Multivariate synchronization curve: A measure of synchronization in different multivariate signals

2021 ◽  
Vol 31 (12) ◽  
pp. 123121
Author(s):  
Binbin Shang ◽  
Pengjian Shang
Keyword(s):  
Multivariate Signals

An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction

2021 ◽  
pp. 1748006X2199052
Author(s):  
Yaping Li ◽  
Enrico Zio ◽  
Ershun Pan
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Average Run Length ◽  
Moving Average ◽  
Model Parameters ◽  
Current Time ◽  
Multivariate Processes ◽  
Industrial Systems ◽  
Hidden States ◽  
Multivariate Signals

Degradation is an unavoidable phenomenon in industrial systems. Hidden Markov models (HMMs) have been used for degradation modeling. In particular, segmental HMMs have been developed to model the explicit relationship between degradation signals and hidden states. However, existing segmental HMMs deal only with univariate cases, whereas in real systems, signals from various sensors are collected simultaneously, which makes it necessary to adapt the segmental HMMs to deal with multivariate processes. Also, to make full use of the information from the sensors, it is important to differentiate stable signals from deteriorating ones, but there is no good way for this, especially in multivariate processes. In this paper, the multivariate exponentially weighted moving average (MEWMA) control chart is employed to identify deteriorating multivariate signals. Specifically, the MEWMA statistic is used as a comprehensive indicator for differentiating multivariate observations. Likelihood Maximization is used to estimate the model parameters. To avoid underflow, the forward and backward probabilities are normalized. In order to assess degradation, joint probabilities are defined and derived. Further, the occurrence probability of each degradation state at the current time, as well as in the future, is derived. The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of NASA is employed for comparative analysis. In terms of degradation assessment and prediction, the proposed model performs very well in general. By sensitivity analysis, we show that in order to improve further the performance of the method, the weight of the chart should be set relatively small, whereas the method is not sensitive to the change of the in-control average run length (ARL).

scholarly journals Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals

2012 ◽  
Vol 60 (4) ◽  
pp. 1597-1611 ◽  
Author(s):  
Quentin Barthelemy ◽  
Anthony Larue ◽  
Aurélien Mayoue ◽  
David Mercier ◽  
Jérôme I. Mars
Keyword(s):  
Sparse Coding ◽  
Rotation Invariant ◽  
Multivariate Signals

scholarly journals A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

2013 ◽  
Vol 23 (3) ◽  
pp. 780-790 ◽  
Author(s):  
Amir Omidvarnia ◽  
Ghasem Azemi ◽  
Paul B. Colditz ◽  
Boualem Boashash
Keyword(s):  
Phase Synchrony ◽  
Time Frequency ◽  
Multivariate Signals

An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals

2018 ◽  
Vol 14 (4) ◽  
Author(s):  
Omkar Singh ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Wavelet Transform ◽  
Multivariate Data ◽  
Heterogeneous Data ◽  
Physiological Signals ◽  
Data Series ◽  
Data Set ◽  
Processing Application ◽  
Adaptive Wavelet ◽  
Empirical Wavelet Transform ◽  
Multivariate Signals

Abstract Background This article proposes an extension of empirical wavelet transform (EWT) algorithm for multivariate signals specifically applied to cardiovascular physiological signals. Materials and methods EWT is a newly proposed algorithm for extracting the modes in a signal and is based on the design of an adaptive wavelet filter bank. The proposed algorithm finds an optimum signal in the multivariate data set based on mode estimation strategy and then its corresponding spectra is segmented and utilized for extracting the modes across all the channels of the data set. Results The proposed algorithm is able to find the common oscillatory modes within the multivariate data and can be applied for multichannel heterogeneous data analysis having unequal number of samples in different channels. The proposed algorithm was tested on different synthetic multivariate data and a real physiological trivariate data series of electrocardiogram, respiration, and blood pressure to justify its validation. Conclusions In this article, the EWT is extended for multivariate signals and it was demonstrated that the component-wise processing of multivariate data leads to the alignment of common oscillating modes across the components.

scholarly journals Modulated oscillations in many dimensions

2013 ◽  
Vol 371 (1984) ◽  
pp. 20110551 ◽  
Author(s):  
S. C. Olhede
Keyword(s):  
Physical Oceanography ◽  
Time Varying ◽  
Signal Component ◽  
One Dimensional ◽  
The Common ◽  
The Difference ◽  
Common Signal ◽  
Multivariate Signals ◽  
Special Case ◽  
Signal Models

Modulated oscillations are described via their time-varying amplitude and frequency. For multivariate signals, there is structure in the signal beyond this local amplitude and frequency defined for each signal component, in turn describing the commonality of the components. The multivariate structure encodes how the common oscillation is present in each component signal. This structure will also be evolving. I review the special case of the representation of both bivariate and trivariate oscillations. Additionally, existing results on the general multivariate oscillation are covered. I discuss the difference between a model of a multivariate oscillation compared with other common signal models of phenomena observed in several channels, and how their properties are different. I show how for the multivariate signal the global dimensionality of the signal is built up from local one-dimensional contributions, and introduce the purely unidirectional signal, to quantify how any given signal is different from the closest such signal. I illustrate the properties of the derived representation of the multivariate signal with synthetic examples, and discuss the representation of data from observations in physical oceanography.

Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

2013 ◽  
Vol 103 (5) ◽  
pp. 50004 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Xin-Wang Zhang ◽  
Ning-De Jin ◽  
Reik V. Donner ◽  
Norbert Marwan ◽  
...  
Keyword(s):  
Stratified Flows ◽  
Oil Water ◽  
Multivariate Signals ◽  
Dynamic Transitions
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