26. Data preparation und analysis of greenhouse telemetry data

 

Julia Niebling

Blog post – Julia Niebling

 

The machine learning group is situated at the junction of fundamental machine learning research and practical applications within the German Aerospace Center (DLR), such as computer vision for earth observation data, anomaly detection, and many more. It aims to be at the state of the art in deep learning, to further develop such methods, and determine how to put them into practice for DLR problems. Consequently, the group considers machine learning not just for a specific set of applications or data sets, but from a holistic perspective.

What is the data science project’s research question?  In this project, telemetry data from a special use case should be prepared for further steps. The data offer a lot of possibilities for analysis like anomaly detection, predictions, predictive maintenance, and many more. After structuring the data and a visualization of the data, suitable concrete analysis questions should be formulated and tackled with state-of-the-art methods.

What data will be worked on?  Several sensor data/telemetry data (time series) catched from the EDEN ISS Greenhouse

What tasks will this project involve?  

1) review, structuring, evaluation, visualization of telemetry data of a greenhouse for research purposes
2) first data analysis with state-of-the-art methods, benchmarks

What makes this project interesting to work on?  The task is situated in a DLR project which aims to develop a greenhouse system for future Moon and Mars missions. There is already a model greenhouse located at the antarctica (EDEN ISS) which collects a lot of telemetry data which should be evaluated in order to detect anomalies or similar phenomena.

What is the expected outcome?   Contribution to research paper, Contribution to software development

What infrastructure, programs and tools will be used? Can they be used remotely?   In the project, our GitLab will be used for development and also to exchange the data – this is accessible remotely. Other tools, like an IDE for Python, etc., can be used locally. For computationally expensive operations we will provide access to a computing cluster.

What skills are necessary for this project?   Data analytics / statistics, Scientific computation, Visualization, experience with Python is helpful

Is the data open source?  No

Interested candidates should be at Master level.  Julia Niebling is looking for 1 visiting scientist, working on the project together with the team.