Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression.
Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms.
For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge.
Overall, we show that our model can efficiently simulate emergency evacuation in complex environments with multiple room exits and obstacles where it is difficult to obtain an intuitive rule for fast evacuation.
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world.
Because of the different migration mechanisms of leader and follower neural crest cells, we train two types of agents (leaders and followers) to learn the collective cell migration behavior.
We focus on one-shot classification by deep learning approach based on a small quantity of training samples.