1,821 research outputs found
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System
Critical infrastructures like water treatment facilities and power plants
depend on industrial control systems (ICS) for monitoring and control, making
them vulnerable to cyber attacks and system malfunctions. Traditional ICS
anomaly detection methods lack transparency and interpretability, which make it
difficult for practitioners to understand and trust the results. This paper
proposes a two-phase dual Copula-based Outlier Detection (COPOD) method that
addresses these challenges. The first phase removes unwanted outliers using an
empirical cumulative distribution algorithm, and the second phase develops two
parallel COPOD models based on the output data of phase 1. The method is based
on empirical distribution functions, parameter-free, and provides
interpretability by quantifying each feature's contribution to an anomaly. The
method is also computationally and memory-efficient, suitable for low- and
high-dimensional datasets. Experimental results demonstrate superior
performance in terms of F1-score and recall on three open-source ICS datasets,
enabling real-time ICS anomaly detection.Comment: 11 pages, 9 figures, journal articl
Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection
There is a need for affordable, widely deployable maternal-fetal ECG monitors
to improve maternal and fetal health during pregnancy and delivery. Based on
the diffusion-based channel selection, here we present the mathematical
formalism and clinical validation of an algorithm capable of accurate
separation of maternal and fetal ECG from a two channel signal acquired over
maternal abdomen
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks
A Survey on Global LiDAR Localization
Knowledge about the own pose is key for all mobile robot applications. Thus
pose estimation is part of the core functionalities of mobile robots. In the
last two decades, LiDAR scanners have become a standard sensor for robot
localization and mapping. This article surveys recent progress and advances in
LiDAR-based global localization. We start with the problem formulation and
explore the application scope. We then present the methodology review covering
various global localization topics, such as maps, descriptor extraction, and
consistency checks. The contents are organized under three themes. The first is
the combination of global place retrieval and local pose estimation. Then the
second theme is upgrading single-shot measurement to sequential ones for
sequential global localization. The third theme is extending single-robot
global localization to cross-robot localization on multi-robot systems. We end
this survey with a discussion of open challenges and promising directions on
global lidar localization
Semi-supervised multiscale dual-encoding method for faulty traffic data detection
Inspired by the recent success of deep learning in multiscale information
encoding, we introduce a variational autoencoder (VAE) based semi-supervised
method for detection of faulty traffic data, which is cast as a classification
problem. Continuous wavelet transform (CWT) is applied to the time series of
traffic volume data to obtain rich features embodied in time-frequency
representation, followed by a twin of VAE models to separately encode normal
data and faulty data. The resulting multiscale dual encodings are concatenated
and fed to an attention-based classifier, consisting of a self-attention module
and a multilayer perceptron. For comparison, the proposed architecture is
evaluated against five different encoding schemes, including (1) VAE with only
normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both
normal and faulty data encodings, but without attention module in the
classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)
encoding. The first four encoding schemes adopted the same convolutional neural
network (CNN) architecture while the fifth encoding scheme follows the
transformer architecture of CViT. Our experiments show that the proposed
architecture with the dual encoding scheme, coupled with attention module,
outperforms other encoding schemes and results in classification accuracy of
96.4%, precision of 95.5%, and recall of 97.7%.Comment: 16 pages, 8 figure
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