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Showing 101 - 120 results of 2,295 for search '((\ source selection functions\ ) OR (( resource OR resources) detection function\ ))', query time: 0.30s Refine Results
  1. 101

    Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model by Yuan Liu, Yilong Liu, Xiaoyan Guo, Xi Ling, Qingyi Geng

    Published 2025-04-01
    “…Abstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. …”
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  2. 102
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  5. 105

    Adaptive integrated weight unsupervised multi-source domain adaptation without source data by Zhirui Wang, Liu Yang, Yahong Han

    Published 2025-04-01
    “…Because target samples with low entropy measured from the pre-trained source model achieve high accuracy, the trust center samples are selected first using the entropy function. …”
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  6. 106
  7. 107

    Physiological and biochemical aspects of using black lion fly larvae as a source of biologically active and nutrient substances by M. S. Talan, I. S. Dokuchaeva, M. A. Mukhamed'yarov

    Published 2025-02-01
    “…Introduction. Functional foods are products that, in addition to taste and nutritional value, have a physiological effect on the human body. ω-6 and ω-3 fatty acids, when consumed in the form of triglycerides from various food sources, undergo digestion in the small intestine, allowing absorption and transport into the blood and subsequent assimilation into the body, including the brain, retina, heart and other tissues. …”
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  8. 108

    An Optimized FPGA-Based FDIR System for Sensor Fault Detection in Satellite Attitude Estimation by Xianliang Chen, Zhicheng Xie, Jiashu Wu, Xiaofeng Wu

    Published 2025-01-01
    “…To solve this problem, a Fault Detection, Isolation, and Recovery (FDIR) was proposed, which integrates an adaptive unscented Kalman filter (AUKF), a radial basis function (RBF) neural network for fault detection, and a QUEST-based estimator. …”
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  9. 109
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    Integrating individual tracking data and spatial surveys to improve estimation of animal spatial distribution by Valentin Lauret, Nicolas Courbin, Olivier Scher, Aurélien Besnard

    Published 2025-05-01
    “…Simulations showed that the integrated model correctly estimated habitat selection coefficients and benefited from both data sources with better accuracy and precision than RSF and Poisson GLM alone, especially when data are limited. …”
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  11. 111

    YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

    Published 2025-08-01
    “…To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification. …”
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  12. 112
  13. 113

    Phenotypic variation of Thenus spp. (Decapoda, Scyllaridae) in the waters of southern Thailand and Malaysia using multivariate morphometric analysis by Ihsan Hani Radzi, Cheng-Ann Chen, Sukree Hajisamae, Kay Khine Soe

    Published 2025-01-01
    “… Thenus spp. are slipper lobsters which are commercially significant as a food source with good aquaculture potential. This study focuses on collecting population information on Thenus orientalis and Thenus indicus from selected sites in southern Thailand and Malaysia to inform sustainable fisheries management about the resources. …”
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  14. 114

    SOURCES OF FINANCING INDUSTRIAL COMPLEX ENTERPRISES by Anzhela Zakhitovna Namitulina, Mikhail Nikolaevich Prokofiev, Nikolay Viktorovich Vorovsky

    Published 2016-08-01
    “…Subject article is relevant because It is devoted to description of sources of financing of defense enterprises and peculiarities of selection of sources of financing enterprises of the military-industrial complex. …”
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  15. 115
  16. 116

    Cookware as source of toxic metals: An overview by Okunola Alabi, Amos Tomiwa Afolabi

    Published 2024-10-01
    “… Cookware assumes a pivotal function in cooking, however, there are concerns about potential health hazards associated with the release of toxic metals into food. …”
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  17. 117

    Cookware as source of toxic metals: An overview by Okunola Alabi, Amos Tomiwa Afolabi

    Published 2024-10-01
    “… Cookware assumes a pivotal function in cooking, however, there are concerns about potential health hazards associated with the release of toxic metals into food. …”
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    Article
  18. 118

    Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data by Fatimah Alzahrani, Steve Maddock, Heidi Christensen

    Published 2025-06-01
    “…Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). …”
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  19. 119
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    SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

    Published 2025-01-01
    “…These results indicate that SDES-YOLO successfully combines efficiency and precision in fall detection. Through these innovations, SDES-YOLO not only improves detection accuracy but also optimizes computational efficiency, making it effective even in resource-constrained environments.…”
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