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Your search keyword '"Yang, Yuechao"' showing total 228 results
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228 results on '"Yang, Yuechao"'

19. Ammonium Sulfite Slurry from Ammonia-Based Desulfurization Activates Water-Soluble Humic Substances from Lignite: Performance, Application, and Mechanism

Author

Zhao, Shanshan, primary, Chen, Denglun, additional, Yao, Yuanyuan, additional, Liu, Chenghao, additional, Gao, Bin, additional, Liu, Yan, additional, Liu, Jiahui, additional, Wang, Songyuan, additional, Gao, Ni, additional, Yang, Mingchuan, additional, Shi, Guifang, additional, Zhang, Shugang, additional, Xie, Jiazhuo, additional, and Yang, Yuechao, additional

Published
2023
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35. Preoperative neutrophil-to-lymphocyte ratio (preNLR) for the assessment of tumor characteristics in lung adenocarcinoma patients with brain metastasis

Author

Cui, Huanhuan, primary, Yang, Yuechao, additional, Feng, Mingtao, additional, Gao, Yang, additional, Li, Liangdong, additional, Tu, Wenjing, additional, Chen, Xin, additional, Hao, Bin, additional, Li, Sen, additional, Li, Deheng, additional, Chen, Lei, additional, Zhou, Changshuai, additional, and Cao, Yiqun, additional

Published
2022
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44. Depth feature extraction for mineral mixed spectrum analysis based hyperspectral data

Author

Cui Xin, Yingjun Zhao, Zhu Ling, Qin Kai, and Yang Yuechao

Subjects
Mineral, Feature (computer vision), Single-scattering albedo, Dimensionality reduction, Feature extraction, Mineralogy, Hyperspectral imaging, Geology, Spectral line, Signal subspace
Abstract

Rocks in nature are mostly aggregates of various minerals. Because of the intimate mixing of minerals, the rock spectrum measured by hyperspectral sensors is generally the mixing spectrum of mineral components. Influenced by the noise of measuring equipment, non-linear mixing of mineral spectra, rock structure and mineral cleavage, determining and quantifying the abundances of minerals is still an open problem. In this paper, a method based on depth feature extraction for spectral unmixing of minerals is proposed to solve the problems of estimating the number of mineral endmembers, extracting the endmembers spectrum and calculating the abundance of mineral. Firstly, hyperspectral signal subspace minimum error identification (HySIME) algorithm is used to calculate the number of mineral endmembers. Secondly, the depth neural network is constructed to extract the dimension reduction feature of hyperspectral data, and obtain the mineral endmembers spectrum. Finally, the single scattering albedo of the spectrum is calculated by the Hapke model, and the abundance is estimated according to the number of endmembers and the endmembers spectrum. Aiming at the common rock forming minerals and altered minerals, the spectral data measured in the laboratory are tested. The results show that the method and technical process proposed in this paper are superior to the commonly used spectral mixed analysis method.

Published
2021

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