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This paper investigates an effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations.
Randomly occurring uncertainties (ROUs) are introduced to reflect the random nature of the network condition fluctuations, and the channel fading phenomenon is considered to account for the possibly unreliable network medium on which the measurement signal is transmitted.
Thus, our proposed scheme has the ability to provide more efficient control over network condition fluctuations.
Under various MCIoT situations, we dynamically adjust ξ values to provide more efficient control over system condition fluctuations.
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The CNN model could thus reduce learning computation requirements in the temporal dimension, and an invariance level of working condition fluctuation and ambient noise is provided by identifying the elementary features of bearings.
This result means that our view-based teaching/playback achieved robustness against changes of task conditions: fluctuations of the initial position of the object.
In America, people were gravitating toward noncarbonated drinks; abroad, Coke sales were affected by local economic conditions, currency fluctuations and regional tastes.
Because cells are highly sensitive to environmental conditions, environmental fluctuations can elicit cellular responses that contribute to the noise in a cell-based biosensor system.
The transformation of both minerals in the water column was sensitive to the hydrological conditions and fluctuations in the water column in relation to antecedent rainfall.
For the wide range of test conditions, pressure fluctuations depending on propeller rotation and on turbulent fluctuations on the wall were obtained.
Under certain conditions these fluctuations develop to deterministic chaos as it is characteristic of open systems with non-linear dynamics in bifurcation regions.
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