Academic Journal
Anomaly detection for maritime navigation based on probability density function of error of reconstruction
| العنوان: | Anomaly detection for maritime navigation based on probability density function of error of reconstruction |
|---|---|
| المؤلفون: | Sadeghi Zahra, Matwin Stan |
| المصدر: | Journal of Intelligent Systems, Vol 32, Iss 1, Pp 1-26 (2023) |
| بيانات النشر: | De Gruyter, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Science LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | anomaly detection, time series trajectories, deep learning, autoencoder, probability density function, Science, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Anomaly detection is a fundamental problem in data science and is one of the highly studied topics in machine learning. This problem has been addressed in different contexts and domains. This article investigates anomalous data within time series data in the maritime sector. Since there is no annotated dataset for this purpose, in this study, we apply an unsupervised approach. Our method benefits from the unsupervised learning feature of autoencoders. We utilize the reconstruction error as a signal for anomaly detection. For this purpose, we estimate the probability density function of the reconstruction error and find different levels of abnormality based on statistical attributes of the density of error. Our results demonstrate the effectiveness of this approach for localizing irregular patterns in the trajectory of vessel movements. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2191-026X |
| Relation: | https://doaj.org/toc/2191-026X |
| DOI: | 10.1515/jisys-2022-0270 |
| URL الوصول: | https://doaj.org/article/09851cce627f47e2a14982d9713a0d5b |
| رقم الانضمام: | edsdoj.09851cce627f47e2a14982d9713a0d5b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2191026X |
|---|---|
| DOI: | 10.1515/jisys-2022-0270 |