/ Machine Learning Seminar of Dr. Dragi Kocev – “Machine learning for space research: Modelling the thermal power consumption of the Mars Express spacecraft”

Machine Learning Seminar of Dr. Dragi Kocev – “Machine learning for space research: Modelling the thermal power consumption of the Mars Express spacecraft”

July 15, 2020
6:30 pm - 7:30 pm

Short Bio

Dragi Kocev is a senior researcher at the Department of Knowledge Technologies, JSI and the CEO and co-founder of Bias Variance Labs. He completed his PhD in 2011 at the JSI Postgraduate School in Ljubljana on the topic of learning ensemble models for predicting structured outputs. He was a visiting research fellow at the University of Bari, Italy in 2014/2015. He has participated in several national Slovenian projects, the EU funded projects IQ, PHAGOSYS and HBP. He was co-coordinator of the FP7 FET Open project MAESTRA. He is currently the principal investigator of two ESA funded projects: GALAXAI – Machine learning for spacecraft operation and AiTLAS – AI prototyping environment for EO. He has been member of the PC of premium AI/ML conferences (e.g., DS, ECML PKDD, AAAI, IJCAI, KDD) and member of the editorial board of Data Mining and Knowledge Discovery, and Ecological Informatics. He served as PC co-chair for DS 2014 and Journal track co-chair for ECML PKDD 2017.


Mars Express (MEX) is a spacecraft operated by the European Space Agency (ESA). It has been orbiting Mars since the beginning of 2004, providing a wealth of scientific invaluable data about Mars such as evidence of the presence of water on  and  below  the  surface  of  the  planet,  three-dimensional renders  of  the  surface  as  well  as  data  about  the  chemical composition of the Martian atmosphere. The scientific payload of MEX consists of a suite of seven instruments  that  provide  global  coverage  of  the  planet’s surface,  subsurface  and  atmosphere.  The  instruments  andon-board  equipment  must  be  kept  within  their  operating temperature ranges, spanning from +50◦C to temperatures as  low  as −180◦C.  In  order  to  maintain  these  predefined operating  temperatures,  the  spacecraft  is  equipped  with  an autonomous thermal control system composed of 33 heater lines  as  well  as  coolers.

The thermal system, that maintains the temperature of the on-board components, consumes a significant amount of the total available electric power, leaving a fraction to be used for science operations. After  more  than  16  years  in  space,  MEX’s  components are  slowly  decaying,  leading  to  reduced  functionality  and ever  decreasing  remaining  lifetime,  e.g.,  the  batteries  are now seriously degraded, making accurate planning and use of the available power essential. To this end, we use explainable methods for structured output prediction to estimate the thermal power consumption of MEX as accurately as possible. The thermal power consumption is estimated under a variety of operating conditions, including gyro-less flying and limited data availability. The used machine learning methods are from the framework of predictive clustering and consist of tree-based ensembles. Besides providing state-of-the-art predictive performance, the methods also facilitate explainability and understanding of the provided estimates.

The talk will be online via Zoom, to register, please submit the form below and we will send you the meeting link.


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