mlsanityData to 7 Oct 2026

Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

arXiv 2304.12583 ↗First posted 25 Apr 2023eess.SY cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-stationary environments impedes the progress and refinement of related methods. In this paper, a review of methods and techniques for RTSA tasks in non-stationary environments is provided. Specifically, the background and significance of RTSA approaches in non-stationary environments are firstly highlighted. We then present a problem description that covers the definition, classification, and main challenges. We review recent developments in related technologies such as online active learning, online semi-supervised learning, online transfer learning, and online anomaly detection. Finally, we discuss future outlooks and potential directions for further research. Our review aims to provide a comprehensive and up-to-date overview of real-time safety assessment methods in non-stationary environments, which can serve as a valuable resource for researchers and practitioners in this field.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 58 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

9 of 58 references.

References · 9

  1. 2020Learning under Concept Drift: A Reviewcited by 470
  2. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  3. 2018Online Learning: A Comprehensive Surveycited by 203
  4. 2021Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergencecited by 130
  5. 2019Maximum Mean Discrepancy Gradient Flowcited by 123
  6. 2022Open-environment Machine Learningcited by 48
  7. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  8. 2020Active Learning for Human-in-the-Loop Customs Inspectioncited by 4
  9. 2023CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Streamcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.