From 5ab0b14f165c7a7a6177930c3b8dc663b468b4f8 Mon Sep 17 00:00:00 2001 From: Anson Date: Sun, 1 May 2022 23:35:28 -0700 Subject: [PATCH] transision to report3 --- .vscode/ltex.hiddenFalsePositives.en-US.txt | 1 + report.qmd | 22 ++++++++++++++------- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/.vscode/ltex.hiddenFalsePositives.en-US.txt b/.vscode/ltex.hiddenFalsePositives.en-US.txt index 5de5a62..dc46257 100644 --- a/.vscode/ltex.hiddenFalsePositives.en-US.txt +++ b/.vscode/ltex.hiddenFalsePositives.en-US.txt @@ -2,3 +2,4 @@ {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QWikipedia gives a different definition^[Wikipedia: Characteristic Length] that defines characteristic length as the volume divided by the surface area.\\E$"} {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QCalculation of Characteristic Length, @hillMeasurementSatelliteImpact slide 9\\E$"} {"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QCurrently, algorithms have been made that are capable of getting many key features from solid ^A mesh with a surface that is fully closed and has no holes in its geometry.\\E$"} +{"rule":"MORFOLOGIK_RULE_EN_US","sentence":"^\\QThe summary is that using PCA determined that by far the most variance out of the current list of properties is captured by the principle moments of inertia.^Eigen Values of the inertia tensor.\\E$"} diff --git a/report.qmd b/report.qmd index 2c0433b..ff3086c 100644 --- a/report.qmd +++ b/report.qmd @@ -266,7 +266,15 @@ CSV.write("scaled_dataset.csv", df) --- -## Gathering Data +## Machine Learning + +The rest of the document is an in depth look at the progress made characterizing the current fake +satellite dataset. The summary is that using PCA determined that by far the most variance out of the +current list of properties is captured by the principle moments of inertia.^[ Eigen Values of the +inertia tensor. ] I'm including this because it is already typed out and may be a good reference, +but likely isn't worth digging into once a new dataset of scans can be made. + +### Gathering Data To get started on the project before any scans of the actual debris are made available, I opted to find 3D models online and process them as if they were data collected by my team. GrabCAD is an @@ -276,7 +284,7 @@ assemblies found on GrabCAD, below is an example of one of the satellites that w ![Example CubeSat Used for Analysis](Figures/assembly.jpg) -## Data Preparation +### Data Preparation The models were processed in Blender, which quickly converted the assemblies to `stl` files, giving 108 unique parts to be processed. Since the expected final size of the dataset is expected to be in @@ -331,7 +339,7 @@ matter for characterization. The dataset is available for download here: - [scaled_dataset.csv](https://gitlab.com/orbital-debris-research/directed-study/report-3/-/blob/main/scaled_dataset.csv) -## Characterization +### Characterization The first step toward characterization is to perform a principal component analysis to determine what properties of the data capture the most variation. `PCA` also requires that the data is scaled, @@ -390,12 +398,12 @@ different shapes and the extreme values are slender rods or flat plates while th the center more closely resemble a sphere. As the dataset grows it should become more apparent what kind of clusters actually make up a satellite, and eventually space debris in general. -## Next Steps +### Next Steps The current dataset needs to be grown in both the amount of data and the variety of data. The most -glaring issue with the current dataset is the lack of any debris since the parts are straight from -satellite assemblies. Getting accurate properties from the current scans we have is an entire -research project in itself, so hopefully, getting pieces that are easier to scan can help bring the +glaring issue with the current dataset is the lack of any debris scans since the parts are straight +from satellite assemblies. Getting accurate properties from the current scans we have has proved +exceedingly difficult, so hopefully, getting pieces that are easier to scan can help bring the project back on track. The other and harder-to-fix issue is finding/deriving more data properties. Properties such as cross-sectional or aerodynamic drag would be very insightful but are likely to be difficult to implement in code and significantly more resource intensive than the current properties