Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of th...
This article presents the development of a sequence-to-sequence sparse stacked Autoencoder model based on LSTM layers and its training through a self-supervised manner for the purpose of detecting anomalous behavior on industrial equipment sensor data. The software stack for the analysis is described...
The vector at this point is known as a context vector, and the decoder uses it to create an output sequence. RNN or LSTM is used by the encoder to transform input into a hidden state vector. The encoder’s output vector is the latest RNN cell’s hidden state. The encoder sends the ...
Procedure of the greedy layer-wise training and fine-tuning. (I) Unsupervised self-learning on SAE Start the training process Step1: Obtain a labeled dataset Ω(x, y). Randomize SAE and initialize the iterative counter l = 1, training set I = Ω(x). Step2: Connect a virtual ...