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Open access Sep 2026

Anomaly Detection in Real-World Seismic Time Series: Evaluation of Timer Model with COGNOS Framework

Time series anomaly detection is one of the core tasks in time series data analysis, aiming to identify abnormal events or behaviors from normal temporal data, and plays a critically important role across numerous domains. In seismic data analysis, the identification of anomaly intervals preceding earthquake occurrence...

Xiao Pei, Wen-Zhuo Chen, Wei Li et al. · 0 citations
Open access Sep 2026

A Magnetic Anomaly Detection Method Based on Multi-Feature Classification-Fusion Neural Network in Colored Noise Background

Magnetic anomaly detection (MAD) is a core technology for the detection of ferromagnetic targets, yet traditional methods such as the Orthogonal basis function (OBF) detector suffer from severe performance degradation in low signal-to-noise ratio (SNR) and Gaussian colored noise environments. To address this issue, thi...

Zhuo Chen, Wen-Bin Xie, Shu-Chang Liu et al. · 0 citations
Review Open access Sep 2026

A Review of Machine Learning-Based Time-Series Anomaly Detection in the Water Domain

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles from th...

Zhuang Liu, Zheng Wang, Cheng-Cheng Ding et al. · 0 citations
Conference

Timeseries Foundation Model for Anomaly Detection in Manufacturing Sensor Data

A time-series foundation model specifically designed for anomaly detection in large-scale industrial datasets is proposed, incorporating both reconstruction error and embedding vector-based anomaly scores, leveraging the effectiveness of embedding-based anomaly detection in simple time-series data.

Young Myoung Ko, Jongwon Kim, Yoon Samuel et al. · 0 citations
Conference Sep 2026

Development of Scan Statistics Methods for Predicting Anomalies in Information and Energy Systems

The main objective of the research is to develop high-performance intelligent software tools for solving complex problems related to the analysis of spatio-temporal data and anomaly detection using scan statistics methods. In the field of electric power engineering, this approach has demonstrated high efficiency in for...

Alexander L. Reznik, A. Soloviev · 0 citations
Oct 2026

An integrated activity tracking based anomaly detection framework for mining dumpers in surface coal mines

Mining dumpers operate under highly variable loading and environmental conditions, making reliable condition monitoring challenging in surface coal mines. Conventional vibration-based monitoring approaches generally treat activity tracking and anomaly detection as independent tasks, ignoring the operational context in...

Nagesh Dewangan, A. Mohanty · 0 citations

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