论文标题
调整MARS科学实验室地面数据系统中数据责任问题的变异自动编码器
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
论文作者
论文摘要
火星好奇的漫游者经常在到达任务运营中心的最终目的地之前，经常寄回通过系统管道的工程和科学数据，使其容易遭受数量损失和数据损坏。地面数据系统分析（GDSA）团队负责监视此信息流以及该数据中异常的检测，以便在必要时请求重新传输。这项工作介绍了$δ$ -MADS，这是一种用于调整各种自动编码器的体系结构和超参数的无衍生优化方法，该方法训练有培训，可通过缺少补丁检测数据，以帮助GDSA团队完成任务。
The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents $Δ$-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.
