science_cases:lmsu_science_case
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science_cases:lmsu_science_case [2022/09/12 11:49] – admin | science_cases:lmsu_science_case [2022/09/12 13:44] (current) – admin | ||
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===== Description of the machine learning problem and our approach ===== | ===== Description of the machine learning problem and our approach ===== | ||
- | Based on data from the mission, several global models of the magnetosphere were proposed (e.g., Winslow et al., 2013; Philpott et al., 2020). However, they could only describe an average shape of the bow shock and magnetopause crossings and can be prone to missing the statistical nuances in the data. Given large data, [[: | + | Based on data from the mission, several global models of the magnetosphere were proposed (e.g., Winslow et al., 2013; Philpott et al., 2020). However, they could only describe an average shape of the bow shock and magnetopause crossings and can be prone to missing the statistical nuances in the data. Given large data, [[: |
- | The use of **statistical neural networks** allows us to explore another aspect: With the help of **[[: | + | The use of **statistical neural networks** allows us to explore another aspect: With the help of **[[: |
The data set was **manually labelled with the boundary crossings**. To identify bow shocks, we first subtracted planetary dipole magnetic field components from the magnetometer measurements, | The data set was **manually labelled with the boundary crossings**. To identify bow shocks, we first subtracted planetary dipole magnetic field components from the magnetometer measurements, | ||
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|4|Magnetosphere|14.1| | |4|Magnetosphere|14.1| | ||
- | The boundaries of critical interest - bow shock and magnetopause - are minorities with only 3.7 and 2.3 % representation. The table highlights the **data imbalance issue** that requires investigating special techniques to ensure the predictor does not bias towards the overrepresented classes. | + | The boundaries of critical interest - bow shock and magnetopause - are minorities with only 3.7 and 2.3 % representation. The table highlights the **[[: |
- | As a first step in pre-processing, | + | As a first step in pre-processing, |
- | In the feature preparation stage, a sliding window of variable sizes (3 seconds to 3 minutes) with a hop size of 1 second was computed on the time series signal to obtain feature vectors. Finally, the features were normalised to have mean of 0 and a standard deviation of 1. No other pre-processing or [[: | + | In the feature preparation stage, a sliding window of variable sizes (3 seconds to 3 minutes) with a hop size of 1 second was computed on the time series signal to obtain |
- | The windowed features are fed first into a block of 3 Convolutional | + | The windowed features are fed first into a block of 3 [[: |
- | The window size used in these experiments is 30 seconds. Overall, the [[: | + | The window size used in these experiments is 30 seconds. Overall, the [[: |
- | The results from the [[: | + | Results of this science case were presented at the {{: |
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- | Results of this science case were presented at the {{: | + | |
**References: | **References: |
science_cases/lmsu_science_case.1662976154.txt.gz · Last modified: 2022/09/12 11:49 by admin