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首页> 外文会议>AIAA Non-Deterministic Approaches Conference;AIAA Sci'Tech forum >Big Data Analytics for Continuous Assessment of Astronaut Health Risk and Its Application to Human-in-the-Loop (HITL) Related Aerospace
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Big Data Analytics for Continuous Assessment of Astronaut Health Risk and Its Application to Human-in-the-Loop (HITL) Related Aerospace

机译：用于持续评估宇航员健康风险的大数据分析及其在与在环人类（HITL）相关的航空航天中的应用

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The man-instrumentation-equipment-vehicle-environment ecosystem is complex in aerospace missions. Health status of the individual has important implications on decision making and performance that should be factored into assessments for probability of success/risk of failure both in offline and real-time models. To date probabilistic models have not considered the dynamic nature of health status. Big Data analytics is enabling new forms of analytics to assess health status in real-time. There is great potential to integrate dynamic health status information with platforms assessing risk and the probability of success for dynamic individualized real-time probabilistic predictive risk assessment. In this research we present an approach utilizing Big Data analytics to enable continuous assessment of astronaut health risk and show its implications for integration with HITL related aerospace mission.
机译：人体仪器设备车辆环境生态系统在航空航天任务中很复杂。个人的健康状况对决策和绩效具有重要影响，在离线和实时模型中，应将这些健康因素纳入评估成功/失败风险的可能性中。迄今为止，概率模型尚未考虑健康状况的动态性质。大数据分析使新的分析形式能够实时评估健康状况。将动态健康状况信息与评估风险和成功进行动态个性化实时实时概率预测风险评估的可能性的平台集成在一起的潜力很大。在这项研究中，我们提出了一种利用大数据分析的方法，能够对宇航员的健康风险进行持续评估，并显示其与HITL相关的航空航天任务整合的意义。

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