某型火炮内弹道性能预测方法的比较分析

本文对于有关单位已有的若干堪用火炮内弹道性能预测方法进行了探讨,针对其中具有代表性的某型火炮内弹道性能的两种预测方法通过实例计算进行了对比,分析了它们存在误差的原因.最后,本文指出两种方法各有所长,并讨论了各自可以改进的方向.

热塑性复合材料力学问题研究进展

热塑性复合材料综合力学性能优异,是可重复使用航天装备的理想结构材料,其力学行为研究已成为国际固体力学和材料科学领域的研究热点。本文概述了热塑性复合材料在宏观力学性能预测方法、塑性本构关系、损伤和断裂力学行为、典型结构件力学行为分析等方面的研究成果。目前,对热塑性复合材料宏观力学性能的精确预测、合理表征热塑性复合材料的弹塑性损伤力学行为以及对在气动加热、过载、冲击等复杂多场耦合环境下热塑性复合材料典型构件的力学行为模拟等许多关键问题亟待解决;未来可从以下几个方面展开研究:(1)建立热塑性复合材料宏细观统一的力学性能预测模型;(2)开展碳纳米管增强热塑性复合材料的宏观和细观力学分析;(3)研究热塑性复合材料在热力耦合等典型多场耦合环境下的力学行为;(4)开展热塑性复合材料构件级实验研究。

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

METHOD FOR QUICKLY INFERRING THE MECHANISMS OF LARGE-SCALE COMPLEX NETWORKS BASED ON THE CENSUS OF SUBGRAPH CONCENTRATIONS

A Mechanism-Inferring method of networks exploited from machine learning theory caneffectively evaluate the predicting performance of a network model.The existing method for inferringnetwork mechanisms based on a census of subgraph numbers has some drawbacks,especially the needfor a runtime increasing strongly with network size and network density.In this paper,an improvedmethod has been proposed by introducing a census algorithm of subgraph concentrations.Networkmechanism can be quickly inferred by the new method even though the network has large scale andhigh density.Therefore,the application perspective of mechanism-inferring method has been extendedinto the wider fields of large-scale complex networks.By applying the new method to a case of proteininteraction network,the authors obtain the same inferring result as the existing method,which approvesthe effectiveness of the method.

QSAR prediction of antagonistic activity of PCBs towards human PXR by using heuristic method and best subset modeling

Polychlorinated biphenyls(PCBs) can antagonize human pregnane X receptor(hPXR) activation.Such chemicals could pose a serious threat to the reproductive and developmental ability of humans.The quantitative structure activity relationship(QSAR) provides a promising method for the estimation of PCBs' antagonistic activity.In this investigation,a QSAR model was developed by using heuristic method and best subset modeling(r2 = 0.873,q2LOO=0.742).The built model was validated externally by splitting the original data set into training and prediction sets.The results of the model derived are as follows:r2 = 0.907,q2LOO=0.709,r2pred=0.676,suggesting developed QSAR model had good robustness and predictive ability.The applicability domain(AD) of the model was assessed by Williams plot.The antagonistic activity(?logKi) of 108 PCBs,which are unavailable by experiment at present,was predicted within the applicability domain of the model.The critical structural features related to the activity of PCBs were identified.