Fault detection in Tennessee Eastman process with temporal deep learning models
Automated early process fault detection and prediction remains a challenging problem in
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
Graph neural networks based detection of stealth false data injection attacks in smart grids
False data injection attacks (FDIAs) represent a major class of attacks that aim to break the
integrity of measurements by injecting false data into the smart metering devices in power …
integrity of measurements by injecting false data into the smart metering devices in power …
Leveraging SMEs technologies adoption in the Covid-19 pandemic: A case study on Twitter-based user-generated content
JR Saura, D Palacios-Marqués… - The Journal of …, 2023 - Springer
The COVID-19 pandemic has caused many entrepreneurs and small and medium
enterprises (SMEs) to adapt their business models and business strategies to the …
enterprises (SMEs) to adapt their business models and business strategies to the …
A new support vector data description method for machinery fault diagnosis with unbalanced datasets
L Duan, M Xie, T Bai, J Wang - Expert Systems with Applications, 2016 - Elsevier
In machinery fault diagnosis area, the obtained data samples under faulty conditions are
usually far less than those under normal condition, resulting in unbalanced dataset issue …
usually far less than those under normal condition, resulting in unbalanced dataset issue …
A support vector domain description approach to supervised classification of remote sensing images
J Muñoz-Marí, L Bruzzone… - IEEE Transactions on …, 2007 - ieeexplore.ieee.org
This paper addresses the problem of supervised classification of remote sensing images in
the presence of incomplete (nonexhaustive) training sets. The problem is analyzed …
the presence of incomplete (nonexhaustive) training sets. The problem is analyzed …
Efficient differentially private kernel support vector classifier for multi-class classification
In this paper, we propose a multi-class classification method using kernel supports and a
dynamical system under differential privacy. For small datasets, kernel methods, such as …
dynamical system under differential privacy. For small datasets, kernel methods, such as …
Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options
We investigated the performance of parametric and non-parametric methods concerning the
in-sample pricing and out-of-sample prediction performances of index options. Comparisons …
in-sample pricing and out-of-sample prediction performances of index options. Comparisons …
Generalized support vector data description for anomaly detection
Traditional anomaly detection procedures assume that normal observations are obtained
from a single distribution. However, due to the complexities of modern industrial processes …
from a single distribution. However, due to the complexities of modern industrial processes …
Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine
Z Xing, J Qu, Y Chai, Q Tang, Y Zhou - Journal of Mechanical Science and …, 2017 - Springer
The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under
variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault …
variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault …
Deep learning-based multilevel classification of Alzheimer's disease using non-invasive functional near-infrared spectroscopy
The timely diagnosis of Alzheimer's disease (AD) and its prodromal stages is critically
important for the patients, who manifest different neurodegenerative severity and …
important for the patients, who manifest different neurodegenerative severity and …