The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system.
We then develop an anomaly detection method based on the analysis results and explore the possibilities of other potential applications.
In GEFCom2014, a winning team JIngrui Xie developed an anomaly detection method based on the Vanilla model with a fixed threshold [14].
We then propose a real-time anomaly detection method based on the DRM for the corrupted load data, which can be further cleansed by replacing the detected anomalies with the forecasted hourly load from last sliding simulation.
In this paper, we will propose an algorithm based on anomaly detection method with residual subspace projection theory for some status of human being detection through brick wall.
The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods.
Currently, anomaly detection methods include anomaly detection based on classification, anomaly detection based on the nearest neighbor method, anomaly detection based on clustering, statistical anomaly detection, anomaly detection based on information theory, spectral theory anomaly detection, and so on [1].
Botnets are becoming more sophisticated and dangerous day-by-day, and most of the existing rule based and flow based detection methods may not be capable of detecting bot activities in an efficient and effective manner.
In this section, we first introduce three anomaly detection methods, and then propose a DRM based detection method with an adaptive threshold.
Several countries (such as the Netherlands and Italy) have moved from cytology based to human papillomavirus DNA based detection methods as their primary cervical screening method.
Fuzzy model based fault detection methods are elaborated envisaging fault tolerant control.
