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Wyszukujesz frazę "Luo, Yi" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Selective extraction of vanadium from vanadium-titanium magnetite concentrates by non-salt roasting of pellets-H2SO4 leaching process
Autorzy:
Luo, Yi
Che, Xiaokui
Wang, Haixia
Zheng, Qi
Wang, Lei
Powiązania:
https://bibliotekanauki.pl/articles/1445902.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
vanadium-titanium magnetite
pellets
selective extraction
sulfuric acid leaching
Opis:
In this work, a novel process of pellet non-salt roasting and H2SO4 leaching was proposed for the selective extraction of vanadium from vanadium–titanium magnetite concentrate. Vanadium can be leached but the iron impurity was maintained in the pellets. Moreover, the leached pellets can meet the quality requirements of the iron-making process after secondary roasting, realizing comprehensive utilization. The maximal vanadium leaching efficiency was up to 60.3%, whereas 0.17% of the iron impurity was leached. The optimum conditions of pellet roasting and leaching were obtained by single-factor experiments. The X-ray diffraction and scanning electron microscopy–energy disperse X-ray spectrometry analyses showed that the vanadium iron spinel can be oxidized and decomposed into Fe2O3 and vanadate during the roasting process. Given that dilute sulfuric acid can react with vanadate without reacting with Fe2O3 in the leaching process, selective vanadium extraction was realized. This work provides new insights into the industrial production of vanadium–titanium magnetite concentrate involving the direct extraction of vanadium.
Źródło:
Physicochemical Problems of Mineral Processing; 2021, 57, 4; 36-47
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Noise quantization simulation analysis of optical convolutional networks
Autorzy:
Zhang, Ye
Zhang, Saining
Zhang, Danni
Su, Yanmei
Yi, Junkai
Wang, Pengfei
Wang, Ruiting
Luo, Guangzhen
Zhou, Xuliang
Pan, Jiaoqing
Powiązania:
https://bibliotekanauki.pl/articles/27310111.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
optical neural network
convolutional neural network
noise
quantization
Opis:
Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.
Źródło:
Optica Applicata; 2023, 53, 3; 483--493
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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