Method and device for training and predicting a conjunction parameter from conjunction data messages
AssigneeNeuraspace, S.A.
InventorMarta Isabel Vaz Guimarães Pinto De Melo
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Abstract
The present document discloses a computer-implemented method for predicting a conjunction parameter from conjunction data messages and the respective method for training a transformer-based machine-learning model for predicting a conjunction parameter. It is also disclosed a device comprising a computer-readable medium comprising the trained transformer-based machine-learning model and a computer configured to carry out the training method of the transformer-based machine-learning model.
Claims17
A computer-implemented method for training a transformer-based machine-learning model for predicting a conjunction parameter from conjunction data messages, CDMs, wherein a conjunction is a pair of orbiting objects, the method comprising:
- receiving a dataset comprising a conjunction time series for each conjunction, wherein each said series is irregularly spaced and comprises a plurality of CDM records, each CDM record comprising a time to TCA, Time of Closest Approach, and a conjunction parameter;
- generating a CDM array comprising a plurality of cells, one cell for each CDM record, organized in rows, one row for each conjunction time series, and a plurality of columns, where each row is right-aligned such that a rightmost column of the CDM array comprises the most recent CDM record for each conjunction time series;
- generating a mask for the CDM array where each CDM record is masked if the time to TCA of said each CDM record is lower than a predetermined time to TCA threshold;
- encoding each CDM record of the CDM array by embedding the conjunction parameter into an embedded encoding using an artificial neural-network;
- training a transformer-based machine-learning model by using, as training input, the CDM array masked by said mask and using, as training output, the CDM array, wherein each said CDM record is a token for transformer-based machine-learning model,
- wherein the conjunction parameter is a spatial conjunction parameter which comprises a position uncertainty at the TCA.
A device comprising a non-transitory computer-readable medium, the non-transitory computer-readable medium having stored thereon instructions that, when executed by the device, cause the device to train a transformer-based machine-learning model for predicting a conjunction parameter from conjunction data messages, CDMs, wherein a conjunction is a pair of orbiting objects, by:
- receiving a dataset comprising a conjunction time series for each conjunction, wherein each said series is irregularly spaced and comprises a plurality of CDM records, each CDM record comprising a time to TCA, Time of Closest Approach, and a conjunction parameter;
- generating a CDM array comprising a plurality of cells, one cell for each CDM record, organized in rows, one row for each conjunction time series, and a plurality of columns, where each row is right-aligned such that a rightmost column of the CDM array comprises the most recent CDM record for each conjunction time series;
- generating a mask for the generated CDM array where each CDM record is masked if the time to TCA of said each CDM record is lower than a predetermined time to TCA threshold;
- encoding each CDM record of the CDM array by embedding the conjunction parameter into an embedded encoding using an artificial neural-network;
- training a transformer-based machine-learning model by using, as training input, the generated CDM array masked by said mask and using, as training output, the generated CDM array,
- wherein each said CDM record is a token for transformer-based machine-learning model,
- wherein the conjunction parameter is a spatial conjunction parameter which comprises a position uncertainty at the TCA.
A non-transitory computer-readable medium comprising computer program instructions that, when executed by a computer, cause the computer to carry out the steps of:
- receiving a dataset comprising a conjunction time series for each conjunction, wherein each said series is irregularly spaced and comprises a plurality of CDM records, each CDM record comprising a time to TCA, Time of Closest Approach, and a conjunction parameter;
- generating a CDM array comprising a plurality of cells, one cell for each CDM record, organized in rows, one row for each conjunction time series, and a plurality of columns, where each row is right-aligned such that a rightmost column of the CDM array comprises the most recent CDM record for each conjunction time series;
- generating a mask for the generated CDM array where each CDM record is masked if the time to TCA of said each CDM record is lower than a predetermined time to TCA threshold;
- encoding each CDM record of the CDM array by embedding the conjunction parameter into an embedded encoding using an artificial neural-network;
- training a transformer-based machine-learning model by using, as training input, the generated CDM array masked by said mask and using, as training output, the generated CDM array,
- wherein each said CDM record is a token for transformer-based machine-learning model,
- wherein the conjunction parameter is a spatial conjunction parameter which comprises a position uncertainty at the TCA.
Dependencies are inferred from the claim text.