通过自适应性噪音速率估计,对噪音的船舶轨迹进行强有力的学习
Haoyu Yang1, Mao Wang1, Zhihao Chen1
1Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了A-JoCoR,这是一种用于船只轨迹分类的新方法,可以适应杂的自动识别系统 (AIS) 数据,而不需要先前的噪音率信息. 它显著提高了海上安全分类的准确性.
科学领域:
- 海上安全是海上安全.
- 机器学习 机器学习
- 数据科学是数据科学.
背景情况:
- 船舶轨迹分类对于海上分析和安全至关重要.
- 非法船只伪造自动识别系统 (AIS) 数据,引入标签噪声,降低了分类准确性.
- 对于噪音标签的现有抽样选择方法通常需要预先确定的噪音率.
研究的目的:
- 开发一种可靠的船舶轨迹分类方法,能够适应性地处理标签噪声.
- 为了解决现有方法的局限性,这些方法需要对噪声率的预先了解.
- 为了提高船舶分类的准确性和可靠性,在存在被操纵的AIS数据的情况下.
主要方法:
- 提出了一种适应性学习机制,可以在没有先决条件的情况下运行.
- 整合了这个机制与JoCoR (联合培训与共同规范化) 强大的培训范式.
- 开发了一个名为A-JoCoR的新型噪音率自适应学习强健训练范式.
主要成果:
- 在培训过程中,A-JoCoR证明了数据噪声率的有效自适应学习.
- 在真实世界船舶轨迹数据上的实验结果证实了A-JoCoR的有效性.
- 与原来的JoCoR方法相比,在分类性能上观察到显著的改进.
结论:
- A-JoCoR提供了一种可靠的解决方案,用于使用杂的AIS数据对船舶轨迹进行分类.
- 该方法成功地适应未知的噪声率,增强海上安全治理.
- A-JoCoR代表了从海上应用的噪音标签中学习的重大进步.
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