing China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China School of Information Science and Technology Hefei China

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption. © 2025 IEEE.

关键词： Computation offloading

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Mobility-Aware Power Control and User Scheduling for Downlink V2I Networks 100

Mobility-Aware Power Control and User Scheduling for Downlin...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Yujie Zhou, Momiao Sun, Yanshi Wang, Kan Hefei University of Technology School of Computer Science and Information Engineering Hefei China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798331517786

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations. © 2024 IEEE.

关键词： Quadratic programming

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Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

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Frontiers of computer science 2023年第3期17卷 91-102页

作者： Mingdi HU Long BAI Jiulun FAN Sirui ZHAO Enhong CHEN School of Communications and Information Engineering&School of Artificial Intelligence Xi’an University of Posts&TelecommunicationsXi’an 710121China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation ***,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual ***,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in *** address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance ***,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth *** former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with ***,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR *** extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail *** Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

关键词： vehicle color recognition benchmark dataset multi-scale feature fusion long-tail distribution improved smooth l1 loss

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TUTNet - A Segmentation Network for Fabric Sewing Thread in the Texture Background 7

TUTNet - A Segmentation Network for Fabric Sewing Thread in ...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Liang, Xi Yu, Ye Chen, Fengxin Wang, Zilong Li, Xin Lu, Qiang School of Computer Science and Information Hefei University of Technology Hefei China School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (纸本)9798350350890

Sewing thread segmentation can help locate the defects in fabric sewing process, which is a challenging problem in factory quality control. In this paper, we propose a twin U-shaped Transformer Network (TUTNet) for sewing thread segmentation. To learn high-dimensional abstract features and capture the sewing direction while ensuring the smoothness of segmentation, we propose a Twin-Attention Module (TAM), which is composed of Sewing Thread Direction (STD) and Sewing Thread Smoothness (STS) blocks. To better combine low-dimensional features with fine-grained features, and generate more continuous results, we propose a Scaled Feature Fusion Module (SFFM), which is used to calculate losses at multiple levels. We also propose a large-scale sewing thread segmentation (STSeg) dataset, which is collected from real clothing production factory. Finally, we conduct experiments on both STSeg and DGRE datasets to verify the effectiveness of our method for sewing thread and other linear object segmentation. © 2024 IEEE.

关键词： Large datasets

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

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Chinese Journal of Electronics 2024年第2期33卷 423-435页

作者： Juncai HE Zhenxue HE Jia LIU Yan ZHANG Fan ZHANG Fangfang LIANG Tao WANG Limin XIAO Xiang WANG Hebei Agricultural University Beijing Information Science and Technology University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

关键词： Multi-output mixed polarity Reed-Muller Power optimization Combinatorial optimization problem Whale optimization algorithm Two-populations strategy Mutation strategy

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LDStega: Practical and Robust Generative Image Steganography based on Latent Diffusion Models 24

LDStega: Practical and Robust Generative Image Steganography...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Peng, Yinyin Wang, Yaofei Hu, Donghui Chen, Kejiang Rong, Xianjin Zhang, Weiming School of Computer Science and Information Engineering Hefei University of Technology Hefei China University of Science and Technology of China Hefei China Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Generative image steganography has gained significant attention due to its ability to hide secret data during image generation. However, existing generative image steganography methods still face challenges in terms of controllability, usability, and robustness, making it difficult to apply real-world scenarios. We propose a practical and robust generative image steganography based on Latent Diffusion Models, called LDStega. LDStega takes controllable condition text as input and designs an encoding strategy in the reverse process of the Latent Diffusion Models to couple latent space generation with data hiding. The encoding strategy selects a sampling interval from a candidate pool of truncated Gaussian distributions guided by secret data to generate the stego latent space. Subsequently, the stego latent space is fed into the Decoder to generate the stego image. The receiver extracts the secret data from the globally Gaussian distribution of the lossy-reconstructed latent space in the reverse process. Experimental results demonstrate that LDStega achieves high extraction accuracy while controllably generating image content and saving the stego image in the widely used PNG and JPEG formats. Additionally, LDStega outperforms state-of-the-art techniques in resisting common image attacks. © 2024 ACM.

关键词： Steganography

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Dual-task enhanced global–local temporal–spatial network for depression recognition from facial videos

Dual-task enhanced global–local temporal–spatial network f...

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作者： Shen, Jinjie Wu, Jing Xing, Yan Hu, Min Wang, Xiaohua Li, Daolun Zha, Wenshu School of Mathematics Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Heifei China

In previous studies on facial video depression recognition, although convolutional neural network (CNN) has become a mainstream method, its performance still has room for improvement due to the insufficient extraction of global and local information and the neglect of the correlation of temporal and spatial information. This paper proposes a novel dual-task enhanced global–local temporal–spatial network (DTE-GLTS) to enhance the extraction capability of global and local features and deepen the analysis of temporal–spatial information correlation. We design a dual-task learning mode that utilizes the data-efficient image transformer (Deit) as the main body to learn the global features of video sequences and guides Deit to learn local features with the pre-trained temporal–spatial fusion network (TSF). In addition, we propose the TSF mechanism to more effectively fuse temporal–spatial information in video sequences, strengthen the correlation between frames and pixels, and embed it in Resnet to form the TSF network. To the best of our knowledge, this is the first application of Deit and dual-task learning mode in the field of facial video depression recognition. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.06/7.73 and 5.91/7.68, respectively, while significantly reducing the number of parameters. © 2024 John Wiley & Sons Ltd.

关键词： Video analysis

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