To address the eccentric error of circular marks in camera calibration,a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper.Firstly,the centers of t...To address the eccentric error of circular marks in camera calibration,a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper.Firstly,the centers of the ellipses are extracted,and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points.Subsequently,since the infinite lines passing through the centers of the marks are parallel,the other center projection coordinates are expressed as the solution problem of linear equations.The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed,and the results are utilized as the true image points to achieve the high precision camera calibration.As demonstrated by the simulations and practical experiments,the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks.The relevant results confirm the precision and robustness of the proposed approach.展开更多
Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative char...Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.展开更多
基金supported by the Aerospace Science and Technology Joint Fund(6141B061505)the National Natural Science Foundation of China(61473100).
文摘To address the eccentric error of circular marks in camera calibration,a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper.Firstly,the centers of the ellipses are extracted,and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points.Subsequently,since the infinite lines passing through the centers of the marks are parallel,the other center projection coordinates are expressed as the solution problem of linear equations.The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed,and the results are utilized as the true image points to achieve the high precision camera calibration.As demonstrated by the simulations and practical experiments,the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks.The relevant results confirm the precision and robustness of the proposed approach.
基金supported in part by the Equipment Development Pre-research Project funded by Equipment Development Department,PRC under Grant No.50923010501Fundamental Research Program of Shenyang Institute of Automation(SIA),Chinese Academy of Sciencess under Grant No.355060201。
文摘Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.
