Interpretable emotion recognition using EEG signals

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy

Negative thermal expansion in YbMn2Ge2 induced by the dual effect of magnetism and valence transition

AbstractNegative thermal expansion (NTE) is an intriguing property, which is generally triggered by a single NTE mechanism. In this work, an enhanced NTE (αv = −32.9 × 10−6 K−1, ΔT = 175 K) is achieved in YbMn2Ge2 intermetallic compound to be caused by a dual effect of magnetism and valence transition. In YbMn2Ge2, the Mn sublattice that forms the antiferromagnetic structure induces the magnetovolume effect, which contributes to the NTE below the Néel temperature (525 K). Concomitantly, the valence state of Yb increases from 2.40 to 2.82 in the temperature range of 300–700 K, which simultaneously causes the contraction of the unit cell volume due to smaller volume of Yb3+ than that of Yb2+. As a result, such combined effect gives rise to an enhanced NTE. The present study not only sheds light on the peculiar NTE mechanism of YbMn2Ge2, but also indicates the dual effect as a possible promising method to produce enhanced NTE materials

A Novel Testis-specific GTPase Serves as a Link to Proteasome Biogenesis: Functional Characterization of RhoS/RSA-14-44 in Spermatogenesis

We functionally characterized RhoS/RSA-14-44 as a new member of Rho GTPase subfamily in spermatogenesis, which provides a direct link between Rho family GTPase and the proteasome biogenesis

An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis

When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM)

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise

Numerical study of turbine rim seals performance with different sealing structures

The unsteady large-scale vortex near the turbine rim has an important influence on the sealing performance. Characteristics and performance of four sealing structures are researched in this paper. Three-dimensional unsteady numerical simulation was adopted to deeply reveal the characteristics of the rim sealing vortex and its influence mechanism on the rim sealing performance. The results show that the rim seal vortex structure induced by the interaction between ingested gas and sealing flow in the gap is the leading cause of unsteady flow in the rim. The vortex size is suppressed with the increasing seal flow rate or a Chute seal structure. However, the rim seal vortex exit in the cavity gap under a low seal flow rate can suppress the gas intrusion and improve the sealing efficiency of the turbine cavity even with a simple sealing structure. The Chute sealing structure achieves better performance among the four sealing structures studied in this paper. It can achieve complete sealing under a low sealing flow rate of 0.5% and has less impact on the aerodynamic performance of the mainstream even with high sealing flow rate. The research of this paper has guiding significance for further understanding the sealing mechanism and optimizing the design of the sealing structures.&nbsp

Theoretical Investigations on the Geometrical Structures, Energies, and Electronic Properties of the Heterofullerenes Made of the Smallest Fullerene

<div><p>In this paper, the heterofullerenes made of the smallest fullerene, C₂₀ were investigated by quantum chemistry calculations based on density functional theory. The geometrical structures, energies, electronic properties, and the aromaticities of the C₁₉X (X = B, N, O, Al, Si, P, S, Ga, Ge, As, and Se) cages were studied systemically and compared with those of the pristine C₂₀ cage. It is found that the doped cages with different heteroatoms exhibit various structural, electronic, and aromatic properties. Several doping behaviors of the C₁₉X cages are different from those of the C₅₉X cages. These results imply the possibility to modulate the physical properties of heterofullerenes by tuning the sizes of the carbon cages as well as the substitution elements.</p></div

Ensemble Neural Network Method for Wind Speed Forecasting

Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed ensemble approaches are simulated on the real wind speed data in the Longdong area of Loess Plateau in China from 2007 to 2015, the experimental results indicate that the ensemble approaches outperform individual models and other hybrid models with different pre-processing methods