Multivariate Multiscale Entropy Analysis Research Articles

Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis

Abstract Due to the complex flow structure and non-uniform phase distribution in the vertical upward gas-liquid two-phase flow, an eight-electrode rotating electric field conductance sensor is used to obtain multi-channel conductance signals. The flow patterns of the vertical upward gas-liquid two-phase flow are classified according to the images obtained from a high-speed camera. Then, we employ the multivariate weighted multi-scale permutation entropy (MWMPE) to detect the instability of flow pattern transition in the gas-liquid two-phase flow. Afterwards, we compare the results of the MWMPE with those of the single-channel weighted multi-scale permutation entropy (SCWMPE) and multivariate multi-scale sample entropy (MMSE). The comparison results indicate that, compared with the SCWMPE and MMSE, the MWMPE has superior performance in terms of the high-resolution presentation of flow instability in the gas-liquid two-phase flow. Finally, we extract the mean value of the MWMPE in whole scales and the entropy rate of the MWMPE in the small scales. The results indicate that the normalized mean value and normalized entropy rate of MWMPE are very sensitive to the transitions of flow patterns, thus allowing the detection of the instability of flow pattern transition.

Evaluation of effects of balance training from using wobble board-based exergaming system by MSE and MMSE techniques

Balance is an important criterion in assessing health. The lack of exercise of most young people leads to an increased risk of balance abnormity as part of irregular lifestyles. This paper examines the effect of training with an interactive exercise game on young people’s balance after 1 month. The experiment and analysis followed a three-stage design. Initially, gait function testing was conducted to determine a datum point for the situation at the outset; next, a Gym Top balance trainer was used for training; finally, balance was measured with a force plate and center of pressure signals were collected. Differences in balance before and after training were analyzed with multiscale entropy and multivariate multiscale entropy methods. 12 healthy young people were recruited for the experiment. Statistical analysis of the gait test observations revealed that the number of times of standing and sitting increased significantly after training. Multiscale entropy analysis showed that the multiscale entropy curve was higher after training than before training. Multivariate multiscale entropy analysis showed that balance after training was greater than before training. These results demonstrate that the balance of young people was improved after training with a balance trainer eight times a month.

Identification of Human Emotions via Univariate and Multivarite Multiscale Entropy

This work analyzes the emotions of human in terms of complexity. This analysis is achieved by applying both univariate and multivariate multiscale entropy methods on a multimodal dataset. Most of the contemporary human-computer interaction systems are unable to identify human affective states. So, the benefit of analyzing human emotions is to fill this gap by detecting human affective states. The univariate and multivariate multiscale entropy analysis curves obtained using multimodal dataset show differences in terms of complexity among different affective states, which can be used for emotion detection and classification for machine vision applications.

Gas–water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions.

Multivariate multiscale entropy analysis of horizontal oil–water two-phase flow

Characterizing flow behavior underlying horizontal oil–water flows from experimental measurements is a challenging problem in the fields of time series analysis and fluid mechanics. We systematically conduct a horizontal oil–water two-phase flow experiment and use our designed distributed conductance sensor to measure multivariate signals from five different flow patterns. Taking two coupled Lorenz systems as examples, we first demonstrate that the multivariate multiscale entropy (MMSE) enables to uncover the one-way/both-way coupling structure of dynamic systems. Then we use MMSE method to analyze the experimental measurements and extract the slopes and mean values from low scales of MMSE to quantitatively characterize the flow behavior. The results suggest that the MMSE enables to quantitatively distinguish different horizontal oil–water flow patterns and further allows deeply uncovering dynamic flow behavior in the transitions of different flow patterns.

EEG sensor based classification for assessing psychological stress.

Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.