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In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added...
Concerning the problem of noise interference in the mechanical equipment fault signal acquisition, a novel mechanical equipment fault diagnosis method of wavelet shrinkage threshold based on Bayesian estimation combined with EMD is proposed. The fault signal denoising characteristics of different scales are considered in the proposed method. A new threshold which is suitable for the situation of noise...
It is quite challenging to monitor an ironmaking process due to some of its special characteristics such as lack of direct measurements and strong disturbances. Hence extracting robust features of the normal process from complex historical data is vitally important. Denoising autoencoder (dA), a recently developed deep learning technique, has become a popular tool to extract and compose robust features...
We propose contourlet-based algorithms to extract local angle from seismic migrated images and attenuate seismic noise. Steerable pyramid and projection-filter-based complex wavelet transform have been used to extract seismic local angle, while curvelet transform and complex wavelet transform have been used to attenuate seismic noise. However, previously mentioned transforms either have a fixed number...
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