双基地雷达系统时间同步方案研究

针对双基地雷达系统中时间同步方案,结合双向时间比对法对站点依赖性小,不易受外界环境影响和具备较高的精度等优势,分析了三种典型的双向时间比对法,给出了原理框图,推导了比对公式,分析了各自方案的误差来源。并作了方案之间的对比,得出基于对流层散射信道的双向时间比对法,更适合于目前的双基地雷达体系。为实现双基地雷达系统之间的时间同步提供一定的参考。     

Lot sizing with learning and forgetting in setups: Analytical results and insights

This article considers the dynamic lot sizing problem when there is learning and forgetting in setups. Learning in setups takes place with repetition when additional setups are made and forgetting takes place when there is a break between two successive setups. We allow the amount forgotten over a break to depend both on the length of the break and the amount of learning at the beginning of the break. The learning and forgetting functions we use are realistic. We present several analytical results and use these in developing computationally efficient algorithms for solving the problem. Some decision/forecast horizon results are also developed, and finally we present managerial insights based on our computational results. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 93–108, 2016     

Strategy in the Age of Artificial Intelligence

ABSTRACT

We argue that Artificial Intelligence (AI) will, in the very near future, have a profound impact on the conduct of strategy and will be disruptive of existing power balances. To do so, we review the psychological foundations of strategy and explore the ways in which AI will impact human decision-making. We then review current and evolving capabilities in ‘narrow’, modular AI that is optimised to perform in a particular environment, and explore its military potential. Lastly, we look ahead to the more distant prospect of a general AI.     

英语学习中的文化背景知识

分析了英语学习过程中文化背景知识的种类及其特性 ,阐明了这些背景知识对英语学习的影响 ,并介绍了获取这些文化背景知识的主要途径 .     

高频相移整流器的开关函数分析法

利用开关函数建立了描述高频相移整流器输入输出关系的开关传递函数矩阵,并以此为基础分析了系统在对称和非对称情况下,对高频相移整流器的影响,仿真结果验证了理论分析结论的正确性。     

频率性规律在非专业英语学习中的窘迫地位及其挽救方法

频率性规律是英语学习本质而重要的特征,在阐明频率性规律在高校非专业英语学习中的窘迫地位之后,以频率性规律为理论模式,以高校非专业英语教学为实践框架,从单词、词组、语法和文化背景等4个方面,对如何挽回频率性规律在英语学习和教学中的重要地位进行了尝试。这种努力,对于解决高等院校非专业英语教学中学时和教学内容之间的尖锐矛盾,有重要的现实意义。     

铁粉焊条熔化过程中基本参数研究

通过试验研究了焊条直径和铁粉加入量对焊条熔化参数的影响.试验结果表明,焊条直径增加,焊条药皮重量系数、熔敷效率增加,熔化速度和熔敷速度降低;熔敷金属中合金元素Mn、Si含量增加,但Mn、Si的过渡系数减小.当增加药皮中铁粉加入量,药皮重量系数、熔敷效率和熔敷速度增加,熔化速度降低,由于铁粉的稀释作用使熔敷金属中合金元素Mn、Si含量及过渡系数降低.     

考虑黏弹性材料随机性的被动约束层阻尼梁动力学分析

采用传递函数方法研究了阻尼层黏弹性材料随机性对被动约束层阻尼(PCLD)梁动力学特性的影响。由Hamilton原理建立了PCLD梁六阶运动微分方程,通过引入状态向量,建立了系统的状态空间方程,利用传递函数方法得到了梁的固有频率和损耗因子。以黏弹性材料分数导数模型中的参数作为基本的随机变量,并假设其服从正态分布,使用Monte Carlo直接抽样法考察了材料模型参数的随机性对结构固有频率和模态损耗因子的影响。计算结果表明黏弹性材料参数的随机性对梁动力学特性的变异系数影响较大,模态损耗因子的变异系数最大值是材料参数变异系数的4.5倍。     

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.     

基于深度神经网络的OFDM系统IQ不平衡补偿技术

正交频分复用(OFDM)是现代移动通信中一项重要的物理层通信技术,并且OFDM系统要求子载波间严格正交。然而在实际系统中,振荡器和滤波器等器件的非理想特性会导致同相正交(In-phase and Quadrature-phase,IQ)不平衡,从而破坏子载波的正交性,严重影响OFDM系统的性能。本文研究了IQ不平衡对OFDM系统的影响,提出了基于并联深度神经网络的IQ不平衡补偿技术。该算法利用了深度神经网络不依赖于模型的特点,直接从接收到的频域信号恢复原输入信号的二进制序列,并利用IQ不平衡的干扰信号来自镜像子载波的先验知识来初始化神经网络,加快其网络优化的收敛速度。仿真结果表明,该算法能有效地补偿IQ不平衡失真,并且在幅度和相位失真的补偿上,其性能都优于传统的导频的最小二乘（LS）估计补偿IQ不平衡的算法,证明了深度学习方法解决物理层问题的优越性。