In real scenarios,the spacecraft deviates from the intended paths owing to uncertainties in dynamics,navigation,and command actuation.Accurately quantifying these uncertainties is crucial for assessing the observabili...In real scenarios,the spacecraft deviates from the intended paths owing to uncertainties in dynamics,navigation,and command actuation.Accurately quantifying these uncertainties is crucial for assessing the observability,collision risks,and mission viability.This issue is further magnified for CubeSats because they have limited control authority and thus require accurate dispersion estimates to avoid rejecting viable trajectories or selecting unviable ones.Trajectory uncertainties arise from random variables(e.g.,measurement errors and drag coefficients)and processes(e.g.,solar radiation pressure and low-thrust acceleration).Although random variables generally present minimal computational complexity,handling stochastic processes can be challenging because of their noisy dynamics.Nonetheless,accurately modeling these processes is essential,as they significantly influence the uncertain propagation of space trajectories,and an inadequate representation can result in either underestimation or overestimation of the stochastic characteristics associated with a given trajectory.This study addresses the gap in characterizing process uncertainties,represented as Gauss–Markov processes in mission analysis,by presenting models,evaluating derived quantities,and providing results on the impact of spacecraft trajectories.This study emphasizes the importance of accurately modeling random processes to properly characterize stochastic spacecraft paths.展开更多
In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximati...In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximating process.展开更多
In the present paper as estimation of unknown pdf derivative of a spline function is suggested. It is studied its some statistical properties which are used to approximate maximal deviation of the spline estimation fr...In the present paper as estimation of unknown pdf derivative of a spline function is suggested. It is studied its some statistical properties which are used to approximate maximal deviation of the spline estimation from pdf with maximum of nonstationary gaussian process.展开更多
Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint mode...Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn a展开更多
基金the European Research Council(ERC)under the European Union's Horizon 2020 Research and Innovation Program(Grant No.864697).
文摘In real scenarios,the spacecraft deviates from the intended paths owing to uncertainties in dynamics,navigation,and command actuation.Accurately quantifying these uncertainties is crucial for assessing the observability,collision risks,and mission viability.This issue is further magnified for CubeSats because they have limited control authority and thus require accurate dispersion estimates to avoid rejecting viable trajectories or selecting unviable ones.Trajectory uncertainties arise from random variables(e.g.,measurement errors and drag coefficients)and processes(e.g.,solar radiation pressure and low-thrust acceleration).Although random variables generally present minimal computational complexity,handling stochastic processes can be challenging because of their noisy dynamics.Nonetheless,accurately modeling these processes is essential,as they significantly influence the uncertain propagation of space trajectories,and an inadequate representation can result in either underestimation or overestimation of the stochastic characteristics associated with a given trajectory.This study addresses the gap in characterizing process uncertainties,represented as Gauss–Markov processes in mission analysis,by presenting models,evaluating derived quantities,and providing results on the impact of spacecraft trajectories.This study emphasizes the importance of accurately modeling random processes to properly characterize stochastic spacecraft paths.
文摘In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximating process.
文摘In the present paper as estimation of unknown pdf derivative of a spline function is suggested. It is studied its some statistical properties which are used to approximate maximal deviation of the spline estimation from pdf with maximum of nonstationary gaussian process.
基金This work was supported by the National Natural Science Foundation of China(Nos.51678101,52078093)Liaoning Revitalization Talents Program(No.XLYC1905015).
文摘Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn a
