Zobrazeno 1 - 10
of 34 244
pro vyhledávání: '"autoencoder"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 3253-3277.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-10-2023-0659
Publikováno v:
International Journal of Coal Science & Technology, Vol 11, Iss 1, Pp 1-11 (2024)
Abstract As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equ
Externí odkaz:
https://doaj.org/article/72b68acf7bce43d1bd3e95266802b37e
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. Commonly used attribution methods localize image areas important for CNN prediction but do not characterize relevant imaging fe
Externí odkaz:
https://doaj.org/article/933854f3e3f34caebec4b7f8fcd1d012
Autor:
SHAN Chonghao
Publikováno v:
Chengshi guidao jiaotong yanjiu, Vol 27, Iss 10, Pp 274-279 (2024)
Objective When using convolutional neural networks (CNNs) for the state detection of catenary system cotter pins, the imbalance between positive and negative samples leads to a low detection rate of missing pins in the network model. Thus, a single-s
Externí odkaz:
https://doaj.org/article/1e4b6be3a39145f5af24cbf54545d3c4
Autor:
HE Jia
Publikováno v:
Chengshi guidao jiaotong yanjiu, Vol 27, Iss 10, Pp 13-20 (2024)
Objective The diverse and complex operating environment of axle box bearings makes it challenging for bearing fault diagnostic methods to achieve satisfactory results with the single sensor. To address this issue, research is conducted on train beari
Externí odkaz:
https://doaj.org/article/9fd129fe371b49c6a3384594b97ba127
Publikováno v:
Journal of Intelligent Systems, Vol 33, Iss 1, Pp 6821-30 (2024)
A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testin
Externí odkaz:
https://doaj.org/article/b2a8183e0b4746a7aaeba8a4f7b0893c
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data
Externí odkaz:
https://doaj.org/article/69fe8ccf934248c4b303fd8fb5c2ccf9
Autor:
Wafa Sulaiman Almukadi, Fadwa Alrowais, Muhammad Kashif Saeed, Abdulsamad Ebrahim Yahya, Ahmed Mahmud, Radwa Marzouk
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older per
Externí odkaz:
https://doaj.org/article/1fd08d1562b444d383ee94ca15f2171a
Publikováno v:
AI, Vol 5, Iss 3, Pp 1695-1708 (2024)
Human action recognition (HAR) based on skeleton data is a challenging yet crucial task due to its wide-ranging applications, including patient monitoring, security surveillance, and human- machine interaction. Although numerous algorithms have been
Externí odkaz:
https://doaj.org/article/6db5e7cb49744e039fc486429393c144
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (G
Externí odkaz:
https://doaj.org/article/54817533ad224a13a909bd864da8aaed