The results show that the root mean square error (RMSE) of the DPR-ED model on the validation set is 1.028 m/s for the long-time sequence prediction, which is 6.6% better than that of the multilayer perceptron (MLP) model. When the two models are applied to the test dataset, the ...
The proposed model includes long short-term memory units and an attention mechanism. Long short-term memory can extract relationships between the historical trajectory of a ship and the current state of encountered ships. Simultaneously, the global attention mechanism in the proposed model can identify...
We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVillage”, “FGVC8”, and “EMBRAPA” datasets, ...
The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well ...
where 𝐮u is the wave displacement, 𝐕V is the P-wave velocity model and 𝐟f is the perturbation source (i.e., shot) function. Since the direct formulation is not tractable, it is common to use the inverse approach. Seismic velocity inversion computes a complete 3D velocity model (...
Objects are detected by comparing new observations to the model and identifying significant deviations. Wren et al. [7] conducted one of the earliest major studies in this field, in which they modeled pixels in the temporal domain with a Gaussian distribution. They detected local changes by ...
This model leverages the core sequential patterns in the data to improve efficiency, beating the regular DPP model. To boost the performance of VS, ref. [37] proposed using a regularization loss term and a CSNet to tackle the problems of ineffective feature learning. These enhancements are ...
we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invari...
In addition, the proposed model is reliable and robust for detecting and segmenting drone camera images from different viewpoints in the presence of wildfire and smoke. Keywords: drone; encoder–decoder; forest fire and smoke segmentation; deep-learning 1. Introduction Fire disasters cause significant...
In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic...