43 citations · 122 across the 16 of their papers we have counts for
16 papers
Detection of Animal Movement from Weather Radar using Self-Supervised Learning
Mubin Ul Haque, Joel Janek Dabrowski, Rebecca M. Rogers +1
Detecting flying animals (e.g., birds, bats, and insects) using weather radar helps gain insights into animal movement and migration patterns, aids in management efforts (such as b…
PINN-Ray: A Physics-Informed Neural Network to Model Soft Robotic Fin Ray Fingers
Xing Wang, Joel Janek Dabrowski, Josh Pinskier +4
Modelling complex deformation for soft robotics provides a guideline to understand their behaviour, leading to safe interaction with the environment. However, building a surrogate…
A Neural Emulator for Uncertainty Estimation of Fire Propagation
Andrew Bolt, Conrad Sanderson, Joel Janek Dabrowski +2
Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behavio…
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
Joel Janek Dabrowski, Ashfaqur Rahman
In this paper we present a novel application of detecting fruit picker activities based on time series data generated from wearable sensors. During harvesting, fruit pickers pick f…
An operational framework to automatically evaluate the quality of weather observations from third-party stations
Quanxi Shao, Ming Li, Joel Janek Dabrowski +4
With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for va…
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton +5
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differentia…