Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary mea...Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.展开更多
The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and ...The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and transmission abilities of well logging instruments are much limited due to the underground environment,it is necessary to explore suitable sampling methods which can be used to obtain an accurate RST with less sampling data.This paper presents a uniform amplitude sampling method（UASM）,and compares it with the conventional uniform time sampling method（UTSM） and logarithm time sampling method（LTSM） in terms of the adaptability to different strata,RTS inversion accuracy,and stratum vertical resolution.Numerical simulation results show that the UASM can obtain high inversion accuracy of RTS with different kinds of pore size distribution formation,with high dynamic ranges of pore size,and with a small number of sampling points.The UASM,being able to adapt to the attenuation speed of polarization curve automatically,thus has the highest vertical resolution.The inversion results of rock samples also show that the UASM is superior to the UTSM and LTSM.展开更多
基金Supported by Shandong Province Key R and D Program,No.2021SFGC0504Shandong Provincial Natural Science Foundation,No.ZR2021MF079Science and Technology Development Plan of Jinan(Clinical Medicine Science and Technology Innovation Plan),No.202225054.
文摘Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.
基金partially supported by a project from the National Natural Science Foundation of China (No.61401168)
文摘The induced polarization relaxation time spectrum（RTS） reflects the distribution of rock pore size,which is a key factor in estimating the oil or water storage capacity of strata.However,as the data acquisition and transmission abilities of well logging instruments are much limited due to the underground environment,it is necessary to explore suitable sampling methods which can be used to obtain an accurate RST with less sampling data.This paper presents a uniform amplitude sampling method（UASM）,and compares it with the conventional uniform time sampling method（UTSM） and logarithm time sampling method（LTSM） in terms of the adaptability to different strata,RTS inversion accuracy,and stratum vertical resolution.Numerical simulation results show that the UASM can obtain high inversion accuracy of RTS with different kinds of pore size distribution formation,with high dynamic ranges of pore size,and with a small number of sampling points.The UASM,being able to adapt to the attenuation speed of polarization curve automatically,thus has the highest vertical resolution.The inversion results of rock samples also show that the UASM is superior to the UTSM and LTSM.
