HoneyCode| Dust Yourself Off
Project Details
The Challenge | Dust Yourself Off
Fortress Guard
Lunar dust detector - honeycode
Dusty Challenge
Craters created by asteroids and smaller meteorites struck the Moon. When an impactor strikes the solid surface of a planet, a shock wave spreads out from the site of the impact. The shock wave fractures the rock and excavates a large cavity. The impact sprays material out in all directions. The impactor is shattered into small pieces and may melt or vaporize. The more strikes that had happened in the area, the more dust that can be accumulates over the time. Because the weather on the moon is static, we use crater density to predict the dust accumulation on moon.
Introduce Fortress Guard
Fortress Guard is a multilayer project that detect and reduce the lunar dust from accumulating on the astronaut and the spacecraft.
AI detection system Dust melting base carpet using the robot to clear the landing area before astronaut are exposed to the dust environment.
Build magnet fence to attract dust away from the base area
Team build
The first portion involved the manual marking and labelling of the 200 crater images. Further, we implemented 23 layered CNN, with 9 convolutional layer, the activation and pooling were used after every 2 consecutive convolutional network. Total number of parameters in the model are 642,935.We ran 100 epochs on it with each epoch taking 180 seconds on average to run on GPU enabled tensorflow back-end. The accuracy started with 0.3058 accuracy and jumped to 0.3866 in just 3rd epoch. Within the 10th epoch the accuracy jumps to 0.5193. Overtime then by the 100th epoch accuracy had only progressed to 0.5751.
The first portion involved the manual marking and labelling of the 200 crater images. Further, we implemented 23 layered CNN, with 9 convolutional layer, the activation and pooling were used after every 2 consecutive convolutional network. Total number of parameters in the model are 642,935.We ran 100 epochs on it with each epoch taking 180 seconds on average to run on GPU enabled tensorflow back-end. The accuracy started with 0.3058 accuracy and jumped to 0.3866 in just 3rd epoch. Within the 10th epoch the accuracy jumps to 0.5193. Overtime then by the 100th epoch accuracy had only progressed to 0.5751.
References:
https://en.wikipedia.org/wiki/Adverse_health_effects_from_lunar_dust_exposure