有载分接开关(on-load tap changer,OLTC)的机械故障声纹在线监测技术逐步得到应用,为消除OLTC声纹在线监测过程中外界环境干扰导致的误动,提出一种基于混合倒谱系数与卷积神经网络(convolutional neural network,CNN)的OLTC声音辨识方...有载分接开关(on-load tap changer,OLTC)的机械故障声纹在线监测技术逐步得到应用,为消除OLTC声纹在线监测过程中外界环境干扰导致的误动,提出一种基于混合倒谱系数与卷积神经网络(convolutional neural network,CNN)的OLTC声音辨识方法。首先通过现场采集与搭建试验平台的方式构建OLTC声信号数据集,并将变电站采集的环境声数据与ESC-50数据集共同构建成环境声数据集;其次,根据OLTC的声信号分布特性,采用梅尔倒谱系数、伽马通滤波倒谱系数、幂律归一化倒谱系数组成的混合倒谱对原始信号进行降维预处理,提升后续识别速度;最后,引入CNN通过超参数调整和网络结构优化设计构建声音辨识模型,实现OLTC与环境干扰的声信号辨识。结果表明,该方法在辨识OLTC与环境干扰声音方面具有较高的准确率与较快的计算速率。展开更多
Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time...Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.展开更多
In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose...In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.展开更多
目的人脸识别技术在很多领域起着重要作用,但大量的欺诈攻击对人脸识别产生了威胁,比如打印攻击和重放攻击。传统的活体检测方法是以手工方式提取特征且缺乏对时间维度的考虑,导致检测效果不佳。针对以上问题,提出一种结合混合池化的双...目的人脸识别技术在很多领域起着重要作用,但大量的欺诈攻击对人脸识别产生了威胁,比如打印攻击和重放攻击。传统的活体检测方法是以手工方式提取特征且缺乏对时间维度的考虑,导致检测效果不佳。针对以上问题,提出一种结合混合池化的双流活体检测网络。方法对数据集提取光流图像并进行面部检测,得到双流网络的两个输入;在双流网络末端加入空间金字塔和全局平均混合池化,利用全连接层对池化后的特征进行分类并进行分数层面的融合;对空间流网络和时间流网络进行融合得到一个最优结果,同时考虑了不同颜色空间对检测性能的影响。结果在CASIA-FASD(CASIA face anti-spoofing database)和replay-attack两个数据集上做了多组对比实验,在CASIA-FASD数据集上,等错误率(equal error rate,EER)为1.701%;在replay-attack数据集上,等错误率和半错误率(half total error rate,HTER)分别为0.091%和0.082%。结论结合混合池化的双流活体检测网络充分考虑时间维度,提出的空间金字塔和全局平均混合池化策略能有效地利用特征。针对包含多种攻击类型、图像质量差异较大的数据集,本文提出的网络模型均能取得较低的错误率。展开更多
文摘有载分接开关(on-load tap changer,OLTC)的机械故障声纹在线监测技术逐步得到应用,为消除OLTC声纹在线监测过程中外界环境干扰导致的误动,提出一种基于混合倒谱系数与卷积神经网络(convolutional neural network,CNN)的OLTC声音辨识方法。首先通过现场采集与搭建试验平台的方式构建OLTC声信号数据集,并将变电站采集的环境声数据与ESC-50数据集共同构建成环境声数据集;其次,根据OLTC的声信号分布特性,采用梅尔倒谱系数、伽马通滤波倒谱系数、幂律归一化倒谱系数组成的混合倒谱对原始信号进行降维预处理,提升后续识别速度;最后,引入CNN通过超参数调整和网络结构优化设计构建声音辨识模型,实现OLTC与环境干扰的声信号辨识。结果表明,该方法在辨识OLTC与环境干扰声音方面具有较高的准确率与较快的计算速率。
基金supported by the National Natural Science Foundation of China(61374003 41631072)the Academic Foundation of Naval University of Engineering(20161475)
文摘Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.
基金sponsored by the National Key Research and Development Project(2018YFC1503202-01)the Emergency Management Project of the National Natural Science Foundation of China(41842042)
文摘In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.
文摘目的人脸识别技术在很多领域起着重要作用,但大量的欺诈攻击对人脸识别产生了威胁,比如打印攻击和重放攻击。传统的活体检测方法是以手工方式提取特征且缺乏对时间维度的考虑,导致检测效果不佳。针对以上问题,提出一种结合混合池化的双流活体检测网络。方法对数据集提取光流图像并进行面部检测,得到双流网络的两个输入;在双流网络末端加入空间金字塔和全局平均混合池化,利用全连接层对池化后的特征进行分类并进行分数层面的融合;对空间流网络和时间流网络进行融合得到一个最优结果,同时考虑了不同颜色空间对检测性能的影响。结果在CASIA-FASD(CASIA face anti-spoofing database)和replay-attack两个数据集上做了多组对比实验,在CASIA-FASD数据集上,等错误率(equal error rate,EER)为1.701%;在replay-attack数据集上,等错误率和半错误率(half total error rate,HTER)分别为0.091%和0.082%。结论结合混合池化的双流活体检测网络充分考虑时间维度,提出的空间金字塔和全局平均混合池化策略能有效地利用特征。针对包含多种攻击类型、图像质量差异较大的数据集,本文提出的网络模型均能取得较低的错误率。
