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Wyświetlanie 1-2 z 2
Tytuł:
3D block modelling of the Sin Quyen IOCG deposit, North Vietnam
Autorzy:
Hao, Duong Van
Nguyen, Dinh Chau
Klityński, Wojciech
Zygo, Władysław
Nowak, Jakub
Powiązania:
https://bibliotekanauki.pl/articles/27310153.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
North Vietnam
iron oxide copper deposits
3D model
resources
deposit development
Opis:
The IOCG Sin Quyen deposit is located in the Red River shear zone of North Vietnam. The ore bodies are known as hydrothermal veins and are hosted in Proterozoic metapelite. A block modelling approach was used to build a 3D model of the ore bodies. An analysis was carried out on Surfer 11 computer software using the archival data recorded from several dozen boreholes distributed within the study area, as well as data obtained from the mineral and chemical analysis of 50 samples collected recently in the deposit. The ore bodies generally trend in a NW-SE direction with an average azimuth of 107° and dip of around 70°.The Cu content in the ore bodies is inhomogeneous. In the bed extension direction, the exponential correlation of Cu concentration in ore bodies is recognized within 2,500 m, while in the direction perpendicular to the bed strike, the exponential dependence is observed on 500 m of distance. The high-grade mineralisation of copper within the ore bodies is often at the altitude interval from ∼100 m to ∼150 m above sea level (asl). These bodies are also rich in uranium and gold bearing minerals. The total resources of Cu, U and Ag were estimated and amount to 361,000; 12.7 and 11.87 tonnes respectively. The model indicates the downward extension of some ore bodies to below 300 m beneath the ground surface.
Źródło:
Geology, Geophysics and Environment; 2023, 49, 2; 175--195
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined YOLOv5 and HRNet for high accuracy 2D keypoint and human pose estimation
Autorzy:
Nguyen, Hung-Cuong
Nguyen, Thi-Hao
Nowak, Jakub
Byrski, Aleksander
Siwocha, Agnieszka
Le, Van-Hung
Powiązania:
https://bibliotekanauki.pl/articles/2147147.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
YOLOv5
HRNet
2D key points estimation
2D human pose estimation
Opis:
Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000×1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 281--298
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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