This IV model detected and adjusted for significant selection effects associated with method of detection (P = 0.02).
Therefore, it might be interpreted that for our complete dataset, the CAT model detected and correctly interpreted more of the saturated positions that were misleading in the RAxML analysis.
The Riptide model detects both known and potentially novel trends in peptide fragment intensity within these high-quality PSMs.
In the following three sections, we will introduce the detailed methods of our proposed system, including how to collect sensing data, remove noises, estimate orientation, calculate distance and position, build the predication model, detect landmark, and calibrate the location.
Therefore, it can be concluded that the proposed model detects the SVA accurately and simulates HVS well by fusing depth information, static image saliency, and motion saliency.
Our algorithm, based on the incorporated trust model, detects the selfish nodes and finds alternative paths to the destination as is shown in Section 5.
This model detects saliency based on color and depth contrast and the pop-out effect.
Interestingly, the bulk of the error occurs prior to the decline in RUL, and the model detects an anomaly that presumably causes the subsequent deterioration.
Both models are applied on unclassified images, resulting in no crack (no model detects a crack), horizontal or vertical cracks (when only a model detects a crack), and alligator crack (when both models detect cracks).
The architecture includes user interface model, user interest model, detecting interest model and update model.
