Based on the fact that it is diffi cult to implement optimum inversion using 2D and 3D forward modeling with magnetic-source transient electromagnetics(TEM),this paper explores a novel approach to the implementation o...Based on the fact that it is diffi cult to implement optimum inversion using 2D and 3D forward modeling with magnetic-source transient electromagnetics(TEM),this paper explores a novel approach to the implementation of 2D magnetic-source TEM inversion.In particular,we converted magnetic-source TEM data into magnetotelluric(MT)data and then used a 2D MT inversion method to implement a 2D magnetic-source TEM inversion interpretation.First,we studied the similarity between magnetic-source TEM waves and MT waves and between magnetic-source TEM all-time apparent resistivity and MT Cagniard apparent resistivity.Then,we selected an optimal time-frequency transformation coeffi cient to implement rapid time-frequency transformation of all-time TEM apparent resistivity to MT Cagniard apparent resistivity.Afterward,we conducted 1D pseudo-MT inversions of magnetic-source 1D TEM theoretical models.The 1D inversion results demonstrated that the diff erence between the inversion parameters and model parameters was small,while the MT 1D inversion method could be used to conduct magnetic 1D TEM inversion within a certain margin of error.We further conducted 2D pseudo-MT inversions of 3D magnetic-source TEM theoretical models,and the 2D inversion results indicated that selecting a joint 2D pseudo-MT transverse-electric(TE)and transverse-magnetic(TM)inversion method based on measuring the line above a 3D anomalous body can help to accurately implement a 2D inversion interpretation of the 3D TEM response.展开更多
Objective:PCG represents the acoustic replay of heart sounds from the cardiac structure.To detect and analyse the different conditions of the heart,heart sound signals are essential.CVD is detected by classifiers who ...Objective:PCG represents the acoustic replay of heart sounds from the cardiac structure.To detect and analyse the different conditions of the heart,heart sound signals are essential.CVD is detected by classifiers who superficially identify the cardiac features.Abnormal sounds in systole or diastole could indicate valve stenosis or regurgitation.The presence of S3 or S4 sounds could indicate heart failure or stiffening of the heart muscle.This paper proposes a CVD detection technique using improved WST and DA classifiers.Method:The PCG was obtained from the Physionet dataset.The raw signals were pre-processed using 2D DCT.The 2D DCT was applied to a matrix containing 3000 sounds with 10000 samples.The DCT matrix was then filtered using a 20Hz–190 Hz Type II Chebyshev filter to remove the high frequency noise above 190 Hz.After filtering,the denoised PCG matrix was obtained from the DCT matrix using inverse 2D DCT.The PCG matrix was feature extracted using WST.WST produces low-frequency components by using the LPFs to filter high-frequency components.These features were then used with the DA classifier to predict the CVD.The DA classifier uses discriminant analysis pattern classification.The DA classifier learns the training PCG pattern,from WST features,and then classifies test samples as normal or abnormal.Results:The proposed method removed noise up to 99%.The 2D DCT filter provided an average noise improvement of 37.34 dB.Further tuning in filter order or attenuation level resulted in distortion of the PCG,and noise improvement declined.The DCT filter removed up to 99%of noise as per the SNR estimation.The improved WST and the DA classifier resulted in an accuracy of 99.63%.Conclusion:Comparative analysis with DNN,advocates the superiority of the proposed method.DNN classifiers provide accurate CVD classification but require a more expensive and complex GPU.The DA classifier requires only a CPU.This work demonstrated that superior CVD classification was obtained using a combination of WST features and the DA classifier with 94%accuracy.展开更多
基金this research project is funded by a major science and technology project of Gansu province,“research on the complete set technology for highway construction in collapsible loess region of Gansu province”(No.1302GKDA009).
文摘Based on the fact that it is diffi cult to implement optimum inversion using 2D and 3D forward modeling with magnetic-source transient electromagnetics(TEM),this paper explores a novel approach to the implementation of 2D magnetic-source TEM inversion.In particular,we converted magnetic-source TEM data into magnetotelluric(MT)data and then used a 2D MT inversion method to implement a 2D magnetic-source TEM inversion interpretation.First,we studied the similarity between magnetic-source TEM waves and MT waves and between magnetic-source TEM all-time apparent resistivity and MT Cagniard apparent resistivity.Then,we selected an optimal time-frequency transformation coeffi cient to implement rapid time-frequency transformation of all-time TEM apparent resistivity to MT Cagniard apparent resistivity.Afterward,we conducted 1D pseudo-MT inversions of magnetic-source 1D TEM theoretical models.The 1D inversion results demonstrated that the diff erence between the inversion parameters and model parameters was small,while the MT 1D inversion method could be used to conduct magnetic 1D TEM inversion within a certain margin of error.We further conducted 2D pseudo-MT inversions of 3D magnetic-source TEM theoretical models,and the 2D inversion results indicated that selecting a joint 2D pseudo-MT transverse-electric(TE)and transverse-magnetic(TM)inversion method based on measuring the line above a 3D anomalous body can help to accurately implement a 2D inversion interpretation of the 3D TEM response.
文摘Objective:PCG represents the acoustic replay of heart sounds from the cardiac structure.To detect and analyse the different conditions of the heart,heart sound signals are essential.CVD is detected by classifiers who superficially identify the cardiac features.Abnormal sounds in systole or diastole could indicate valve stenosis or regurgitation.The presence of S3 or S4 sounds could indicate heart failure or stiffening of the heart muscle.This paper proposes a CVD detection technique using improved WST and DA classifiers.Method:The PCG was obtained from the Physionet dataset.The raw signals were pre-processed using 2D DCT.The 2D DCT was applied to a matrix containing 3000 sounds with 10000 samples.The DCT matrix was then filtered using a 20Hz–190 Hz Type II Chebyshev filter to remove the high frequency noise above 190 Hz.After filtering,the denoised PCG matrix was obtained from the DCT matrix using inverse 2D DCT.The PCG matrix was feature extracted using WST.WST produces low-frequency components by using the LPFs to filter high-frequency components.These features were then used with the DA classifier to predict the CVD.The DA classifier uses discriminant analysis pattern classification.The DA classifier learns the training PCG pattern,from WST features,and then classifies test samples as normal or abnormal.Results:The proposed method removed noise up to 99%.The 2D DCT filter provided an average noise improvement of 37.34 dB.Further tuning in filter order or attenuation level resulted in distortion of the PCG,and noise improvement declined.The DCT filter removed up to 99%of noise as per the SNR estimation.The improved WST and the DA classifier resulted in an accuracy of 99.63%.Conclusion:Comparative analysis with DNN,advocates the superiority of the proposed method.DNN classifiers provide accurate CVD classification but require a more expensive and complex GPU.The DA classifier requires only a CPU.This work demonstrated that superior CVD classification was obtained using a combination of WST features and the DA classifier with 94%accuracy.
