Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionOutlier DetectionSimilar Papers 제목 키워드 기반
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series
Unsupervised fault detection in multivariate time series is critical for maintaining the integrity and efficiency of complex systems, with current methodologies largely focusing on statistical and machine learning techni…
Contrastive LearningData AugmentationDiversityFault Detection+1Chromosomal Structural Abnormality Diagnosis by Homologous Similarity
Pathogenic chromosome abnormalities are very common among the general population. While numerical chromosome abnormalities can be quickly and precisely detected, structural chromosome abnormalities are far more complex a…
Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach
With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communicati…
image-classificationImage ClassificationABD: Default Exception Abduction in Finite First Order Worlds
We introduce ABD, a benchmark for default-exception abduction over finite first-order worlds. Given a background theory with an abnormality predicate and a set of relational structures, a model must output a first-order …
What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow
Machine learning (ML), including deep learning, has recently gained tremendous popularity in a wide range of applications. However, like traditional software, ML applications are not immune to the bugs that result from p…