Maintenance and flight scheduling of low observable aircraft

In this article, we focus on relatively new maintenance and operational scheduling challenges that are faced by the United States Air Force concerning low‐observable (LO) or stealth aircraft. The LO capabilities of an aircraft degrade stochastically as it flies, making it difficult to make maintenance scheduling decisions. Maintainers can address these damages, but must decide, which aircraft should be put into maintenance, and for how long. Using data obtained from an active duty Air Force F‐22 wing and interviews with Air Force maintainers and program specialists, we model this problem as a generalization of the well‐known restless multiarmed bandit superprocess. Specifically, we use an extension of the traditional model to allow for actions that require varying lengths of time, and generate two separate index policies from a single model; one for maintenance actions and one for the flying action. These index policies allow maintenance schedulers to intuitively, quickly, and effectively rank a fleet of aircraft based on each aircraft's LO status and decide, which aircraft should enter into LO maintenance and for how long, and which aircraft should be used to satisfy daily sortie requirements. Finally, we present extensive data‐driven, detailed simulation results, where we compare the performance of the index policies against policies currently used by the Air Force, as well as some other possible more naive heuristics. The results indicate that the index policies significantly outperform existing policies in terms of fully mission capable (FMC) rates. In particular, the experiments highlight the importance of coordinated maintenance and flying decisions. © 2015 Wiley Periodicals, Inc. 62:60–80, 2015     

关于强增生算子的有限族的强收敛定理

提出了Banach空间中非线性增生算子方程带误差的三重迭程式, 研究了多个非线性增生算子解与多个强伪压缩映象的公共不动点逼近问题, 获得 2个收敛定理。     

Machine scheduling with an availability constraint and job delivery coordination

In this paper we study the scheduling problem that considers both production and job delivery at the same time with machine availability considerations. Only one vehicle is available to deliver jobs in a fixed transportation time to a distribution center. The vehicle can load at most K jobs as a delivery batch in one shipment due to the vehicle capacity constraint. The objective is to minimize the arrival time of the last delivery batch to the distribution center. Since machines may not always be available over the production period in real life due to preventive maintenance, we incorporate machine availability into the models. Three scenarios of the problem are studied. For the problem in which the jobs are processed on a single machine and the jobs interrupted by the unavailable machine interval are resumable, we provide a polynomial algorithm to solve the problem optimally. For the problem in which the jobs are processed on a single machine and the interrupted jobs are nonresumable, we first show that the problem is NP‐hard. We then propose a heuristic with a worst‐case error bound of 1/2 and show that the bound is tight. For the problem in which the jobs are processed on either one of two parallel machines, where only one machine has an unavailable interval and the interrupted jobs are resumable, we propose a heuristic with a worst‐case error bound of 2/3. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

度量空间的非标准特性(Ⅱ)

本文在文《度重空间的非标准特性（Ⅰ）》的基础上,用非标准分析方法刻划了度量空间上的全有界映射与紧映射的许多特性,并给出了一个赋范线性空间的维数有限的非标准特征。最后,在赋范线性空间中,通过弱拓扑性质的非标准刻划,简洁地证明了Ｅｂｅｒｌｅｉｎ－Ⅲ定理。     

局部耗散型算子的Ishikawa迭代序列收敛性判别准则

给出了局部耗散型算子的Ishikawa迭代序列的收敛定理,将近期该领域的许多结果一般化。     

某岸舰导弹作战指挥模拟系统的设计

针对岸舰导弹作战指挥训练设计的模拟系统,运用直接模拟的方法构建系统框架,给出了作战指挥、发射过程和飞行弹道模拟的方法,分析了矢量海图的制作和嵌入技术,运用墨卡托制图原理和导弹射击理论建立了射击计算数学模型,运用GDI+绘图和数据库技术实现了战斗现场保存回放功能.     

基于中间表示规则替换的二进制翻译中间代码优化方法

动态二进制翻译在实现多源到多目标的程序翻译过程中,为屏蔽不同源平台间的硬件差异引入中间代码,采用内存虚拟策略进行实现,但同时带来中间代码膨胀问题。传统的中间代码优化方法主要采用对冗余指令进行匹配删除的方法。将优化重点聚焦在针对特殊指令匹配的中间表示规则替换上,提出了一种基于中间表示规则替换的二进制翻译中间代码优化方法。该方法针对中间代码膨胀所呈现的几种典型情景,描述了中间表示替换规则,并将以往应用在后端代码优化上的寄存器直接映射策略应用在此处。通过建立映射公式,实现了将原来的内存虚拟操作替换为本地寄存器操作,从而降低了中间代码膨胀率。使用SPEC CPU2006测试集进行了实验,验证了此优化方法的正确性和有效性。测试用例在优化前和优化后的执行结果一致,验证了优化方法的正确性;优化后测试用例的中间代码平均缩减率达到32.59%,验证了优化方法的有效性。     

对流层散射双向时间比对中对流层斜延迟估计

为精确估计对流层散射双向时间比对系统中对流层斜延迟,分析了估计卫星信号对流层斜延迟的Hopfield天顶延迟模型及CFA2.2映射函数模型并对其进行修正,从而适用于对流层散射斜延迟的精确估计。根据北纬35°~37°范围内的三个测站2010—2012三年的气象数据,验证Hopfield模型精度范围小于35 mm,并将三个测站按相互之间基线距离的不同分为三组比对站,利用2012年的气象数据,计算在不同入射角下一年的对流层散射斜延迟,并得出最大斜延迟对应的年积日和入射角。结果表明,三组比对站的最大单向散射斜延迟为21.82~35.45 m。在双向比对抵消90%的情况下,时间延迟为7.3 ns~11.7 ns;相互抵消95%时,时间延迟为3.6 ns~5.9 ns。     

均匀面积映射及其在均匀纹理映射中的应用

为形成一个具有更小拉伸的网格参数化映射,在三角形网格面片面积均匀分布的情况下,采用分两步走的均匀面积映射法。通过均质化参数域里的三角形面积,用均匀或不均匀方式有效地降低了采样稀疏问题的影响,避免了传统方法中矩阵奇异值的计算。对三角形网格,先进行均值协调参数化,然后通过一个凸组合让参数域更加均匀,得到一个拉伸较低、面积更加均匀的三角形分布,从而可以更好地取样且适合于纹理映射。通过实验和对比,可以证明在均匀纹理映射方面,均匀面积映射法比保角映射法和等积投影映射法都有明显的提高。     

涡轮泵故障检测的频段能量比SOM算法

为了解决缺少故障样本情况下的涡轮泵健康状态判别问题,分析了涡轮泵振动信号的频谱,提取了频段能量比作为其故障检测特征,并讨论了自组织映射的竞争学习原理及聚类结果的U-矩阵表示,提出了一种基于频段能量比的自组织映射故障检测算法,并实现了该算法最佳匹配神经元的选择和权重向量的自适应更新。通过某型液体火箭发动机历史试车数据的验证,结果表明,健康涡轮泵数据利用该算法聚类时仅存在一个类别,相邻神经元距离小于0.1;反之,故障涡轮泵数据利用该算法聚类时明显存在两个或多个类别,且相邻神经元的最大距离大于0.1。因此,基于频段能量比的SOM算法能有效地判别涡轮泵的健康状况。