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A SINR soft combining model extracts the effective SINR upon retransmissions.
The SSA-SVR model extracts the mean trend of the wind speed or wind power and treats the remainder as noise.
The proposed model extracts the five characteristic parameters using only exact analytical relationship and tabular data always available such as short-circuit current, open circuit voltage and the Maximum Power Point (MPP).
The proposed network architecture consists of Convolution layers followed by Recurrent Neural Network (RNN) which the combined model extracts the relations within facial images and by using the recurrent network the temporal dependencies which exist in the images can be considered during the classification.
The approach, based on a kinetic lattice gas model extracts the diffusion coefficient directly from the master equations which account for the microscopic kinetics of the system in which microscopic processes underlying the diffusion are particle jumps between neighboring adsorption sites.
The RBM model extracts the latent variables hidden in the training data and several RBMs are stacked one on top of others, using the hidden variables derived from lower models as the input, to get deeper layer variables that explain the hierarchical factorizations of PPINs.
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The model extracted the minimum number of nucleotide changes that resulted in the best prediction.
Because of the global property, the vector-valued C-V model extracts all the prominent objects of the images.
The keyword generation model extracts important keywords from the corpus and they will become the backbone of the template.
The VQM model extracts several features from the video sequences, expressing spatial gradient activity, chrominance information, contrast information, and absolute temporal information.
An analysis tool was developed for constructing the model, and the model extracted from the tool was evaluated its validity through reviews by engineers in a vehicle manufacturer with different specialties.
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