Search Results for author:
Found 4 papers, 2 papers with code
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics.
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality
We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video.
Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems
The aim of this study is to develop surrogate models for quick, accurate prediction of thrust forces generated through flapping fin propulsion for given operating conditions and fin geometries.
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video
We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
