Monte Carlo方法在计算机辅助布防中的应用

借助于统计推断的有关原理,提出了一种评估多个火力单元射击一批目标教果的方法。该方法可以为防空批导武器系统的合理布置提供参考,也可以用于评估一个已布置的防空反导系统的有效防卫能力,为最大限度地利用防空反导兵器提供依据。     

差分GPS导航系统的设计

本文详细地介绍了差分GPS导航系统的设计方法。试验结果证明了该方法是正确可行的。     

用MSI设计同步序列检测器

本文采用移位寄存器分别和数字比较器、多路选择器、译码器等中规模集成电路(MSI)来设■同步序列检测器。与传统的设计方法相比,这种方法具有设计步骤简单和所得电路具有一定通用性的特点。     

应用Bayes分析方法鉴定误码率

本文应用了Bayes分析方法,即依据样本考虑先验知识,进行统计推断“可信度”。由此推导出了误码率计算公式,从而在数据传输试验中实现了不需要测试设备,只需计算重发次数,传输总次数,即可求得误码率,同时给出其风险,为科学鉴定误码率提供了理论依据。     

光电编码器在调速系统中的应用

本文阐述了用光电编码器代替传统的测速电机和旋转变压器作调速系统反馈元件的技术。结果表明,光电编码器可有效地简化转换电路,更精确地测定出速度和位移两个参数。     

概率化认知控制模式下人为差错概率的量化方法

在现有成果的基础上,对CREAM方法中的人为差错概率量化进行了改进.介绍了CREAM基本法量化人为差错概率的基本思想;讨论了两种概率化认知控制模式的确定方法:贝叶斯网络法和模糊逻辑法,强调了概率化认知控制模式下量化人为差错概率的必要性.通过理论推导,构建了概率化认知控制模式下人为差错概率的量化方法.另外,为了提高计算效...     

蒙特卡洛优化法在机组组合问题中的应用

机组组合(UC)是电力系统经济运行的重要内容之一。根据UC约束特点将机组组合问题分解为单机多时段和多机单时段问题。基于蒙特卡洛(MC)优化法原理,解决单机多时段的启停问题及最后过滤比较找出最优可行解;利用运行费用可导性,解决了爬坡约束并得出多机单时段下的最优解。最后在Madab 6.1下通过算例验证了该方法的可行性。     

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。     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

Carlo Bianco and Guerra per bande: an Italian approach to irregular warfare

Since the end of the Cold War, a huge debate over how war has been changing has emerged; a common feature is that modern conflicts are not state vs. state wars, but ‘irregular wars’. In order to better understand modern irregular warfare, it is important to analyse past authors and ideas. Carlo Bianco’s concept of Guerra per bande highlights elements of mobility with different cooperating units, of terror, and of complex terrain. The present study offers the first English analysis of Carlo Bianco and underlines the similarities of his work to the hybrid warfare concept.