First, the difference method is used to preprocess the meteorological observational data to obtain stationary time series data. Second, 1DCNN is used to extract feature variables related to temperature changes as the input variables of the neural network model. Finally, 1DCNN and ...
Then, these features are transferred to Bi-LSTM to explore the effective memory information. Finally, the prediction results of battery SOH are output from the fully connected layer. In the training process, parameters, such as the number of nodes of each layer and the number of epochs, are ...
A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 55, 1499–1526 (2023). https://doi.org/10.1007/s11063-022-10949-9 Download citation Accepted27 June 2022 Published12 July 2022 Issue DateApril 2023 DOIhttps:...
Combining the three types of characteristics of vehicle motion status,drive system status and power battery electrical signal,the 1dCNN-LSTM fusion model is established to estimate the indi⁃ vidual cell voltage under ideal conditions as reference. The difference between the real-time voltage ...
are shown in Figure 18, where the green line graph is the recognition result of the CNN model proposed in [57], the gray line graph is the recognition result of the LSTM model proposed in [58], and the blue line graph is the recognition result of the CNN-LSTM model proposed in [59...
The model consists of long short-term memory (LSTM) and CNN models. It was trained on an AWID dataset and its customized version. The customization relied on generating additional data using Time Series Generative Adversarial Network (TGAN) and merging it with an original dataset. Both models ...
are shown in Figure 18, where the green line graph is the recognition result of the CNN model proposed in [57], the gray line graph is the recognition result of the LSTM model proposed in [58], and the blue line graph is the recognition result of the CNN-LSTM model proposed in [59...
LS was used as a deterministic technique to obtain stochastic residuals (the difference between the observed data and the LS model). Consequently, a one-dimensional convolutional neural network (1D CNN) was used to predict the time-varying behaviors of the LOD change using different input sizes ...
Secondly, the distribution of values of different categories of data is large in order to prevent the large difference in the data scale level from adversely affecting the deep learning model, the data need to be normalized to eliminate the data scale, and the normalized data range is between ...
In addition, inertial sensors have been used to capture sequential patterns and perform pose estimation using algorithms such as Multi-Layer Perceptron (MLP) [4] and Long Short-Term Memory networks (LSTM) [5]. These techniques aim to optimize predictions by reducing the necessary amount of data...