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Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network Method

Research in Astronomy and Astrophysics 2024年第9期24卷 248-259页

作者： Nyasha Mariam Mkwanda Weixin Tian Junlin Li Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China

The automatic detection and analysis of sunspots play a crucial role in understanding solar dynamics and predicting space weather *** paper proposes a novel method for sunspot group detection and classification called the dual stream Convolutional Neural Network with Attention Mechanism(DSCNN-AM).The network consists of two parallel streams each processing different input data allowing for joint processing of spatial and temporal information while classifying *** takes in the white light images as well as the corresponding magnetic images that reveal both the optical and magnetic features of *** extracted features are then fused and processed by fully connected layers to perform detection and *** attention mechanism is further integrated to address the“edge dimming”problem which improves the model’s ability to handle sunspots near the edge of the solar *** network is trained and tested on the SOLAR-STORM1 data *** results demonstrate that the DSCNN-AM achieves superior performance compared to existing methods,with a total accuracy exceeding 90%.

关键词： Sun:magnetic fields Sun:flares (Sun:)sunspots DSCNN Attention mechanism Edge dimming

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A gesture recognition method based on improved YOLOv8 detection

A gesture recognition method based on improved YOLOv8 detect...

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2024 China Automation Congress, CAC 2024

作者： Li, Haiming Chen, Yanfei Yang, Lei Shi, Jiaoxing Liu, Tao Zhou, Yihui Hubei Key Laboratory of Intelligent Robot School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350368604

Aiming at the existing hand gesture estimation algorithms with large computation and poor real-time performance, this paper proposes an improved YOLOv8 algorithm for hand recognition, which combines the improved YOLOv8 detection algorithm for the detection of the hand and combines it with gesture analysis to realize gesture recognition. The improved algorithm not only reduces the amount of floating-point arithmetic, but also increases the accuracy and robustness of detection. In the gesture detection part, the traditional YOLOv8 algorithm is improved by firstly adding the p6 detection head, secondly replacing the CBS module with RFSE module, and thirdly replacing the C2f module with CFE module. The gesture analysis part is performed by training the yolov8 classification model for gesture recognition. The test results of the improved model in TV-Hand and COCO-Hand datasets confirm the effectiveness of the improved YOLOv8 algorithm in this paper, which improves the accuracy by 2.3% and reduces the computational volume by 13.5% compared to the original YOLOv8n, and the correct rates of gesture recognition are all over 0%, and the improved algorithm, which ensures the lightweight and at the same time realizes the further enhancement of accuracy The improved algorithm can realize further improvement of accuracy while ensuring lightweight, and the application prospect is broader. © 2024 IEEE.

关键词： Gesture recognition

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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PLIP: Language-Image Pre-training for Person Representation Learning 38

PLIP: Language-Image Pre-training for Person Representation ...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI United States Department of Computer Vision Baidu Inc. China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based Vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Angle-insensitive dual bound states in the continuum on germanium metasurface

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Chip 2025年第1期4卷 44-51页

作者： Yiqing Liu Jinwen Lv Ye Fan Meixue Zong Shubin Zhang Zhengji Xu School of Microelectronics Science and Technology Sun Yat-sen UniversityZhuhai 519082China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Zhuhai 519082China

Metasurface-enabled bound states in the continuum(BICs)provide a novel solution for achieving exceptionally high quality factors(Q factors),which could overcome the limitations of traditional mid-infrared filters,sensors,lasers,and nonlinear ***,most BIC metasurfaces are restricted by their sensitivity to specific incident angles,limiting their practical ***,we introduced a germanium-based metasurface that supports two BIC modes for different polarizations,exhibiting robust angle *** leveraging geometric asymmetry,we effectively controlled BIC leakage and *** device maintained infinite Q factors under oblique incidence with preserved symmetry,and exhibited stable quasi-BIC resonance wavelengths and linewidths even with broken symmetry,regardless of TE or TM *** angular robustness has been validated both theoretically and experimentally,demonstrating its potential for broadening the applicability of high-performance mid-infrared optical devices.

