Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Yaolin Zhu"'
Publikováno v:
IET Image Processing, Vol 18, Iss 10, Pp 2665-2678 (2024)
Abstract Fires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmen
Externí odkaz:
https://doaj.org/article/3e63619606124e6690e6ae62c37265da
Publikováno v:
Journal of Natural Fibers, Vol 21, Iss 1 (2024)
Near-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduc
Externí odkaz:
https://doaj.org/article/9ce981c45cee4e348ca7b90b2cd51fb5
Publikováno v:
Journal of Natural Fibers, Vol 21, Iss 1 (2024)
Virgin cashmere is highly prized for its superior quality, and chemically modified wool is often used to fake it. However, traditional spectral analysis and image processing methods struggle to identify the two fibers. To address this, combining chem
Externí odkaz:
https://doaj.org/article/af46ec5922544df3853376fab19ec571
Publikováno v:
Journal of Natural Fibers, Vol 21, Iss 1 (2024)
ABSTRACTSuitable features are the key to identifying cashmere and wool fibers, and feature selection is an important step in classification. Existing supervised feature selection methods need to consider the information between fiber features and cla
Externí odkaz:
https://doaj.org/article/727ac81cea1c4ab085b0484969e8b9fb
Publikováno v:
Heliyon, Vol 10, Iss 14, Pp e34537- (2024)
Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extracti
Externí odkaz:
https://doaj.org/article/a55e1b785e304dccaf186d46c7ab732c
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-22 (2023)
Abstract Recognizing cashmere and wool fibers has been a challenging problem in the textile industry due to their similar morphological structure, chemical composition, and physicochemical properties. Traditional manual methods for identifying these
Externí odkaz:
https://doaj.org/article/074ae38620ba4db08d89ce9a6129ef13
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 10, Pp 9301-9310 (2022)
In order to solve the problem of insufficient features and overfitting in network training, an image identification method of cashmere and wool fibers based on an improved Xception network is proposed. Firstly, the normalized fiber image is input int
Externí odkaz:
https://doaj.org/article/9056696770674bf5b3ea78b5d1024af9
Publikováno v:
Fire, Vol 6, Iss 10, p 383 (2023)
To further improve the detection of smoke and small target smoke in complex backgrounds, a novel smoke detection model called RepVGG-YOLOv7 is proposed in this paper. Firstly, the ECA attention mechanism and SIoU loss function are applied to the YOLO
Externí odkaz:
https://doaj.org/article/b6c952e576d94757bc0f5cdde09e77ef
Publikováno v:
International Journal of Antennas and Propagation, Vol 2023 (2023)
Reliable channel estimation is critical for wireless communication performance, especially in vehicle-to-vehicle (V2V) communication scenarios. Aiming at the major challenges of channel tracking and estimating as the highly dynamic nature of vehicle
Externí odkaz:
https://doaj.org/article/c0183b736fdc41558c9c93ca42d6110e
Publikováno v:
Journal of Engineered Fibers and Fabrics, Vol 18 (2023)
There are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gra
Externí odkaz:
https://doaj.org/article/22c9a3a3040744e097d912e75f8c01dc