High-Level Project Summary
Deception of the system due to the interference of unwanted data with the data received by the satellites from the sun delays us to take precautions. We have developed a machine learning algorithm (ALDOR) to minimize the margin of error in the data transmitted by WIND and DSCOVR.Using machine learning, this algorithm filters the data entering the algorithm and categorizes the most accurate versionsOur machine learning algorithm (ALDOR) is very important as it will prevent us from experiencing illusions in time by obtaining less jagged data and enable us to take action faster.
Link to Final Project
Link to Project "Demo"
Detailed Project Description
The algorithm we developed will receive data from two satellites, pass it through its own precision filter, and test it again. It will also develop its own machine learning by taking this data as an example (useful or not). The machine learning module we will use for this algorithm is desicion tree classification. Python is the programming language we will use here. Basically, the biggest benefit of this algorithm for us is that we will gain more accurate data, thus gaining time to take action against the next Carrington Event. Additionally, among the tools we will use, there is also a precision measurement filter that can re-read the data read by the sensors on the satellites.
Space Agency Data
Unfortunately, although we wanted to be able to read, test and improve the data sent by DSCOVR and WIND for 2 days, we could not install the library and view the data due to technical problems. If we could reach them, we could benefit from that data while developing our algorithm and obtain more precise results, just like the purpose of our project.
Hackathon Journey
Our team's name is ALDORES, short for "Always Do Research". That's why our inspiration is always the love of research.
Recognizing the seriousness of the issue and how damaging the results of another Carrington Event could be, we decided to choose and develop this project. We wanted to develop this machine learning algorithm (ALDOR) in order to separate the unwanted data between the data sent by the DSCOVR and WIND spacecraft to the ground station, and to prevent these unwanted data from sabotaging the accuracy of the observations.
When our team faced difficulties, we all exchanged ideas about that problem and tried to solve it together. At the same time, when we encounter problems or questions that we cannot solve, we asked the space apps coordinator and authorized officials here for help. During this 48-hour space apps challenge, we learned how to do the most effective research, how to deal with possible crises related to the team and the competition, and to develop an idea from the beginning and support it with data. We would like to thank all the people, institutions, sponsors and organizations who helped organize this event. We had a very productive 48 hours as a whole team.
References
https://algoritmaveprogramlama.wordpress.com/2013/09/26/algoritma-gosterim-akis-semasi/
https://www.spaceappschallenge.org/space-apps-challenge-2022-example-resource-save-the-earth-from-another-carrington-event/
https://www.youtube.com/watch?v=m_pDSJive-E&ab_channel=NASASpaceAppsChallenge
https://en.wikipedia.org/wiki/Faraday_cup#:~:text=A%20Faraday%20cup%20is%20a,first%20theorized%20ions%20around%201830.
https://solarsystem.nasa.gov/missions/DSCOVR/in-depth/
https://en.wikipedia.org/wiki/Wind_(spacecraft)
https://app.diagrams.net/
https://www.sciencedirect.com/science/article/abs/pii/S0168900215011675
images:
https://www.canva.com/
https://unsplash.com/
https://images.google.com/
Tags
#hardware #Dscovr #Wind #algorithm #solarwind #Carrington #solar #sun #machinelearning #AI #software #spaceappschallenge