Cross-Domain Few-Shot Anomaly Detection for equipment in nuclear power plants

Junjie He; Sheng Zheng; Shuang Yi; Senquan Yang; Zhongjie Huan

doi:10.1016/j.nucengdes.2025.113956

ScienceGate Book Chapters

JOURNAL ARTICLE

Cross-Domain Few-Shot Anomaly Detection for equipment in nuclear power plants

Junjie He Sheng Zheng Shuang Yi Senquan Yang Zhongjie Huan

Year: 2025 Journal: Nuclear Engineering and Design Vol: 436 Pages: 113956-113956 Publisher: Elsevier BV

DOI: 10.1016/j.nucengdes.2025.113956

Get Full-Text PDF Get Analytical Report

Keywords:

Shot (pellet) Nuclear power Domain (mathematical analysis) Anomaly detection Anomaly (physics) Nuclear engineering Nuclear power plant One shot Power (physics) Engineering Nuclear physics Physics Computer science Mechanical engineering Materials science Data mining Mathematics Condensed matter physics

Metrics

Cited By

3.83

FWCI (Field Weighted Citation Impact)

Refs

0.85

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Risk and Safety Analysis

Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty

Geophysical Methods and Applications

Physical Sciences → Engineering → Ocean Engineering

Nuclear Engineering Thermal-Hydraulics

Physical Sciences → Engineering → Aerospace Engineering

Cross-Domain Few-Shot Anomaly Detection for equipment in nuclear power plants

Metrics

Citation History

Topics

Related Documents

Style-Aware Cross Domain Few-Shot Anomaly Detection

Towards Cross-Domain Few-Shot Graph Anomaly Detection

Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain Images

Few-Shot Learning for Abnormal Event Detection in Nuclear Power Plants

Few-shot time-series anomaly detection with unsupervised domain adaptation