图 书 馆
使用深度时态多实例学习解释航空安全事件Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning |
|
| 课程网址: | http://videolectures.net/kdd2018_das_janakiraman_aviation_safety/ |
| 主讲教师: | Kamalika Das |
| 开课单位: | NASA艾姆斯研究中心 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL’s ability to learn using weakly supervised data and DRNN’s ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model’s abilities and shortcomings, with some final remarks about possible deployment directions. |
| 关 键 词: | 美国联邦航空管理局; 对系统健康和安全监测; 结合多实例学习; DRNN建模时间行为的能力 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-02-09：cyh |
| 最后编审: | 2023-02-09：cyh |
| 阅读次数: | 25 |