Zobrazeno 1 - 8 of 8 pro vyhledávání: '"Zhenwen Sheng"'
1
Akademický článek
Multimodal imbalanced‐data fault diagnosis method based on a dual‐branch interactive fusion network
Autor: Jing He, Ling Yin, Zhenwen Sheng
Publikováno v: IET Science, Measurement & Technology, Vol 18, Iss 7, Pp 373-384 (2024)
Abstract Bearing‐fault diagnosis in rotating machinery is essential for ensuring the safety and reliability of mechanical systems. However, under complicated working conditions, the number of normal mechanical equipment samples can far exceed the n
Externí odkaz: https://doaj.org/article/8d678f9c00fe42e3a921aa08cabcb5f9
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2
Akademický článek
Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm
Autor: Zhenwen Sheng, Jinke Kuang, Li Yang, Guiyun Wang, Cuihong Gu, Yanxia Qi, Ruowei Wang, Yuehua Han, Jiaojiao Li, Xia Wang
Publikováno v: BMC Public Health, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Objective To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the tw
Externí odkaz: https://doaj.org/article/e97f9d1bae224846b31309d96b8bbe24
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4
Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network
Autor: Jianhua Liu, Haonan Yang, Jing He, Zhenwen Sheng, Shou Chen
Publikováno v: Computational Intelligence and Neuroscience. 2022:1-15
The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situation
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8977ac89403b1e130ec291d526bcb39
https://doi.org/10.1155/2022/1875011
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5
A bearing fault diagnosis method based on a convolutional spiking neural network with spatial–temporal feature-extraction capability
Autor: Changfan Zhang, Zunguang Xiao, Zhenwen Sheng
Publikováno v: Transportation Safety and Environment. 5
Convolutional neural networks (CNNs) are widely used in the field of fault diagnosis due to their strong feature-extraction capability. However, in each timestep, CNNs only consider the current input and ignore any cyclicity in time, therefore produc
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a6c4076a434ded1bc0500062449eb011
https://doi.org/10.1093/tse/tdac050
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6
Akademický článek
A bearing fault diagnosis method based on a convolutional spiking neural network with spatial-temporal feature-extraction capability.
Autor: Changfan Zhang, Zunguang Xiao, Zhenwen Sheng
Publikováno v: Transportation Safety & Environment; Jun2023, Vol. 5 Issue 2, p1-15, 15p
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8
Fast Method of Detecting Packaging Bottle Defects Based on ECA-EfficientDet
Autor: Zhenwen Sheng, Guiyun Wang
Publikováno v: Journal of Sensors.
Conventional methods of detecting packaging defects face challenges with multiobject simultaneous detection for automatic filling and packaging of food. Targeting this issue, we propose a packaging defect detection method based on the ECA-EfficientDe
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::addb0fbea52d4428ac100092e822bd2c
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