A review of distributed acoustic sensing applications for railroad condition monitoring
Accurate condition monitoring has been a major challenge among railroad management
authorities as they work to minimize collisions that lead to fatalities or damage to railroads …
authorities as they work to minimize collisions that lead to fatalities or damage to railroads …
A critical review of common log data sets used for evaluation of sequence-based anomaly detection techniques
Log data store event execution patterns that correspond to underlying workflows of systems
or applications. While most logs are informative, log data also include artifacts that indicate …
or applications. While most logs are informative, log data also include artifacts that indicate …
Leveraging machine learning for cybersecurity resilience in industry 4.0: Challenges and future directions
J Yu, AV Shvetsov, SH Alsamhi - IEEE Access, 2024 - ieeexplore.ieee.org
Industry 4.0, where the convergence of digital technology impacts industrial operations and
processes, is characterized by cybersecurity resilience. Therefore, in Industry 4.0, Machine …
processes, is characterized by cybersecurity resilience. Therefore, in Industry 4.0, Machine …
Tele-knowledge pre-training for fault analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a
crucial task in telecommunication applications that requires a wide range of knowledge …
crucial task in telecommunication applications that requires a wide range of knowledge …
A novel physics-informed deep learning strategy with local time-updating discrete scheme for multi-dimensional forward and inverse consolidation problems
H Guo, ZY Yin - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Physics-informed deep learning (PIDL) offers innovative and powerful ways for spatio-
temporal soil consolidation analysis. However, status quo applications employ physics …
temporal soil consolidation analysis. However, status quo applications employ physics …
[HTML][HTML] A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs
Unsupervised anomaly detection (UAD) is a diverse research area explored across various
application domains. Over time, numerous anomaly detection techniques, including …
application domains. Over time, numerous anomaly detection techniques, including …
Loggd: Detecting anomalies from system logs with graph neural networks
Log analysis is one of the main techniques engineers use to troubleshoot faults of large-
scale software systems. During the past decades, many log analysis approaches have been …
scale software systems. During the past decades, many log analysis approaches have been …
Multivariate Log-based Anomaly Detection for Distributed Database
Distributed databases are fundamental infrastructures of today's large-scale software
systems such as cloud systems. Detecting anomalies in distributed databases is essential …
systems such as cloud systems. Detecting anomalies in distributed databases is essential …
Log parsing evaluation in the era of modern software systems
Due to the complexity and size of modern software systems, the amount of logs generated is
tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time …
tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time …
Interpretable spatial–temporal graph convolutional network for system log anomaly detection
To ensure seamless information flow and operational integrity, computer systems need
effectively to manage their system logs, but the expansion in their scale and complexity …
effectively to manage their system logs, but the expansion in their scale and complexity …