In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite e...In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model.Firstly,according to the depth map-based photogrammetric theory,the estimation of JRC from a three-dimensional(3D)digital surface model of rock discontinuities was presented.Secondly,an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes.Meanwhile,the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths,and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests.Then,by combining the theory of point cloud coordinate computation error with the equation of JRC calculation,a composite error model controlled by the shooting parameters was proposed.This newly proposed model was validated via the 3D model dataset,demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters.Furthermore,the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement.Subsequently,our proposed error model was integrated into the shooting parameter selection algorithm,thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes.Moreover,the optimal combination of three shooting parameters was offered.JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data.Finally,the application scope and limitations of the newly proposed approach were further addressed.展开更多
Objective：A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for...Objective：A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations（ODEs）that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test（GTT）in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods：Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals（SSR）function,which quantifies the difference between theoretical model predictions and GTT＇s experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results：Based on the Ackerman＇s published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion：The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52225904 and 52039007)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102212207).
文摘In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model.Firstly,according to the depth map-based photogrammetric theory,the estimation of JRC from a three-dimensional(3D)digital surface model of rock discontinuities was presented.Secondly,an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes.Meanwhile,the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths,and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests.Then,by combining the theory of point cloud coordinate computation error with the equation of JRC calculation,a composite error model controlled by the shooting parameters was proposed.This newly proposed model was validated via the 3D model dataset,demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters.Furthermore,the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement.Subsequently,our proposed error model was integrated into the shooting parameter selection algorithm,thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes.Moreover,the optimal combination of three shooting parameters was offered.JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data.Finally,the application scope and limitations of the newly proposed approach were further addressed.
基金supported by a grant from the NIH(No.U42 RR16607)
文摘Objective：A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations（ODEs）that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test（GTT）in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods：Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals（SSR）function,which quantifies the difference between theoretical model predictions and GTT＇s experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results：Based on the Ackerman＇s published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion：The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.
