On a topological classification of recurrence plots: Application to noise perturbed molecular dynamics time series

2019 ◽  
Vol 29 (2) ◽  
pp. 023113 ◽  
Author(s):  
Theodoros E. Karakasidis ◽  
Ioannis Andreadis ◽  
Athanasios D. Fragkou
Keyword(s):  
Molecular Dynamics ◽  
Time Series ◽  
Topological Classification ◽  
Recurrence Plots

scholarly journals A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Sichen Li ◽  
Mélissa Zacharias ◽  
Jochem Snuverink ◽  
Jaime Coello de Portugal ◽  
Fernando Perez-Cruz ◽  
...  
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Multivariate Time Series ◽  
Proton Accelerator ◽  
Particle Accelerators ◽  
Recurrence Plots ◽  
Time Loss ◽  
Safety Measures ◽  
Novel Approach

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals High-pass filtering artifacts in multivariate classification of neural time series data

2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multivariate Classification ◽  
High Pass
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