Zobrazeno 1 - 10 of 44 pro vyhledávání: '"Xiaomei Ni"'
1
Akademický článek
Comprehensively dissecting the hub regulation of PkaC on high‐productivity and pellet macromorphology in citric acid producing Aspergillus niger
Autor: Xiaomei Zheng, Timothy C. Cairns, Xiaomei Ni, Lihui Zhang, Huanhuan Zhai, Vera Meyer, Ping Zheng, Jibin Sun
Publikováno v: Microbial Biotechnology, Vol 15, Iss 6, Pp 1867-1882 (2022)
Summary Aspergillus niger, an important industrial workhorse for citric acid production, is characterized by polar hyphal growth with complex pelleted, clumped or dispersed macromorphologies in submerged culture. Although organic acid titres are dram
Externí odkaz: https://doaj.org/article/21f75c96e6ae4e1cbae6adc58ce7948d
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2
Akademický článek
Phosphorylation of islet-1 serine 269 by CDK1 increases its transcriptional activity and promotes cell proliferation in gastric cancer
Autor: Qiong Shi, Xiaomei Ni, Ming Lei, Quansong Xia, Yan Dong, Qiao Zhang, Weiping Wang
Publikováno v: Molecular Medicine, Vol 27, Iss 1, Pp 1-11 (2021)
Abstract Background Despite recent advances in diagnostic and therapeutic approaches for gastric cancer (GC), the survival of patients with advanced GC remains very low. Islet-1 (ISL1) is a LIM-homeodomain transcription factor, which is upregulated a
Externí odkaz: https://doaj.org/article/11f20958fb4940cca3fbe8354dc2501c
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3
Akademický článek
Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
Autor: Changchang Che, Huawei Wang, Qiang Fu, Xiaomei Ni
Publikováno v: Advances in Mechanical Engineering, Vol 11 (2019)
Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling
Externí odkaz: https://doaj.org/article/79177b5cc8ae40ef9f8d9b16b9d82ebc
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4
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions
Autor: Changchang Che, Huawei Wang, Ruiguan Lin, Xiaomei Ni
Publikováno v: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 236:9900-9913
Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep me
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4196c7c2428333d8314d85c408ce06fe
https://doi.org/10.1177/09544062221101834
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6
Performance degradation prediction of aeroengine based on attention model and support vector regression
Autor: Qiang Fu, Changchang Che, Xiaomei Ni, Huawei Wang
Publikováno v: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 236:410-416
Accurate performance degradation prediction of aeroengines can ensure the safety and reliability of the aircraft. Based on the mass long time series data of multiple state parameters, a novel performance degradation prediction method based on attenti
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2287e0db2e90e9f98742f3a0418721ac
https://doi.org/10.1177/09544100211014743
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7
Akademický článek
Recent advances in carbon monoxide-releasing nanomaterials
Autor: Xiaomei Ning, Xinyuan Zhu, Youfu Wang, Jinghui Yang
Publikováno v: Bioactive Materials, Vol 37, Iss , Pp 30-50 (2024)
As an endogenous signaling molecule, carbon monoxide (CO) has emerged as an increasingly promising option regarding as gas therapy due to its positive pharmacological effects in various diseases. Owing to the gaseous nature and potential toxicity, it
Externí odkaz: https://doaj.org/article/d39c47849add4670989f9063cea74820
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8
Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder
Autor: Qiang Fu, Huawei Wang, Xiaomei Ni, Changchang Che
Publikováno v: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 235:1106-1114
Accurate fault prediction of rolling bearing can predict the operation condition in advance, which is an important means to ensure the safety and reliability of rotating machinery. Aimed at the data processing of rolling bearing vibration signal with
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c7206bc81404c0b730892fbc0a0a8636
https://doi.org/10.1177/0954406220941037
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9
Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network
Autor: Qiang Fu, Changchang Che, Xiaomei Ni, Huawei Wang
Publikováno v: Industrial Lubrication and Tribology. 72:947-953
Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::46c8949493788422f84412f0939563c7
https://doi.org/10.1108/ilt-11-2019-0496
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