Applying a bootstrap approach for setting reorder points in military supply systems

This paper develops and applies a nonparametric bootstrap methodology for setting inventory reorder points and a simple inequality for identifying existing reorder points that are unreasonably high. We demonstrate that an empirically based bootstrap method is both feasible and calculable for large inventories by applying it to the 1st Marine Expeditionary Force General Account, an inventory consisting of $20–30 million of stock for 10–20,000 different types of items. Further, we show that the bootstrap methodology works significantly better than the existing methodology based on mean days of supply. In fact, we demonstrate performance equivalent to the existing system with a reduced inventory at one‐half to one‐third the cost; conversely, we demonstrate significant improvement in fill rates and other inventory performance measures for an inventory of the same cost. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 459–478, 2000     

机载激光雷达数据的三维深度学习树种分类

针对传统基于激光雷达(Light Detection And Ranging,LiDAR)数据的树种分类方法难以直接且全面地利用点云的三维结构信息的问题,提出一种基于三维深度学习的机载LiDAR数据的树种分类方法.该方法直接从三维数据中抽象出高维特征,而无须将点云转化为体素或二维图像.以塞罕坝国家森林公园内白桦和落叶松...     

导弹突防后弹道机动调整策略强化学习

针对弹道导弹中段突防后飞行弹道与标准弹道产生较大偏离的弹道机动调整问题,建立了机动调整时机策略最优化模型。设计了机动调整逆序Q学习算法,采用Tile coding逼近器编码状态特征空间,并对其进行线性逼近。构建了Q学习算法与蒙特卡罗方法相结合的逆序更新策略机制,以对导弹机动调整最优时机进行训练。仿真测试分析结果表明,在给定场景参数下,通过10 000代强化学习算法训练得到的策略能够可靠地使用最少机动次数控制导弹突防后飞行弹道的调整决策,验证了方法的有效性。     

一类非线性动力系统的时滞反馈分叉控制

对时滞控制作用下的参数激励van der Pol-Duffing方程进行了研究,着重研究了时滞参数对该类参数激励系统的1/2亚谐共振-主参数共振分叉响应控制。首先采用摄动法从理论上推导出时滞动力系统的分叉响应方程,再采用数值模拟的方法研究了时滞参数对系统分叉响应的影响。研究结果表明,适当选取时滞参数,不仅可以改变分叉响应曲线的拓扑形态,还可以平移分叉响应曲线,即使其分叉提前或滞后,还能同时满足这两种要求。     

C4ISR领域本体研究初探

如何实现C4ISR系统的跨越式发展,是当前的研究重点。本体论作为最新的知识工程化技术,信息系统建设是其研究、应用的重要领域。通过分析C4ISR系统特点及未来军事发展趋势,结合C4ISR系统建设的实际情况,阐述了军事领域开展本体研究的广阔前景,并探求建立C4ISR领域本体库,对本体库的作用及发展过程提出了初步设想,在C4ISR系统建设和本体应用两方面做出了有益的探索。     

背景音乐对人类学习效率影响的脑与行为研究进展

背景青乐是与主体的意识或行为无直接关系,通过非青乐鉴赏环境这一媒介间接作用于主体意识与行为的音乐。近些年来将背景音乐引入教育领域来探讨其对学习效率的影响是研究的新课题。从背景音乐对学习效率影响的相关理论、背景音乐对学习效率影响的实验研究以及背景音乐影响的研究趋势等方面进行了综述。     

C4ISR系统开发的仿真模型描述研究

C4ISR(Command、Control、Communication、Computer、Intelligence、Surveillance and Reconnaissance)系统开发的仿真模型涉及多个应用领域,具有多粒度、阶段相关性等的特性.本文提出了C4ISR系统开发的仿真模型体系,给出了仿真模型的规范化描述....     

Survey and technological analysis of laser and its defense applications

The laser technology has made remarkable progress over the past couple of decades.It is being widely employed in diverse domains,such as holography,space sciences,spectroscopy,medical sciences,micro and power electronics,industrial engineering,and most distinctively,as directed energy military weapons.Owing to their active transmissions,laser systems are similar to microwave radars to some extent;however,unlike conventional radars,the laser operates at very high frequencies thus making it a potent enabler of narrow-beam and high energy aerial deployments,both in offensive and defensive roles.In modern avionics systems,laser target indicators and beam riders are the most common devices that are used to direct the Laser Guided Weapons(LGW)accurately to the ground targets.Additionally,compact size and outstanding angular resolution of laser-based systems motivate their use for drones and unmanned aerial applications.Moreover,the narrow-beam divergence of laser emissions offers a low probability of intercept,making it a suitable contender for secure transmissions and safety-critical operations.Furthermore,the developments in space sciences and laser technology have given syner-gistic potential outcomes to use laser systems in space operations.This paper comprehensively reviews laser applications and projects for strategic defense actions on the ground or in space.Additionally,a detailed analysis has been done on recent advancements of the laser technology for target indicators and range-finders.It also reviews the advancements in the field of laser communications for surveillance,its earlier state of the art,and ongoing scientific research and ad-vancements in the domain of high energy directed laser weapons that have revolutionized the evolving military battlefield.Besides offering a comprehensive taxonomy,the paper also critically analyzes some of the recent contributions in the associated domains.     

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.     

ARCS动机模型在初中英语语法教学中的应用

语法学习能在一定程度上保证语言的地道性、流畅性和实用性。但就目前来看,英语学习者学习语法的积极性普遍不高,不少英语学习者抱怨英语语法项目琐碎繁杂、语法学习枯燥无聊。针对此现状,本文尝试将ARCS模型的四类动机策略整合于初中英语语法教学,以期有效激发并维持学习者的语法学习热情,提高语法教学效果。