When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To ...When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.展开更多
Power systems are the largest and most complex human made systems, consisting of thousands of electrical sources, loads, transmission and distribution lines, power transformers, circuit breakers, etc. where faults alw...Power systems are the largest and most complex human made systems, consisting of thousands of electrical sources, loads, transmission and distribution lines, power transformers, circuit breakers, etc. where faults always occurred. Faults can cause personnel and equipment safety problems, and can result in significant disruption to power supply and thus financial losses. In this paper we will present comprehensive mathematical suite to detect and classify fault dependent models of various types of power systems. This work will extract fault unique signatures by using polarization ellipse during the healthy condition and the polarization will be circular shape with radius equal the rated voltage of the system, but during the fault condition the polarization will be ellipse shape and the fault signature will be defined according the ellipse parameters major axis, minor axis, ellipticity and orientation angle, by using least squares criterion will define the ellipse parameters this system will identify and classify. This paper will be a milestone for extended paper based on the proposed mathematical modelling and applying it to identify, classify and localize with simulation model.展开更多
Pulse laser range detector is to measure the distance by estimating the time delay between the emitting pulse and echo pulse.In this paper,a mathematical model for the target echo signal of laser fuze has been establi...Pulse laser range detector is to measure the distance by estimating the time delay between the emitting pulse and echo pulse.In this paper,a mathematical model for the target echo signal of laser fuze has been established;in accordance with this model,the formulas for echo time-delay estimation and for amplitude estimation based on least squares criterion have been deduced.It is argued and simulated that the resolution of echo time-delay estimation could be improved through multi-reference correlation approach.Experiments illustrate that the approach enables pulsed laser fuze to perform high-precision ranging under a low signal-to-noise ratio condition.展开更多
基金supported by the National Natural Science Foundation of China,Nos.41874001 and 41664001Support Program for Outstanding Youth Talents in Jiangxi Province,No.20162BCB23050National Key Research and Development Program,No.2016YFB0501405。
文摘When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.
文摘Power systems are the largest and most complex human made systems, consisting of thousands of electrical sources, loads, transmission and distribution lines, power transformers, circuit breakers, etc. where faults always occurred. Faults can cause personnel and equipment safety problems, and can result in significant disruption to power supply and thus financial losses. In this paper we will present comprehensive mathematical suite to detect and classify fault dependent models of various types of power systems. This work will extract fault unique signatures by using polarization ellipse during the healthy condition and the polarization will be circular shape with radius equal the rated voltage of the system, but during the fault condition the polarization will be ellipse shape and the fault signature will be defined according the ellipse parameters major axis, minor axis, ellipticity and orientation angle, by using least squares criterion will define the ellipse parameters this system will identify and classify. This paper will be a milestone for extended paper based on the proposed mathematical modelling and applying it to identify, classify and localize with simulation model.
基金Sponsored by the National Defense Science and Technology Laboratory Foundation (9140C3601130802)
文摘Pulse laser range detector is to measure the distance by estimating the time delay between the emitting pulse and echo pulse.In this paper,a mathematical model for the target echo signal of laser fuze has been established;in accordance with this model,the formulas for echo time-delay estimation and for amplitude estimation based on least squares criterion have been deduced.It is argued and simulated that the resolution of echo time-delay estimation could be improved through multi-reference correlation approach.Experiments illustrate that the approach enables pulsed laser fuze to perform high-precision ranging under a low signal-to-noise ratio condition.
