We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …

Anomaly detection is a long-standing problem in system designation. High-quality anomaly
detection can benefit plenty of applications (eg system monitoring, disaster precaution and …

Much of the worlds data is streaming, time-series data, where anomalies give significant
information in critical situations. Yet detecting anomalies in streaming data is a difficult task …

This article presents an algorithm to detect outliers in seasonal, univariate network traffic
data using Gaussian Mixture Models (GMMs). Additionally we show that this methodology …

Wire Arc Additive Manufacturing (WAAM) offers the possibility to build up large-scale metal
parts. Data which is obtained from a multivariate sensor system in-situ must be analyzed …

Anomaly detection is an important research direction, which takes the real-time information
system from different sensors and conditional information sources into consideration. Based …

Outlier detection is an increasingly important and intensively developing area of research.
This paper focuses on the problem of outlier detection in data streams. It presents a …

Time series are one of the most common data types encountered by data scientists and, in
the context of today's exponentially increasing data, learning how to best model them to …

This report explores the possibilities and applications of artificial intelligence (AI) within the
various stages of a cyberattack. It is a literature review of the current state of the art in AI …

A key goal in structural health monitoring is to detect abnormal events in a structure's
behavior by interpreting its observed responses over time. The goal is to develop an …