函数生成器的优化设计

本文介绍了一种优化的多变量函数产生器的设计思想,详尽论述了多变量函数快速插值算法、消除冗余断点搜索的思想以及搜索次数为１的断点搜索策略。实用表明,该产生器高效可行,比一般的多变量函数产生器减少计算量一半左右,有效地缩短了仿真问题的求解时间。     

空中预警机

本文说明什么是空中预警机,它在战争中的作用,以及E-2C 空中预警机的特点。详细叙述了E-3A 的六个航空电子设备分系统和空中预警机的发展趋势。     

一体化联合作战指挥信息流通体系逻辑结构分析

针对协同性联合作战向一体化联合作战转变对作战指挥产生的影响,在正确定位指挥信息流通体系的基础上,从一体化联合作战的本质特征及对指挥信息流通体系的影响出发,设计了基本作战单元指挥信息输入、输出模型,并通过将作战体系区分为四类模块,得出了指挥信息流通体系的逻辑结构.     

关于Tu-Deng函数的一个注记

2009年,Tu和Deng在一个组合猜想成立的基础上,构造了同时具有最优代数免疫性、最优代数次数和高非线性度的一类偶数元布尔函数.这类函数被称为Tu-Deng函数.基于同一猜想,Tu和Deng又构造了同时具有次最优代数免疫性、最优代数次数和较高非线性度的一类偶数元的l -阶弹性函数.通过研究由Tu-Deng函数导出的两个布尔函数的级联的密码学性质,在Tu-Deng猜想成立的基础上,给出一类奇数元的1 -阶弹性布尔函数.这类函数同时具有次最优代数免疫性、最优代数次数和较高非线性度.     

基于截断函数的有限时间滑模控制方法

针对一类二阶不确定非线性系统的鲁棒控制问题,提出一种基于截断函数的有限时间滑模控制算法。该算法具有以下特点:系统状态可在期望的有限时刻收敛为零;受控系统对外部扰动和参数不确定性具有全局鲁棒特性;系统状态的动态响应可解析预测;通过选择不同的截断函数可以实现对控制器性能的调节。仿真结果验证了所提算法的有效性。     

相似分布特性准则下的高斯混合项聚类-合并算法

针对高斯混合模型估计非高斯系统时高斯混合项呈指数级增长问题,提出一种基于相似分布特性准则的聚类-合并方法。通过分析高斯混合项的分布特性,基于扩展积分均方误差代价函数搜索最优置信范围,并对混合项进行高斯聚类,进而获得具有不同分布特性的高斯簇。为防止高斯簇间对高斯子项的重复利用,引入局部最近邻思想对交叉高斯项进行重新分配。采用并行多元素合并方法对高斯簇中的混合项进行合并,在保证无偏性基础上减少下一时刻混合项数量。仿真结果表明,改进算法在保证跟踪精度的同时还可有效提高算法效率。     

Transactional and in-store display data of a large supermarket for data-driven decision-making

This article describes daily and monthly transactional and in-store display data of a large supermarket from January to October in 2019 associated with 28 757 stock-keeping units (SKUs) in 5 categories and 41 subcategories. The database contains five parts, including information about each SKU, in-store display, daily sales, inventory, and replenishment. We also propose some research questions related to assortment planning, pricing, inventory management, and customer behavior. Researchers are welcome to develop data-driven models or other innovative methods to address these questions or other practical problems using this database.     

拟声修辞对英语语言音、意、形的辅助作用

拟声作为一种修辞格,主要有基本拟声和次要拟声两种形式,它常用于文学创作中,创造出所需要的音响效果,使人有身临其境之感,在语言的音韵,意像,形式上发挥着重要的修辞作用。     

Ambulance location for maximum survival

This article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival‐maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival‐maximizing models using data from the Edmonton EMS system. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

What you should know about simulation and derivatives

Derivatives (or gradients) are important for both sensitivity analysis and optimization, and in simulation models, these can often be estimated efficiently using various methods other than brute‐force finite differences. This article briefly summarizes the main approaches and discusses areas in which the approaches can most fruitfully be applied: queueing, inventory, and finance. In finance, the focus is on derivatives of another sort. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008