https://openaccess.iyte.edu.tr/bitstream/11147/4414/1/4414.pdf

The effect of the interlayer on the ballistic performance of ceramic/composite armors: Experimental and numerical study

Penetration of 6061-T6511 aluminum targets by ogive-nose steel projectiles with striking velocities between 0.5 and 3.0 km/s

https://digital.library.unt.edu/ark:/67531/metadc681158/m2/1/high_res_d/3358.pdf

Penetration Experiments with 6061-T6511 Aluminum Targets and Spherical-Nose Steel Projectiles at Striking Velocities Between 0.5 and 3.0 km/s

Impact failure mechanisms in alumina tiles on finite thickness support and the effect of confinement

For any two random variables X and Y with distributions F and G defined on [0,∞), X is said to stochastically precede Y if P(X≤Y) ≥ 1/2. For independent X and Y, stochastic precedence (denoted by X≤spY) is equivalent to E[G(X–)] ≤ 1/2. The applicability of stochastic precedence in various statistical contexts, including reliability modeling, tests for distributional equality versus various alternatives, and the relative performance of comparable tolerance bounds, is discussed. The problem of estimating the underlying distribution(s) of experimental data under the assumption that they obey a stochastic precedence (sp) constraint is treated in detail. Two estimation approaches, one based on data shrinkage and the other involving data translation, are used to construct estimators that conform to the sp constraint, and each is shown to lead to a root n-consistent estimator of the underlying distribution. The asymptotic behavior of each of the estimators is fully characterized. Conditions are given under which...

/pdf/nonparametric-estimation-of-a-distribution-subject-to-a-3tmzdwx8d9.pdf

Nonparametric Estimation of a Distribution Subject to a Stochastic Precedence Constraint

A method is given for the comparison of two linear forms of a common random vector under the criterion of Pitman's measure of closeness. Assuming multivariate normality of the random vector, one can determine exact expressions for the closeness probabilities. The applicability of the theory is illustrated on the comparison of ridge regression estimators.

Comparison of linear estimators using Pitman's measure of closeness

Comparison of Linear Estimators Using Pitman's Measure of Closeness

http://aux.ciar.org/ttk/mbt/papers/papers.2007-12-21/armor.x.ijie.vol18pp001_021.scale_model_experiments_with_ceramic_laminate_targets.anderson_scott_mullin_piekutowski_blaylock_poormon.1996.pdf

Scale model experiments with ceramic laminate targets

The theory presented in Mason, Keating, Sen and Blaylock (1990) and applied to the comparison of ridge regression estimators using Pitman's measure of closeness is illustrated on two data sets, one that contains a strong collinearity and one that is nearly orthogonal. The regions of preference for the ridge regression estimator over the least squares estimator are established and shown to be dependent on the eigenvalues and eigenvectors of the correlation matrix of the predictor variables, the unknown coefficient vector, the error variance, and the value of the biasing constant in the ridge estimator.

Ridge regression estimator comparisons using pitman's measure of closeness

: A kinetic energy penetration scaling study has been performed with L/D 20 projectiles into homogeneous RHA-like targets at two impact velocities: 1.5 kmls and 2.2 km/s. The experiments were performed at three scales: nominally 1/3, 1/6, and 1/12. The report documents the similitude analysis, discusses issues in scaling using replica models, and the experiments. The report concludes with a discussion of a possible cause of the observed distortions in perfect scaling.

Neil W. Blaylock

Papers

Comparison of linear estimators using Pitman's measure of closeness

Comparison of Linear Estimators Using Pitman's Measure of Closeness

Scale model experiments with ceramic laminate targets

Ridge regression estimator comparisons using pitman's measure of closeness

Scale Model Penetration Experiments: Finite Thickness Steel Targets.