Infrared radiation characteristics of dagger-type hypersonic missile

Hypersonic vehicles emit strong infrared radiation from their high-temperature exhaust plume and body, which is critical for infrared early warning, tracking, and guidance. In this work, a comprehensive analysis is conducted on the factors involved in air dissociation reaction within the shock layer of hypersonic missile heads, as well as the multi-component afterburning effect of the exhaust plume. A novel Reverse Monte Carlo Method(RMCM) is proposed for infrared radiation calculation, which utilizes two-dimensional Low-Discrepancy Sequences(LDS) to improve computational accuracy. The numerical calculations for a dagger-type missile show that afterburning reactions increase the temperature on the centerline of the outlet exhaust plume by about 1000 K. The total infrared radiation intensity of the missile is the highest in the 1–3 μm band, with the hightemperature wall of the nozzle being the primary source of solid radiation, and gas radiation primarily coming from H_(2)O. The radiation intensity of the missile exhaust plume in the 3–5 μm band is the highest, with radiation sources primarily coming from CO_(2), CO, and HCl. Afterburning reactions of the exhaust plume increase the total infrared radiation intensity of the missile by about 0.7times. These results can provide reference for the detection and guidance of hypersonic missiles.

Subsampling bias and the best-discrepancy systematic cross validation

Statistical machine learning models should be evaluated and validated before putting to work.Conventional k-fold Monte Carlo cross-validation(MCCV)procedure uses a pseudo-random sequence to partition instances into k subsets,which usually causes subsampling bias,inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation.Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory,we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence,which ensures low subsampling bias and leads to more precise expected-prediction-error(EPE)estimates.Experiments with 156 benchmark datasets and three classifiers(logistic regression,decision tree and na?ve bayes)show that in general,our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18%and the variances around 26.73%.In comparison,the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58%and 11.85%,respectively.The leave-one-out(LOO)can lower the EPE around 2.50%but its variances are much higher than the any other cross-validation(CV)procedure.The computational time of our cross-validation procedure is just 8.64%of the MCCV,8.67%of the stratified MCCV and 16.72%of the LOO.Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio.This makes our approach particularly pertinent when solving bioscience classification problems.Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism.

Halton-type sequences from global function fields

For any prime power q and any dimension s≥1, a new construction of (t, s)-sequences in base q using global function fields is presented. The construction yields an analog of Halton sequences for global function fields. It is the first general construction of (t, s)-sequences that is not directly based on the digital method. The construction can also be put into the framework of the theory of (u, e, s)-sequences that was recently introduced by Tezuka and leads in this way to better discrepancy bounds for the constructed sequences.