A methodology based on deep recurrent models for maritime surveillance, over
publicly available Automatic Identification System (AIS) data, is presented in
this paper. The setup employs a deep Recurrent Neural Network (RNN)-based
model, for encoding and reconstructing the observed ships' motion patterns. Our
approach is based on a thresholding mechanism, over the calculated errors
between observed and reconstructed motion patterns of maritime vessels.
Specifically, a deep-learning framework, i.e. an encoder-decoder architecture,
is trained using the observed motion patterns, enabling the models to learn and
predict the expected trajectory, which will be compared to the effective ones.
Our models, particularly the bidirectional GRU with recurrent dropouts,
showcased superior performance in capturing the temporal dynamics of maritime
data, illustrating the potential of deep learning to enhance maritime
surveillance capabilities. Our work lays a solid foundation for future research
in this domain, highlighting a path toward improved maritime safety through the
innovative application of technology.

基于深度循环模型的海上监视方法，利用公开可用的自动识别系统（AIS）数据，通过对观测到的船舶运动模式进行编码和重建，结合阈值机制对计算的观测和重建的运动模式之间的误差进行处理，通过训练深度学习框架（编码器 - 解码器架构）使用观测到的运动模式，使模型能够学习和预测预期的轨迹，并与实际轨迹进行比较，研究展示了具有回归丢失的双向门控循环单元（GRU）在捕捉海上数据的时间动态方面表现出卓越的性能，彰显了深度学习在增强海上监视能力方面的潜力，为未来领域的研究奠定了坚实基础，通过技术的创新应用为改善海上安全指明了道路。

应用 AIS 数据和深度循环架构进行海上环境中的异常检测

Outlier detection in maritime environments using AIS data and deep  recurrent architectures

The Large Scale Visual Recognition Challenge based on the well-known Imagenet
dataset catalyzed an intense flurry of progress in computer vision. Benchmark
tasks have propelled other sub-fields of machine learning forward at an equally
impressive pace, but in healthcare it has primarily been image processing
tasks, such as in dermatology and radiology, that have experienced similar
benchmark-driven progress. In the present study, we performed a comprehensive
review of benchmarks in medical machine learning for structured data,
identifying one based on the Medical Information Mart for Intensive Care
(MIMIC-III) that allows the first direct comparison of predictive performance
and thus the evaluation of progress on four clinical prediction tasks:
mortality, length of stay, phenotyping, and patient decompensation. We find
that little meaningful progress has been made over a 3 year period on these
tasks, despite significant community engagement. Through our meta-analysis, we
find that the performance of deep recurrent models is only superior to logistic
regression on certain tasks. We conclude with a synthesis of these results,
possible explanations, and a list of desirable qualities for future benchmarks
in medical machine learning.

综合评估医疗机器学习的基准，提出一种基于医学信息志 (MIMIC-III) 的基准，让我们可以直接比较预测性能，并评估生存率、住院时间、表型和患者恶化任务的进展。我们发现，在这些任务上，尽管社区参与度高，但在过去的三年里，几乎没有真正显著的进展。通过我们的元分析，我们发现，深度递归模型的性能仅在某些任务上优于逻辑回归。最后综合这些结果，提出未来医学机器学习基准所需的理想特性。