Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this ana...Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.展开更多
To overcome the deficiencies of the existing Verhulst GM(1,1) model, based on the existing grey theory, a non-equal-interval direct optimum Verhulst GM(1,1) model is built which chooses a modified n-th component x(n) ...To overcome the deficiencies of the existing Verhulst GM(1,1) model, based on the existing grey theory, a non-equal-interval direct optimum Verhulst GM(1,1) model is built which chooses a modified n-th component x(n) of X(0) as the starting condition of the grey differential model. It optimizes a modified β value and the background value, and takes two times fitting optimization. The new model extends equal intervals to non-equal-intervals and is suitable for general data modelling and estimating parameters of the direct Verhulst GM(1,1). The new model does not need to pre-process the primitive data, nor accumulate generating operation (AGO) and inverse accumulated generating operation (IAGO). It is not only suitable for equal interval data modelling, but also for non-equal interval data modelling. As the new information is fully used and two times fitting optimization is taken, the fitting accuracy is the highest in all existing models. The example shows that the new model is simple and practical. The new model is worth expanding on and applying in data processing or on-line monitoring for tests, social sciences and other engineering sciences.展开更多
The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a ta...The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.展开更多
This paper starts with untime-diversification of the time-diversification deformation model and gives displacement distribution model of untime-diversification and simplifies further the study of deformation model.The...This paper starts with untime-diversification of the time-diversification deformation model and gives displacement distribution model of untime-diversification and simplifies further the study of deformation model.The paper discusses the problem of least squares fitting of coordinate parameters model—parameters of deformation model.During discussion,the basic means of cubic B splines and two steps of multidimensional disorder datum fitting are adopted which can make fitting function calculated mostly approximate coordinate parameters model and it can make calculation easier.展开更多
Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte ...Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo(MCMC)methods are commonly used for such data.When the spatio-temporal support of the model is large,implementing an MCMC algorithm becomes a significant computational burden.This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatiotemporal mixed effects binomial regression model.We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach.Both methods are used to produce monthly forecasts for Lyme disease,anaplasmosis,ehrlichiosis,and heartworm disease in domestic dogs for the contiguous United States.The data have a spatial support of 3108 counties and a temporal support of 108e138 months with 71e135 million test results.The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm,with a similar mean absolute prediction error.展开更多
基金Supported by the National Natural Science Foundation of China(No.51379006 and No.51009106)the Program for New Century Excellent Talents in University of Ministry of Education of China(No.NCET-12-0404)the National Basic Research Program of China("973"Program,No.2013CB035903)
文摘Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.
基金The 11th Five-Year Plan for Key Constructing Academic Subject of Hunan Province(No.XJT2006180)Natural Science Foundation of Hunan Province (No.07JJ3093)Hunan Province Foundation Research Program (No.2007FJ3030,2007GK3058)
文摘To overcome the deficiencies of the existing Verhulst GM(1,1) model, based on the existing grey theory, a non-equal-interval direct optimum Verhulst GM(1,1) model is built which chooses a modified n-th component x(n) of X(0) as the starting condition of the grey differential model. It optimizes a modified β value and the background value, and takes two times fitting optimization. The new model extends equal intervals to non-equal-intervals and is suitable for general data modelling and estimating parameters of the direct Verhulst GM(1,1). The new model does not need to pre-process the primitive data, nor accumulate generating operation (AGO) and inverse accumulated generating operation (IAGO). It is not only suitable for equal interval data modelling, but also for non-equal interval data modelling. As the new information is fully used and two times fitting optimization is taken, the fitting accuracy is the highest in all existing models. The example shows that the new model is simple and practical. The new model is worth expanding on and applying in data processing or on-line monitoring for tests, social sciences and other engineering sciences.
基金supported by the National Natural Science Foundation of China(61671181).
文摘The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.
文摘This paper starts with untime-diversification of the time-diversification deformation model and gives displacement distribution model of untime-diversification and simplifies further the study of deformation model.The paper discusses the problem of least squares fitting of coordinate parameters model—parameters of deformation model.During discussion,the basic means of cubic B splines and two steps of multidimensional disorder datum fitting are adopted which can make fitting function calculated mostly approximate coordinate parameters model and it can make calculation easier.
基金RH and SS were supported in part or in full by the Companion Animal Parasite Council.SSAM were supported in part by the Research Center for Child Well-Being[NIGMS P20GM130420].
文摘Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo(MCMC)methods are commonly used for such data.When the spatio-temporal support of the model is large,implementing an MCMC algorithm becomes a significant computational burden.This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatiotemporal mixed effects binomial regression model.We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach.Both methods are used to produce monthly forecasts for Lyme disease,anaplasmosis,ehrlichiosis,and heartworm disease in domestic dogs for the contiguous United States.The data have a spatial support of 3108 counties and a temporal support of 108e138 months with 71e135 million test results.The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm,with a similar mean absolute prediction error.
