天基核爆光辐射探测方法研究

通过计算提出了天基核爆炸光辐射探测指标要求,根据对源项信号特征的分析,设计了具有通带为1～4000Hz带通滤波器的天基核爆光辐射探测电路,采取了降噪措施,并以实验检验了其灵敏度、信噪比、频率响应和动态范围等性能,提出了软件修正系统误差的方法．     

航空核辐射侦察信息更新率分析

分析了航空核辐射侦察信息的时序关系,结合相关测量设备的发展现状,认为更新率应以水平定位信息为准。分析了侦察过程中定位设备的各种工作状态,指出了其最优工作状态。最后在此工作状态下依据现实可用的定位设备给出了信息更新率。     

日本遗弃化学武器紧急处置中挖掘清理技术研究

就日本遗弃化学武器紧急处置中的挖掘清理技术进行了论述。着重论述了紧急处置现场的设置、挖掘方法步骤、程序、初始位置的挖掘以及污染物的包装等技术。同时,介绍了该技术在南京黄胡子山等地日本遗弃化学武器紧急处置中的应用情况。并对挖掘方法、进度及现场管理等工作提出了探讨性建议。     

BSC信道下线性分组码的差错概率下界分析

针对BSC信道,提出了一种线性分组码的最大似然译码差错概率下界的计算方法.根据最大似然译码算法原理,首先将译码差错概率转化为差错事件的联合概率,基于改进的Dawson-Sankoff界的优化准则,推导出BSC信道下线性分组码差错冗余事件的判决准则,最后得到差错概率下界的计算表达式.该下界只依赖于码字的Hamming重量...     

一种含氧混合燃料喷雾特性的试验研究

针对柴油和所需的油料方案B20D10（即在柴油中掺混20%生物柴油和10%碳酸二甲酯,Dimethyl-Carbonate,DMC）,在某型重型车辆柴油机高压泵试验台上,采用定容弹和高速摄影技术,研究了掺混生物柴油和DMC后对燃油喷雾特性的影响。结果表明：在不同的背景压力下,燃料B20D10油束的贯穿距离低于柴油,最大降幅达到7.0%,锥角最大增加约15.2%;在非破碎阶段,燃料的贯穿距离与时间呈正比关系;在破碎阶段,贯穿距离与时间的关系为s∝tn,其中n约为0.57。试验结果为建立该混合燃料的喷雾模型,并进行缸内燃烧的数值模拟奠定了基础。     

群众路线:中国共产党的根本路线

群众路线是我们党创造性地运用马克思主义的群众观点创立的根本路线,是我们党的优良传统和政治优势.新形势下坚持群众路线,是巩固党的执政地位的必然要求,是推进中国特色社会主义宏伟事业的强大动力,是构建社会主义和谐社会的重要因素,是党的执政能力建设和作风建设的核心所在.当前要结合人民群众的新特点,探索坚持群众路线的新规律;着眼人民群众的新诉求,拓展坚持群众路线的新载体;汲取人民群众的新创见,丰富坚持群众路线的新形式,不断开创坚持群众路线的新局面.     

用于作战效能评估的概率化案例推断模型

作战效能评估是作战辅助决策和装备体系建设等现代战争研究领域的核心问题.针对现有作战效能评估方法在不确定性处理或样本规模等方面的限制,从概率化案例推断的视角来看待作战效能评估问题,使用贝叶斯网络技术建立评估模型.该模型能够以虚拟证据形式纳入案例数据,并借助后验推断机制学得潜在的评估映射规则,以驱动新案例的评估,同时还能揭示评估指标权重的分布情况.以仿真系统结果数据作为案例输入,在Age-naRisk软件中建立并运行该模型,实际验证了模型的有效性.     

MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

多武器平台目标域协同射击控制模型研究

目标域协同射击控制模型是在单平台火控系统空域窗射击控制、射击门控制等基础上,针对多武器平台对点目标或面目标协同射击的需求,进行了目标域和射击效能的定义,提出了空间/时间复合三维射击门的概念,设计了三维射击门模型,分别建立了多武器平台对点目标射击控制模型和多武器平台对面目标射击控制模型,通过对多武器平台协同射击效能的计算分析,证明了目标域协同射击控制模型可显著提高多武器平台协同射击对目标的火力打击效能.     

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.