Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Quantifying the Cloud Water Resource:Methods Based on Observational Diagnosis and Cloud Model Simulation

Based on the concepts of cloud water resource(CWR)and related variables proposed in the first part of this study,this paper provides details of two methods to quantify the CWR.One is diagnostic quantification(CWR-DQ)based on satellite observations,precipitation products,and atmospheric reanalysis data;and the other is numerical quantification(CWR-NQ)based on a cloud resolving model developed at the Chinese Academy of Meteorological Sciences(CAMS).The two methods are applied to quantify the CWR in April and August 2017 over North China,and the results are evaluated against all available observations.Main results are as follows.(1)For the CWR-DQ approach,reference cloud profiles are firstly derived based on the Cloud Sat/CALIPSO joint satellite observations for 2007–2010.The NCEP/NCAR reanalysis data in 2000–2017 are then employed to produce three-dimensional cloud fields.The budget/balance equations of atmospheric water substance are lastly used,together with precipitation observations,to retrieve CWR and related variables.It is found that the distribution and vertical structure of clouds obtained by the diagnostic method are consistent with observations.(2)For the CWR-NQ approach,it assumes that the cloud resolving model is able to describe the cloud microphysical processes completely and precisely,from which four-dimensional distributions of atmospheric water vapor,hydrometeors,and wind fields can be obtained.The data are then employed to quantify the CWR and related terms/quantities.After one-month continuous integration,the mass of atmospheric water substance becomes conserved,and the tempospatial distributions of water vapor,hydrometeors/cloud water,and precipitation are consistent with observations.(3)Diagnostic values of the difference in the transition between hydrometeors and water vapor(Cvh-Chv)and the surface evaporation(Es)are well consistent with their numerical values.(4)Correlation and bias analyses show that the diagnostic CWR contributors are well correlated with observations,and match their numerical counterparts as well,indicating that the CWR-NQ and CWR-DQ methods are reasonable.(5)Underestimation of water vapor converted from hydrometeors(Chv)is a shortcoming of the CWR-DQ method,which may be rectified by numerical quantification results or by use of advanced observations on higher spatiotemporal resolutions.

AI-Enabled High-Quality Development of Teaching Research:Theoretical Framework,Practice Blueprint,and Development Vein of Intelligent PrecisionTeaching Research

Intelligent technology will change the measures,concepts,and methods of teaching research which should be not merely precision but intelligent.Intelligent precision teaching research will become the new method and form of the development of teaching research in the era of intelligence.The paper brings forth the theoretical framework of intelligent precision teaching research,including its definition,characteristics,technological environment,and approaches,and designs the practice blueprint of“one center,two paths,three principles,four objectives,and five tasks.”It introduces the initial effects of the practice of intelligent precision teaching research and looks forward to the future prospects of this practice.