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Yingying Liang, Peng Zhao and Yimeng Wang
Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a chall...
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Pier Paolo G. Bruno
In this paper, seismic exploration methods are reviewed with a particular emphasis on the use of the reflection seismology to investigate the subsurface structures and characterize active faults. The paper provides a descriptive overview, intended for a ...
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Hong Je-Gal, Seung-Jin Lee, Jeong-Hyun Yoon, Hyun-Suk Lee, Jung-Hee Yang and Sewon Kim
Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in ...
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Weifeng Liu, Feihong Yun, Gang Wang, Liquan Wang and Shaoming Yao
As a key piece of equipment in underwater production system, a reliability study of deep-sea connectors has important theoretical significance and engineering value for increasing fault-free operation time, improving engineering safety, and reducing main...
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Prathamesh Lahande, Parag Kaveri and Jatinderkumar Saini
Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud?s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally,...
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