Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper propose...Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper proposes a combinatorial method of ensemble empirical mode decomposition(EEMD),cepstrum,fast Fourier transform(FFT)and zero-crossing detection to improve the robustness of the estimation of pulse rate(PR),heart rate(HR)and respiratory rate(RR)from the PPG.First,the PPG signal was decomposed into finite intrinsic mode functions(IMF)by EEMD.Because of its adaptive filtering property,the different signals were reconstructed using different IMFs when estimating different physiological parameters.Second,the PR was obtained by zero-crossing detection after rejecting low frequency IMFs containing artifacts.Third,IMFs with frequency between 1.00 Hz to 1.67 Hz(60 beats/min to 100 beats/min)were selected for estimating HR.Then,the frequency band that reflects the heart activity was analyzed by the cepstrum method.Finally,the respiratory signal can be extracted from PPG signal by IMFs with frequency between 0.05 Hz to 0.75 Hz(3 breahts/min to 45 breaths/min).Then the spectrum of signal was obtained by FFT analysis and the RR was estimated by detecting the maximum frequency peak.The algorithm has been tested on MIMIC database obtained from 53 adults.The experiment results show that the physiological parameters extracted by this integrated signal processing method are consistent with the real physiological parameters.And the computation load of this method is small and the precision is high(not larger than 1.17%in error).展开更多
针对当前不同的非白噪声背景研究很少,根据噪音、语音和音乐的性质并且结合统计学理论,提出一种在不同噪声背景下低信噪比的语音/音乐分割算法。以往的检测算法很少考虑低信噪比的环境,首先从音频数据中提取新的特征参数概率密度比(prob...针对当前不同的非白噪声背景研究很少,根据噪音、语音和音乐的性质并且结合统计学理论,提出一种在不同噪声背景下低信噪比的语音/音乐分割算法。以往的检测算法很少考虑低信噪比的环境,首先从音频数据中提取新的特征参数概率密度比(probability density ratio,PR)和概率密度比过零率(probability density ratio crossing rate,PRCR),特征参数在低信噪比环境下亦能明显表征语音和音乐的不同特性,然后根据音频的特性对PRCR进行修正,再基于此修正的特征参数对语音和音乐进行改变点检测,最后得到分割结果。实验结果显示,在信噪比达到5dB时分割点准确率达到85%以上,具有良好的鲁棒性。展开更多
Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk...Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.展开更多
文摘Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper proposes a combinatorial method of ensemble empirical mode decomposition(EEMD),cepstrum,fast Fourier transform(FFT)and zero-crossing detection to improve the robustness of the estimation of pulse rate(PR),heart rate(HR)and respiratory rate(RR)from the PPG.First,the PPG signal was decomposed into finite intrinsic mode functions(IMF)by EEMD.Because of its adaptive filtering property,the different signals were reconstructed using different IMFs when estimating different physiological parameters.Second,the PR was obtained by zero-crossing detection after rejecting low frequency IMFs containing artifacts.Third,IMFs with frequency between 1.00 Hz to 1.67 Hz(60 beats/min to 100 beats/min)were selected for estimating HR.Then,the frequency band that reflects the heart activity was analyzed by the cepstrum method.Finally,the respiratory signal can be extracted from PPG signal by IMFs with frequency between 0.05 Hz to 0.75 Hz(3 breahts/min to 45 breaths/min).Then the spectrum of signal was obtained by FFT analysis and the RR was estimated by detecting the maximum frequency peak.The algorithm has been tested on MIMIC database obtained from 53 adults.The experiment results show that the physiological parameters extracted by this integrated signal processing method are consistent with the real physiological parameters.And the computation load of this method is small and the precision is high(not larger than 1.17%in error).
文摘针对当前不同的非白噪声背景研究很少,根据噪音、语音和音乐的性质并且结合统计学理论,提出一种在不同噪声背景下低信噪比的语音/音乐分割算法。以往的检测算法很少考虑低信噪比的环境,首先从音频数据中提取新的特征参数概率密度比(probability density ratio,PR)和概率密度比过零率(probability density ratio crossing rate,PRCR),特征参数在低信噪比环境下亦能明显表征语音和音乐的不同特性,然后根据音频的特性对PRCR进行修正,再基于此修正的特征参数对语音和音乐进行改变点检测,最后得到分割结果。实验结果显示,在信噪比达到5dB时分割点准确率达到85%以上,具有良好的鲁棒性。
文摘Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.
