21 citations · 26 across the 5 of their papers we have counts for
5 papers
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Alexander Marusov, Dmitry Anikin, Petr Sokerin +3
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generat…
BinConv: A Neural Architecture for Ordinal Encoding in Time-Series Forecasting
Andrei Chernov, Vitaliy Pozdnyakov, Ilya Makarov
Recent work in time series forecasting has explored reformulating regression as a classification task. By discretizing the continuous target space into bins and predicting over a f…
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov +2
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of th…
Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data
Alexander Kovalenko, Vitaliy Pozdnyakov, Ilya Makarov
Timely detected anomalies in the chemical technological processes, as well as the earliest detection of the cause of the fault, significantly reduce the production cost in the indu…
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
Maksim Golyadkin, Vitaliy Pozdnyakov, Leonid Zhukov +1
Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to…