精确量化检出大学生的焦虑情绪并对病理因素进行追溯分析,是临床心理治疗和心理危机干预的重要环节,而基于脑电(Electroencephalograph,EEG)信号的深度学习是当前最具发展潜力的一种诊断方法。本研究对传统卷积神经网络(Convolutional N...精确量化检出大学生的焦虑情绪并对病理因素进行追溯分析,是临床心理治疗和心理危机干预的重要环节,而基于脑电(Electroencephalograph,EEG)信号的深度学习是当前最具发展潜力的一种诊断方法。本研究对传统卷积神经网络(Convolutional Neural Networks,CNN)进行改进,提出并构造一个基于“扩展信息输入空间”的神经网络(Neural Network Based on Extended Information Input Space,NN-EIIS)模型,取代CNN末端的分类器;并引入具有独立性的被试对象焦虑量表得分(Score of Anxiety Scale,SAS),作为焦虑情绪量化标准和训练样本集的输出。以某高校大学生为研究对象进行实验,结果表明所提出的方案不仅实现了对焦虑情感的精确量化识别,还能利用所得模型,在一定程度上对大学生焦虑障碍患者的某些重要的内在病理因素进行追溯分析。展开更多
The S transform, which is a time-frequency representation known for its local spectral phase properties in signal processing, uniquely combines elements of wavelet transforms and the short-time Fourier transform （STF...The S transform, which is a time-frequency representation known for its local spectral phase properties in signal processing, uniquely combines elements of wavelet transforms and the short-time Fourier transform （STFT）. The fractional Fourier transform is a tool for non-stationary signal analysis. In this paper, we define the concept of the fractional S transform （FRST） of a signal, based on the idea of the fractional Fourier transform （FRFT） and S transform （ST）, extend the S transform to the time-fractional frequency domain from the time- frequency domain to obtain the inverse transform, and study the FRST mathematical properties. The FRST, which has the advantages of FRFT and ST, can enhance the ST flexibility to process signals. Compared to the S transform, the FRST can effectively improve the signal time- frequency resolution capacity. Simulation results show that the proposed method is effective.展开更多
A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal o...A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.展开更多
文摘精确量化检出大学生的焦虑情绪并对病理因素进行追溯分析,是临床心理治疗和心理危机干预的重要环节,而基于脑电(Electroencephalograph,EEG)信号的深度学习是当前最具发展潜力的一种诊断方法。本研究对传统卷积神经网络(Convolutional Neural Networks,CNN)进行改进,提出并构造一个基于“扩展信息输入空间”的神经网络(Neural Network Based on Extended Information Input Space,NN-EIIS)模型,取代CNN末端的分类器;并引入具有独立性的被试对象焦虑量表得分(Score of Anxiety Scale,SAS),作为焦虑情绪量化标准和训练样本集的输出。以某高校大学生为研究对象进行实验,结果表明所提出的方案不仅实现了对焦虑情感的精确量化识别,还能利用所得模型,在一定程度上对大学生焦虑障碍患者的某些重要的内在病理因素进行追溯分析。
基金supported by Scientific Research Fund of Sichuan Provincial Education Departmentthe National Nature Science Foundation of China (No. 40873035)
文摘The S transform, which is a time-frequency representation known for its local spectral phase properties in signal processing, uniquely combines elements of wavelet transforms and the short-time Fourier transform （STFT）. The fractional Fourier transform is a tool for non-stationary signal analysis. In this paper, we define the concept of the fractional S transform （FRST） of a signal, based on the idea of the fractional Fourier transform （FRFT） and S transform （ST）, extend the S transform to the time-fractional frequency domain from the time- frequency domain to obtain the inverse transform, and study the FRST mathematical properties. The FRST, which has the advantages of FRFT and ST, can enhance the ST flexibility to process signals. Compared to the S transform, the FRST can effectively improve the signal time- frequency resolution capacity. Simulation results show that the proposed method is effective.
基金Supported by the Natural Science Foundation of Zhejiang Province（Y1100842） the Planning Projects of General Administration of Quality Supervision Inspection and Quarantine of the People＇s Republic of China（2006QK23）
文摘A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.
