Study on wavelet neural network based anomaly detection in ocean observing data series
Y Wang, L Han, W Liu, S Yang, Y Gao - Ocean Engineering, 2019 - Elsevier
In this paper, a novel method is presented for detecting anomalies in ocean fixed-point
observing time series, which combines wavelet neural network (WNN), classifying threshold …
observing time series, which combines wavelet neural network (WNN), classifying threshold …
A novel quality control method of time-series ocean wave observation data combining deep-learning prediction and statistical analysis
J Xie, H Jiang, W Song, J Yang - Journal of Sea Research, 2023 - Elsevier
Quality control (QC) of marine data is a critical aspect in ensuring the usability of oceanic
data. In this paper, we propose a novel QC method for time-series ocean wave data, which …
data. In this paper, we propose a novel QC method for time-series ocean wave data, which …
Anomaly detection for non-stationary and non-periodic univariate time series
YL Li, JR Jiang - 2020 IEEE Eurasia Conference on IOT …, 2020 - ieeexplore.ieee.org
This study proposes an anomaly detection method called wavelet autoencoder anomaly
detection (WAAD) for non-stationary and non-periodic univariate time series. The method …
detection (WAAD) for non-stationary and non-periodic univariate time series. The method …
Detecting interesting and anomalous patterns in multivariate time-series data in an offshore platform using unsupervised learning
IS Figueirêdo, TF Carvalho, WJD Silva… - Offshore Technology …, 2021 - onepetro.org
Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines
can help to avoid production losses, environmental disasters, and human fatalities, besides …
can help to avoid production losses, environmental disasters, and human fatalities, besides …
[PDF][PDF] Outlier detection in ocean wave measurements by using unsupervised data mining methods
K Mahmoodi, H Ghassemi - Polish Maritime Research, 2018 - sciendo.com
Outliers are considerably inconsistent and exceptional objects in the data set that do not
adapt to expected normal condition. An outlier in wave measurements may be due to …
adapt to expected normal condition. An outlier in wave measurements may be due to …
Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform
The quest for more efficient real-time detection of anomalies in time series data is critically
important in numerous applications and systems ranging from intelligent transportation …
important in numerous applications and systems ranging from intelligent transportation …
Real-time maritime traffic anomaly detection based on sensors and history data embedding
The automated identification system of vessel movements receives a huge amount of
multivariate, heterogeneous sensor data, which should be analyzed to make a proper and …
multivariate, heterogeneous sensor data, which should be analyzed to make a proper and …
Ensembled deep learning approach for maritime anomaly detection system
The maritime anomaly detection is an essential part for ensuring curity for any nation. The
ships in a sea follow a common route for a particular source and destination pair. This paper …
ships in a sea follow a common route for a particular source and destination pair. This paper …
Anomaly detection using explainable random forest for the prediction of undesirable events in oil wells
The worldwide demand for oil has been rising rapidly for many decades, being the first
indicator of economic development. Oil is extracted from underneath reservoirs found below …
indicator of economic development. Oil is extracted from underneath reservoirs found below …
Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring
The importance of anomaly detection in multivariate time series has led to the development
of several prominent deep learning solutions. As a part of the anomaly detection process …
of several prominent deep learning solutions. As a part of the anomaly detection process …