A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics

2022 ◽  
Vol 167 ◽  
pp. 108575
Author(s):  
Yunhan Kim ◽  
Kyumin Na ◽  
Byeng D. Youn
2020 ◽  
Vol 107 (9-10) ◽  
pp. 4077-4095
Author(s):  
Iskander Imed Eddine Amarouayache ◽  
Mohamed Nacer Saadi ◽  
Noureddine Guersi ◽  
Nadir Boutasseta

2020 ◽  
Vol 30 (9) ◽  
pp. 5088-5106 ◽  
Author(s):  
Jean-Paul Noel ◽  
Tommaso Bertoni ◽  
Emily Terrebonne ◽  
Elisa Pellencin ◽  
Bruno Herbelin ◽  
...  

Abstract Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on an adaptive time-scale, trial-to-trial. Here, we test this idea first via a visuo-tactile reaction time (RT) paradigm in augmented reality where participants are asked to respond as fast as possible to touch, as visual objects approach them. Results demonstrate that RTs to touch are facilitated as a function of visual proximity, and the sigmoidal function describing this facilitation shifts closer to the body if the immediately precedent trial had indexed a smaller visuo-tactile disparity. Next, we derive the electroencephalographic correlates of PPS and demonstrate that this multisensory measure is equally shaped by recent sensory history. Finally, we demonstrate that a validated neural network model of PPS is able to account for the present results via a simple Hebbian plasticity rule. The present findings suggest that PPS encoding remaps on a very rapid time-scale and, more generally, that it is sensitive to sensory history, a key feature for any process contextualizing subsequent incoming sensory information (e.g., a Bayesian prior).


Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1327
Author(s):  
Ziyan Zhang ◽  
Jiawei Tian ◽  
Weizheng Huang ◽  
Lirong Yin ◽  
Wenfeng Zheng ◽  
...  

In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction.


2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev

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