基于教育价值论视角探析兵团精神的教育价值

兵团精神是社会主义核心价值体系在兵团的具体体现,是兵团文化的核心与精髓,是指引兵团人实现兵团跨越式发展和长治久安的精神动力和智力支持。开展兵团精神教育是屯垦戍边和＂教育戍边＂事业进程中的一个重要课题。本文在教育价值论的视角下分析开展兵团精神教育所依托的优势资源,包括文化资源优势、特殊体制优势、理论指导优势及实践经验优势等,并在此基础上分别从本质属性、理论创新及学生发展三个方面进一步探讨了兵团精神的教育价值。     

导弹贮存寿命预估的组合模型

在分析比较了几种寿命预估方法优缺点的基础上,针对单一预估方法在解决实际问题中所暴露的局限性,优选了GM-BPNN组合预估模型。该组合模型既利用了灰色预估理论具有所需要的样本数据少、原理简单、运算方便、短期预估精度高、可检验等优点,也发挥了神经网络并行计算、容错能力强、自适应能力强等优点。最后利用该组合模型对弹上某设备寿命进行了预估,取得了较好的预测效果。     

RBF神经网络的C4ISR系统效能评估

针对当前效能评估方法存在人为因素干扰的问题,并考虑到RBF网络具有收敛速度快,无局部极小优点等优点,建立了基于RBF网络的C4ISR系统效能评估模型.模型采用K-均值聚类算法确定隐层节点数据中心和扩展常数;用最小二乘法进行输出层权值的修正;为防止病态,用目标总函数动态地确定隐节点的个数.最后,将得到的结果和BP网络输出...     

基于小波变换和中值滤波的无人机图像去噪算法

在无人机航拍图像的实时传输过程中,有可能会同时受到脉冲和高斯混合噪声的污染,为后续图像的识别造成很大的困难.针对这种情况,提出了一种基于中值滤波和小波变换相结合的图像去噪方法.仿真结果表明,该方法不仅能有效地滤除脉冲和高斯的混合噪声,而且可以很好地保留图像的细节信息,改善图像的视觉效果.     

采用弹道修正技术的红外干扰弹性能优化

针对弹道修正弹的高维非线性特性导致的性能优化难题,改变概念设计阶段传统的串行设计方式,提出了一种基于实验设计(Design Of Experiments,DOE)和响应面(Response Surface Methodology,RSM)的智能优化算法,定义基本的弹丸结构模型以及相关的设计参数.在DOE的基础上,将设计...     

弹性基上圆柱管弯曲行为的高阶剪切梁理论解

以弹性基上圆柱管为研究对象,基于高阶剪切变形梁理论推导了Winkler弹性基圆柱管弯曲变形的高阶剪切梁理论控制方程,给出四种典型工况弯曲问题的精确解.研究表明,无须假设剪切修正系数,只需引入适当横截面翘曲形状函数,高阶梁理论在圆柱管内外表面满足剪应力τxr为零的边界条件,且对于不同长径比和厚径比的圆柱管弯曲问题均能提供...     

多目标环境下基于分布式融合思想的误差估计方法

针对多雷达多目标跟踪过程中分布未知的系统误差估计问题,提出了基于"分布式融合思想"的误差估计方法。给出相应误差估计方法的计算公式,利用改进截断奇异值方法来减轻矩阵病态性的影响,提高误差估计的稳健性。设置了两种不同的系统误差仿真场景,对"分布式"误差估计方法在两种情形下的估计性能进行了仔细对比分析。结合"分布式"误差估计方法与"集中式估计"方法所体现出的优缺点,提出了一种将两种方法结合起来的系统误差估计算法,算法通过合理选择阈值门限η,能够在多雷达多目标且系统误差分布未知的复杂环境下对两种误差估计算法自适应地进行切换,从而充分发挥两种误差估计算法各自的优点,给出更好的误差估计结果。     

A Fuzzy Cluster and Retinex Theory-based Variation Model for Inhomogenous Image Segmentation

A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership function.The existence of the solution of the proposed model is proved theoretically.A valid algorithm is designed to make numerical solution of the model under the framework of alternating minimization.The last experimental results show that the model can make segmentation of the real image with intensity inhomogeneity effectively.     

Buckling analysis of embedded graphene oxide powder-reinforced nanocomposite shells

In this study, the buckling analysis of a Graphene oxide powder reinforced (GOPR) nanocomposite shell is investigated. The effective material properties of the nanocomposite are estimated through Halpin-Tsai micromechanical scheme. Three distribution types of GOPs are considered, namely uniform, X and O. Also, a first-order shear deformation shell theory is incorporated with the principle of virtual work to derive the governing differential equations of the problem. The governing equations are solved via Galerkin's method, which is a powerful analytical method for static and dynamic problems. Comparison study is performed to verify the present formulation with those of previous data. New results for the buckling load of GOPR nanocomposite shells are presented regarding for different values of circumfer-ential wave number. Besides, the influences of weight fraction of nanofillers, length and radius to thickness ratios and elastic foundation on the critical buckling loads of GOP-reinforced nanocomposite shells are explored.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.