Zhong-Ke Gao

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows

Measuring water holdup and characterizing the flow behavior of an oil-water two-phase flow is a contemporary and challenging problem of significant importance in industry. To address this problem, we develop a new method to design a new four-sector distributed conductance sensor. Specifically, we first use the finite-element method (FEM) to investigate the sensitivity distribution of the electric field and then calculate its response on the measurement electrodes. Based on the FEM analysis results, we extract two optimizing indexes to describe and find the optimum geometry for the four-sector distributed conductance sensor. We carry out oil-water two-phase flow experiments in a vertical upward pipe to validate the designed sensor implemented in the measurement of water holdup. In addition, we use the multivariate pseudo Wigner distribution (MPWD) method to analyze the multivariate signals from the four-sector distributed sensor. Our analytical and experimental results indicate that the four-sector distributed conductance sensor enables measuring water holdup and the MPWD allows uncovering local flow behavior revealing different oil-water flow patterns.

Multilayer Network from Multivariate Time Series for Characterizing Nonlinear Flow Behavior

The exploration of two-phase flows, as a multidisciplinary subject, has drawn a great deal of attention on account of its significance. The dynamical flow behaviors underlying the transitions of oil–water bubbly flows are still elusive. We carry out oil–water two-phase flow experiments and capture multichannel flow information. Then we propose a novel methodology for inferring multilayer network from multivariate time series, which enables to fuse multichannel flow information at different frequency bands. We employ macro-scale, meso-scale and micro-scale network measures to characterize the generated multilayer networks, and the results suggest that our analysis allows uncovering the nonlinear flow behaviors underlying the transitions of oil-in-water bubbly flows.

Wavelet multiresolution complex network for analyzing multivariate nonlinear time series

Characterizing complicated behavior from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. We in this paper propose a novel wavelet multiresolution complex network (WMCN) for analyzing multivariate nonlinear time series. In particular, we first employ wavelet multiresolution decomposition to obtain the wavelet coefficients series at different resolutions for each time series. We then infer the complex network by regarding each time series as a node and determining the connections in terms of the distance among the feature vectors extracted from wavelet coefficients series. We apply our method to analyze the multivariate nonlinear time series from our oil–water two-phase flow experiment. We construct various wavelet multiresolution complex networks and use the weighted average clustering coefficient and the weighted average shortest path length to characterize the nonlin...

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.

Multivariate weighted recurrence network inference for uncovering oil-water transitional flow behavior in a vertical pipe

Exploring the dynamical behaviors of high water cut and low velocity oil-water flows remains a contemporary and challenging problem of significant importance. This challenge stimulates us to design a high-speed cycle motivation conductance sensor to capture spatial local flow information. We systematically carry out experiments and acquire the multi-channel measurements from different oil-water flow patterns. Then we develop a novel multivariate weighted recurrence network for uncovering the flow behaviors from multi-channel measurements. In particular, we exploit graph energy and weighted clustering coefficient in combination with multivariate time-frequency analysis to characterize the derived complex networks. The results indicate that the network measures are very sensitive to the flow transitions and allow uncovering local dynamical behaviors associated with water cut and flow velocity. These properties render our method particularly useful for quantitatively characterizing dynamical behaviors governing the transition and evolution of different oil-water flow patterns.

The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy.

A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multichannel sensor information viewpoint remains a complicated challenge of ongoing interest. In this paper, first, based on a well-designed double-layer distributed-sector conductance (DLDSC) sensor, systematic oil–water and gas–liquid two-phase flow experiments are carried out to capture abundant spatiotemporal flow information. Second, well flow parameter measurement performance of the DLDSC sensor is effectively validated from the perspective of normalized conductance. Third, a novel multiplex network-based model is presented to implement data mining and characterize the evolution of flow dynamics. The results demonstrate that the model is powerful for the exploration of the spatial flow behaviors from heterogeneity to randomness in the studied two-phase flows.

Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flow

Characterizing the flow structure underlying the evolution of oil-in-water bubbly flow remains a contemporary challenge of great interests and complexity. In particular, the oil droplets dispersing in a water continuum with diverse size make the study of oil-in-water bubbly flow really difficult. To study this issue, we first design a novel complex impedance sensor and systematically conduct vertical oil-water flow experiments. Based on the multivariate complex impedance measurements, we define modalities associated with the spatial transient flow structures and construct modality transition-based network for each flow condition to study the evolution of flow structures. In order to reveal the unique flow structures underlying the oil-in-water bubbly flow, we filter the inferred modality transition-based network by removing the edges with small weight and resulting isolated nodes. Then, the weighted clustering coefficient entropy and weighted average path length are employed for quantitatively assessing the original network and filtered network. The differences in network measures enable to efficiently characterize the evolution of the oil-in-water bubbly flow structures.

Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system

Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yield novel insights into the brain fatigue behavior resulting from a long use of the ERP-based smart home system. These properties render the multivariate recurrence network, particularly useful for analyzing electroencephalographic recordings from the ERP-based smart home system.