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In this article, we propose a method for emotion classification based on multilayer convolutional neural network (MCNN) and combining differential entropy (DE) and brain network. First, we use continuous wavelet transform (CWT) to get the time–frequency representation (TFR) of electroencephalogram (EEG) signals on each channel and extract rich information from different frequency bands for subsequent analysis. Brain networks are then constructed in multiple bands to characterize the spatial connections hidden in the multichannel EEG signals. Based on brain networks, we further develop core brain networks through a set of key nodes determined by DE. These core brain networks are associated with brain activities and differ markedly between different emotional states. The final designed MCNN model takes DE features and core brain networks as inputs for emotion recognition. We evaluate our method on the SJTU emotion EEG dataset, and the average accuracy of 15 subjects achieves 91.45%. Utilizing the complementary features of DE and brain network, the proposed method provides an efficient framework for accurate emotion recognition from EEG signals. [ABSTRACT FROM AUTHOR]

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures. [ABSTRACT FROM AUTHOR]

Detecting fatigue driving from electroencephalogram (EEG) signals constitutes a challenging problem of continuing interest since fatigue driving has caused the majority of traffic accidents. We carry out a simulated driving experiment for EEG data acquisition. Then, we calculate the wavelet entropy under the alert and fatigue state, respectively, and find that the wavelet entropy gets an acceptable performance on classification. Despite that the traditional entropy-based methods have been successfully applied to detect EEG-based fatigue driving, how to improve the classification remains to be investigated. To solve this problem, we in this paper propose a novel relative wavelet entropy complex network (RWECN) for improving the classification accuracy. In particular, we infer the complex network by regarding each EEG channel as a node and determining the connections of nodes in terms of the relative wavelet entropy between the EEG signals. Then, we extract a series of network statistical measures to characterize the topological structure of the brain networks. We combine the wavelet entropy and RWECN statistical measures to form a feature vector for realizing the classification of different states through the Fisher linear discriminant analysis. The results suggest that our method allows obtaining intrinsic and effective features from fatigue EEG signals and enables to improve the classification accuracy of EEG-based fatigue driving. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]