Autor: Yang, Xiao-Feng - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Study on flotation behavior and mechanism of separating chalcopyrite and Molybdenite with ethyl mercaptoglycolate as inhibitor
Autorzy:: Yang, Xiao-Feng
Xu-Zhao
Liu, Yao-Yao
Powiązania:: https://bibliotekanauki.pl/articles/24085868.pdf
Data publikacji:: 2023
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: ethyl thioglycolate
chalcopyrite
molybdenite
inhibitor
separation
Opis:: The effect of ethyl thioglycolate organic small molecule inhibitors on chalcopyrite molybdenite flotation behaviour is investigated via single mineral micro-flotation tests, zeta potential tests, and X-ray photoelectron spectroscopy (XPS) analysis. Results of the flotation test indicate that ethyl thioglycolate organic small-molecule inhibitors can effectively separate Cu and Mo and selectively inhibit chalcopyrite under weak alkaline conditions. Infrared spectroscopy and XPS analysis show that hydrophilic functional groups C=O and -COOH in the ethyl thioglycolate organic small molecules can chemically adsorb onto the chalcopyrite surface. Moreover, ethyl thioglycolate has no obvious effect on zeta potential of molybdenite. Therefore, ethyl thioglycolate can effectively separate chalcopyrite and molybdenite.
Źródło:: Physicochemical Problems of Mineral Processing; 2023, 59, 2; art. no. 162824
1643-1049
2084-4735
Pojawia się w:: Physicochemical Problems of Mineral Processing
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A new low SNR underwater acoustic signal classification method based on intrinsic modal features maintaining dimensionality reduction
Autorzy:: Ju, Yang
Wei, Zhengxian
Li, Huangfu
Feng, Xiao
Powiązania:: https://bibliotekanauki.pl/articles/259300.pdf
Data publikacji:: 2020
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: acoustic
low SNR
signal classification
feature maintain
dimension reduction
Opis:: The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic. . This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the proces of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
Źródło:: Polish Maritime Research; 2020, 2; 187-198
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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