关键词： Metasurface BIC Germanium Mid-infrared Angle-insen sitive

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A study of MySQL protocol-based database proxy approval system for fortress machine

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International Journal of Data Science 2025年第1期10卷 96-117页

作者： Zhang, Xian Luo, Xinhui Yin, Dong Qu, Taiguo Li, Hao Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Huaihua Tourism School Hunan Huaihua418099 China

With the increase of enterprise informatisation, database security and compliance operation management have become increasingly important. Therefore, it is essential to design an efficient database proxy approval system. In this paper, we develop a database proxy approval system based on the MySQL protocol for fortress machines, which provides a real-time customised configuration scheme for high-risk commands, designs a real-time approval process for six types of high-risk commands, and creates a simple and efficient matching algorithm for high-risk commands. We designed a large number of experiments to test the system’s connection success rate, operation stability, response time, CPU resource consumption, matching algorithm performance, and other aspects. The experimental results show that this database proxy approval system has good configuration flexibility, high accuracy, and good time performance. This system has a wide range of applications in electric power, finance, petroleum, and other fields. © 2025 Inderscience Enterprises Ltd.

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Boosting Explore-Exploit Behavior for Navigating Vehicle by Maintaining Informative Topological Frontier

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-11页

作者： Liu, Qiming Cui, Xinru Liu, Zhe Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

Target search in unknown environments places high demands not only on an autonomous vehicle's ability to perceive and interpret target cues, but also on its conscious of collecting these cues by active exploration. While existing navigation methods have successfully built target-driven policies by maintaining memory of explored areas, there has been a lack of focus on facilitating target-aware exploration-the informative frontier information at unexplored yet visible areas is often overlooked. In this paper, we introduce a novel topology-based memory structure, Frontier-enhanced Topological Memory (FTM), and a Hierarchical Topology Encoding and Extraction (HTEE) module, fostering the autonomous vehicle's awareness of both environmental exploration and target approach. Specifically, FTM innovatively incorporates informative ghost nodes on traditional topological map to represent unexplored yet visible regions. We leverage an online-trained implicit scene representation to estimate the positions and generate features of these ghost nodes. The HTEE then employs implicit graph convolutions and attention mechanisms to extract cognitive information from FTM, taking into account the hierarchical memory structure, target cues, and current state. Our design bolsters cognitive navigation decisions. The experiments in the high-fidelity environments, including performance tests, visualizations, and interpretability experiments, validate the effectiveness of our approach in enhancing the vehicle's exploratory behavior. The improved exploration awareness for target cue collection, in turn, enhances the success rate and path efficiency of target search. Furthermore, we demonstrate the adaptability of our algorithm in real-world physical environments. IEEE

关键词： Data mining

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年第4期40卷 3499-3512页

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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3D Lane Detection From Front or Surround-View Using Joint-Modeling & Matching

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-14页

作者： Zhou, Haibin Zhou, Huabing Chang, Jun Lu, Tao Ma, Jiayi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Computer Science School Wuhan University Wuhan China Electronic Information School Wuhan University Wuhan China

3D lanes offer a more comprehensive understanding of the road surface geometry than 2D lanes, thereby providing crucial references for driving decisions and trajectory planning. While many efforts aim to improve prediction accuracy, we recognize that an efficient network can bring results closer to lane modeling. However, if the modeling data is imprecise, the results might not accurately capture the real-world scenario. Therefore, accurate lane modeling is essential to align prediction results closely with the environment. This study centers on efficient and accurate lane modeling, proposing a joint modeling approach that combines Bézier curves and interpolation methods. Furthermore, based on this lane modeling approach, we developed a Global2Local Lane Matching method with Bézier Control-Point and key-Point, which serve as a comprehensive solution that leverages hierarchical features with two mathematical models to ensure a precise match. We also introduce a novel 3D Spatial Encoder, representing an exploration of 3D surround-view lane detection research. The framework is suitable for front-view or surround-view 3D lane detection. By directly outputting the key points of lanes in 3D space, it overcomes the limitations of anchor-based methods, enabling accurate prediction of closed-loop or U-shaped lanes and effective adaptation to complex road conditions. This innovative method establishes a new benchmark in front-view 3D lane detection on the Openlane dataset and achieves competitive performance in surround-view 2D lane detection on the Argoverse2 dataset. IEEE

关键词： Interpolation

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A Bi-selection Method Based on Consistent Matrix for Large-Scale Datasets

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1992-2005页

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Rough sets Noise Feature extraction Classification algorithms Accuracy Machine learning information systems Data models Computational efficiency Approximation algorithms

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