UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection
InformationMulti-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity.
Jianmei Zhong, Yanzhi Song
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]
PLoS ONE, 2016 Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
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Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework [PDF]
, 2023 openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention.
BUGARIN, NIKOLA
core
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data
Advanced Engineering Materials, EarlyView.Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour +5 more
wiley +1 more source
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
IEEE AccessAnomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic.
Kamil Faber +3 more
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Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization
Jisuanji kexue yu tansuo, 2020 Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
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Machine learning for Internet of things anomaly detection under low-quality data
International Journal of Distributed Sensor Networks, 2022 With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security.
Shangbin Han, Qianhong Wu, Yang Yang
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Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction
IEEE AccessHyperspectral anomaly detection is a detection of abnormal targets in a region based on spectral and spatial information under the premise of no prior knowledge of the target, which is a very important research topic in the field of remote sensing.
Jiao Jiao, Longlong Xiao, Chonglei Wang
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