The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristic...The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.展开更多
In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneit...In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneity means that regression coefficients are grouped and have exactly the same value in each group.A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints.We develop a modified alternating direction method of multipliers algorithm to obtain the estimators and demonstrate its convergence.The efficiency of both sparsity and homogeneity detection can be improved by combining the prior information.Our proposed method is further illustrated by simulation studies and analysis of an ozone dataset.展开更多
We consider the control of the diserete component n_(t)of a owitching Markov proceaa x_(t)=(z_(t),n_(t))when there ia a running cost and an immediate coat c(i,j)for owitching n_(t)from i to j.We satudy the minimizatio...We consider the control of the diserete component n_(t)of a owitching Markov proceaa x_(t)=(z_(t),n_(t))when there ia a running cost and an immediate coat c(i,j)for owitching n_(t)from i to j.We satudy the minimization of the ergodic(or long-term average)total coat.Eooentially,this paper trento the cnce where,for n_(t)=n fixed,z_(t)ia a reflected diffusion or a reflected diffusion with jumps,nt being,for fixed z,a continuous-time Markov chain.Using the vanishing discount appronch,we exctend existing reoulta dealing with the situation where nt evolvea only by the switching control action and the diffusion is non-degenerate.Moreover,we solve the ergodic problem for a claso of diffusiono which can be degenerate and for an example with aboorbing atate.展开更多
TheMahlermeasure,a notion often appearing in the number theory and dynamic system literature,provides a way to quantify the instability in a linear discrete-time system.
基金been supported by Project of the National Natural Science Foundation of China(No.62073256)the Shaanxi Provincial Science and Technology Department(No.2020GY-125)Xi’an Science and Technology Innovation talent service enterprise project(No.2020KJRC0041)。
文摘The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.
文摘In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneity means that regression coefficients are grouped and have exactly the same value in each group.A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints.We develop a modified alternating direction method of multipliers algorithm to obtain the estimators and demonstrate its convergence.The efficiency of both sparsity and homogeneity detection can be improved by combining the prior information.Our proposed method is further illustrated by simulation studies and analysis of an ozone dataset.
文摘We consider the control of the diserete component n_(t)of a owitching Markov proceaa x_(t)=(z_(t),n_(t))when there ia a running cost and an immediate coat c(i,j)for owitching n_(t)from i to j.We satudy the minimization of the ergodic(or long-term average)total coat.Eooentially,this paper trento the cnce where,for n_(t)=n fixed,z_(t)ia a reflected diffusion or a reflected diffusion with jumps,nt being,for fixed z,a continuous-time Markov chain.Using the vanishing discount appronch,we exctend existing reoulta dealing with the situation where nt evolvea only by the switching control action and the diffusion is non-degenerate.Moreover,we solve the ergodic problem for a claso of diffusiono which can be degenerate and for an example with aboorbing atate.
基金supported by Hong Kong Research Grants Council[grant number 618608],[grant number 618213]the National Science Foundation of China[grant number 60834003].
文摘TheMahlermeasure,a notion often appearing in the number theory and dynamic system literature,provides a way to quantify the instability in a linear discrete-time system.