20 citations · 51 across the 9 of their papers we have counts for
9 papers
Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
Saraa Ali, Aleksandr Khizhik, Stepan Svirin +2
The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accura…
Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors
Stepan Svirin, Artem Ryzhikov, Saraa Ali +1
The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accura…
Performance Modeling of Data Storage Systems using Generative Models
Abdalaziz Rashid Al-Maeeni, Aziz Temirkhanov, Artem Ryzhikov +1
High-precision modeling of systems is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various co…
Robust Neural Particle Identification Models
Aziz Temirkhanov, Artem Ryzhikov, Denis Derkach +3
The volume of data processed by the Large Hadron Collider experiments demands sophisticated selection rules typically based on machine learning algorithms. One of the shortcomings…
Latent Neural Stochastic Differential Equations for Change Point Detection
Artem Ryzhikov, Mikhail Hushchyn, Denis Derkach
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series beha…
A Comparison of CPU and GPU implementations for the LHCb Experiment Run 3 Trigger
R. Aaij, M. Adinolfi, S. Aiola +157
The LHCb experiment at CERN is undergoing an upgrade in preparation for the Run 3 data taking period of the LHC. As part of this upgrade the trigger is moving to a fully software i…
Variational Dropout Sparsification for Particle Identification speed-up
Artem Ryzhikov, Denis Derkach, Mikhail Hushchyn
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment. Modern machine learning techniques such as neural networks (NNs) are efficiently…
NFAD: Fixing anomaly detection using normalizing flows
Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin +1
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare ca…
-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin +3
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class…