In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting ...In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.展开更多
A series of blends of Acrylonitrile-Butadiene-Styrene (ABS) and Polycarbonate (PC) were prepared and some of their thermal and mechanical properties were determined. The Young’s modulus changed gradually and monotoni...A series of blends of Acrylonitrile-Butadiene-Styrene (ABS) and Polycarbonate (PC) were prepared and some of their thermal and mechanical properties were determined. The Young’s modulus changed gradually and monotonically with the polycarbonate content. This effect was tentatively explained as the antiplasticization of the PC which is ascribed to the chain mobility, which permits the PC chains to pack more tightly, to the secondary cross-linking between the PC chains, or to the secondary attachment of bulky side-chains to the PC, thus producing steric hindrance to the rotation of the PC main chains. The experimental values found for the impact strength were intermediate between those of the neat polymers, depending upon the dispersed rubber particles of butadiene in the matrix of SAN (Styrene-Acrylonitrile), and the dispersed PC particles which generally make the ABS more brittle. A maximum value of about 88 KJ/m2 for the impact strength was observed for the blend with 90% PC. This may be attributed to the strong polymer-polymer interactions for this particular composition. The variations in the heat deflection temperature HDT and the Vicat softening point with the blend composition were very similar, and allowed us to assume that the phase inversion between the matrices of the two polymers takes place at 50% PC. The morphology of the blends revealed by SEM observation, show a co-continuous structure.展开更多
Under single-satellite observation,the parameter estimation of the boost phase of high-precision space noncooperative targets requires prior information.To improve the accuracy without prior information,we propose a p...Under single-satellite observation,the parameter estimation of the boost phase of high-precision space noncooperative targets requires prior information.To improve the accuracy without prior information,we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting.The use of the plane passing through the geo-center and the cutting sequence line of sight(LOS)generates the trajectory-cutting plane.With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated,a motion parameter estimation model in space non-cooperative targets is established,and the Gauss-Newton iteration method is used to solve the flight parameters.The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy,providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.展开更多
基金Project Funded by Chongqing Changjiang Electrical Appliances Industries Group Co.,Ltd
文摘In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.
文摘A series of blends of Acrylonitrile-Butadiene-Styrene (ABS) and Polycarbonate (PC) were prepared and some of their thermal and mechanical properties were determined. The Young’s modulus changed gradually and monotonically with the polycarbonate content. This effect was tentatively explained as the antiplasticization of the PC which is ascribed to the chain mobility, which permits the PC chains to pack more tightly, to the secondary cross-linking between the PC chains, or to the secondary attachment of bulky side-chains to the PC, thus producing steric hindrance to the rotation of the PC main chains. The experimental values found for the impact strength were intermediate between those of the neat polymers, depending upon the dispersed rubber particles of butadiene in the matrix of SAN (Styrene-Acrylonitrile), and the dispersed PC particles which generally make the ABS more brittle. A maximum value of about 88 KJ/m2 for the impact strength was observed for the blend with 90% PC. This may be attributed to the strong polymer-polymer interactions for this particular composition. The variations in the heat deflection temperature HDT and the Vicat softening point with the blend composition were very similar, and allowed us to assume that the phase inversion between the matrices of the two polymers takes place at 50% PC. The morphology of the blends revealed by SEM observation, show a co-continuous structure.
基金supported in part by the National Natural Science Foundation of China(Nos.42271448,41701531)the Key Laboratory of Land Satellite Remote Sensing Application,Ministry of Natural Resources of the People’s Republic of China(No.KLSMNRG202317)。
文摘Under single-satellite observation,the parameter estimation of the boost phase of high-precision space noncooperative targets requires prior information.To improve the accuracy without prior information,we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting.The use of the plane passing through the geo-center and the cutting sequence line of sight(LOS)generates the trajectory-cutting plane.With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated,a motion parameter estimation model in space non-cooperative targets is established,and the Gauss-Newton iteration method is used to solve the flight parameters.The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy,providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.
