Showing 381 - 400 results of 1,810 for search '((\ (source OR sources) detection functions\ ) OR (( resource OR resource) detection function\ ))', query time: 0.31s Refine Results
  1. 381

    FsDAOD: Few-shot domain adaptation object detection for heterogeneous SAR image by Siyuan Zhao, Yong Kang, Hang Yuan, Guan Wang, Hui Wang, Shichao Xiong, Ying Luo

    Published 2025-06-01
    “…Heterogeneous Synthetic Aperture Radar (SAR) image object detection task with inconsistent joint probability distributions is occurring more and more frequently in practical applications. …”
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  2. 382

    MSSA: multi-stage semantic-aware neural network for binary code similarity detection by Bangrui Wan, Jianjun Zhou, Ying Wang, Feng Chen, Ying Qian

    Published 2025-01-01
    “…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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  3. 383

    DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

    Published 2019-06-01
    “…The functionality of stereotypes of perception and «memory of the genre» is briefly presented, which manifests itself in the history of understanding a domestic detective story as a constant appeal to the foreign sources of the genre. …”
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  4. 384
  5. 385

    App-DDoS detection method using partial binary tree based SVM algorithm by Bin ZHANG, Zihao LIU, Shuqin DONG, Lixun LI

    Published 2018-03-01
    “…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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  6. 386

    A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

    Published 2025-03-01
    “…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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  7. 387
  8. 388

    Self-Powered Microsystem for Ultra-Fast Crash Detection via Prestressed Triboelectric Sensing by Yiqun Wang, Yuhan Wang, Xinzhi Liu, Xiaofeng Wang, Keren Dai, Zheng You

    Published 2025-01-01
    “…We further developed a self-powered, compact (<4.5 cm3) microsystem that integrates the shock sensor, a signal processing module, airbag triggering circuitry, and a high-g-resistant supercapacitor as a backup power source. The microsystem achieves ultra-fast shock detection and airbag activation with a delay of less than 0.2 ms. …”
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    Article
  9. 389

    A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1 by Adedayo O. Adeyanju, Patrick J. Rich, Gebisa Ejeta

    Published 2025-03-01
    “…The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.…”
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    Article
  10. 390

    Specific detection of tartaric acid chiral isomers based on centrosymmetric terahertz metamaterial sensors by Xujun Xu, Zhen Sun, Guocui Liu, Jianjun Liu, Yong Du

    Published 2025-01-01
    “…Tartaric acid (C4H6O6) is a common food additive with two mutually symmetrical chiral carbons, which is a very important class of four-carbon organic chiral sources. L-, D-, DL-tartaric acids have different uses in food additives and pharmaceutical fields. …”
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  11. 391

    Study on Point Spread Function of Perovskite Fast Neutron Scintillation Imaging Screen by FENG Zhelin1, LIU Linyue2, , SONG Yan2, DUAN Baojun2, BAO Zizhen3, OUYANG Xiaoping2

    Published 2025-02-01
    “…Additionally, the limited availability of experimental machines for fast neutron imaging and the high cost of imaging systems hinders the efficient detection of large number of materials by using common fast neutron sources. …”
    Article
  12. 392

    The Relevance of Osteoscintigraphy Technique in Early Detection of Bone Metastatic Lesions: a Systematic Review by E. A. Litvinenko, I. V. Burova

    Published 2023-06-01
    “…OSG is an effective and informative technique for early detection of bone metastases, allowing to assess the functional state of the tumor and its surrounding tissues, even before the appearance of structural disorders visible by other diagnostic methods. …”
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  13. 393

    A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

    Published 2025-05-01
    “…Furthermore, the original loss function is replaced with SIoU to enhance detection accuracy. …”
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  14. 394

    Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

    Published 2025-04-01
    “…Abstract Aiming at the problems of low detection accuracy and poor generalization ability of underwater disease targets in inland waterways, an underwater disease target detection algorithm for inland waterways based on improved YOLOv5 is designed, which is denoted as YOLOv5-GBCE. …”
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  15. 395

    Enhancing Drone Detection via Transformer Neural Network and Positive–Negative Momentum Optimizers by Pavel Lyakhov, Denis Butusov, Vadim Pismennyy, Ruslan Abdulkadirov, Nikolay Nagornov, Valerii Ostrovskii, Diana Kalita

    Published 2025-06-01
    “…The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. …”
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  16. 396
  17. 397

    YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

    Published 2025-06-01
    “…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). …”
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  18. 398

    Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization by Haifei Xia, Haiyan Zhou, Mingao Zhang, Qingyi Zhang, Chenlong Fan, Yutu Yang, Shuang Xi, Ying Liu

    Published 2025-04-01
    “…The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. …”
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  19. 399

    STAR-YOLO: A High-Accuracy and Ultra-Lightweight Method for Brain Tumor Detection by Liyan Sun, Linxuan Zheng, Zhiguo Xiao, Yi Xin, Linqing Jiang

    Published 2025-01-01
    “…STAR-YOLO accomplishes a lightweight design while guaranteeing high detection accuracy, prominently demonstrating its immense potential in the diagnosis of clinical brain tumors, particularly in circumstances with constrained computing resources.…”
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  20. 400