装备精确修理浅析

如何用有限的装备修理力量,在战役全过程中尽可能多地修复战损装备,提高装备参战率,是战役装备修理的任务.提出精确修理以装备动态需求为依据,通过优化装备修理各要素,为战损装备提供"精确"的服务,来实现装备修理的快速高效,并通过建立修理人员效能评估模型,说明精确修理的高效性.     

目标位置不确定性的图形描述

目标的观测和状态估计涉及目标的各种运动和属性数据,其中位置不确定性是最重要的性能指标.借鉴测绘学科中衡量点元位置不确定性的方法,分别用椭圆和椭球来表示二维目标和三维目标的位置误差散布的不确定性区域.从位置误差的协方差矩阵导出1-σ误差椭圆(球)参数,再由椭圆(球)的概率分布导出相应的95%圆(球)概率误差的半径.位置不确定性的图形描述为评价位置观测数据的质量提供了可视化的方法.     

残差域加权ACF基音周期检测算法

针对传统自相关(ACF)基音周期检测算法存在较多的倍频和半频错误,文章提出一种基于线性预测残差域加权ACF基音周期检测方法。首先对语音信号中心削波,减小共振峰的影响;而后进行线性预测分析获得残差信号,对其求自相关值和循环幅度差(CAMDF)值,以CAMDF的倒数值为权重加权ACF进行基音周期检测;最后通过基音平滑算法对提取的基音轨迹进行后处理。仿真实验表明,该算法可降低基音提取的倍频和半频错误,提高估计精度。     

利用动态规划原理实现多冲量最优交会问题

多冲量最优交会问题是航天领域非常重要的一个研究方向,从运筹学角度将此问题转化为一个多阶段多维动态规划问题,并着重对多阶段二维动态规划进行分析研究。在给出状态方程及指标函数递推公式的基础上,得到了两冲量和三冲量交会问题最优解的求解算法,之后分别通过实例验证了算法的有效性。对于以地心角等参数为决策变量的更高维问题,讨论了利用进化算法等降维方法实现多维动态规划的思想。     

战车火控系统目标角速度测量误差分析

目标角速度测量误差是严重影响解命中问题解算结果的重要的输入误差。通过目标角速度测量误差的理论分析,证明目标角速度测量技术方案的不合理,是形成角速度粗大测量误差的主要原因。火控系统向目标自动跟踪式方向的发展及非线性滤波器在目标信息处理中的应用,是提高目标运动状态估计精度的重要技术途径。     

一座小型建筑空调动态负荷的ANN预测研究

人工神经网络(Artificial Neural Network,简称ANN)是一种崭新非线性建模和预测方法,具有良好的非线性品质和极高的拟合精度。在前人研究的基础上,针对一座小型建筑物建立了其空调动态负荷预测的BP模型。为克服常规BP训练算法的缺陷,提出了一种改进的遗传搜索算法,结果证明是一种高效实用的算法。     

弹丸动平衡自动测试方法研究

采用 IBM- PC机为主机 ,对弹丸动不平衡量 (m1 ,m2 )及相位角α进行自动测试并做相应的数据处理。设计了弹丸左、右两标定面的平面分离电路、A/D转换电路等。     

非高斯噪声下的滤波方法

干扰条件下的机动目标跟踪在一些文献[1][2]中已有讨论,但利用多传感器,尤其是被动传感器进行非高斯观测噪声条件下的目标跟踪仍需要研究。本文讨论了被动传感器在随机干扰条件下进行机动目标跟踪的方法,其观测量包含非高斯噪声,也可能包含影响观测值的随机干扰。与基于卡尔曼滤波的常见方法不同,采用动态规划算法进行多假设检验,从而估计目标的状态。仿真试验表明,本文方法能有效地处理非高斯噪声情况下的目标跟踪问题,而基于卡尔曼滤波的跟踪方法,比如EKF,则效果较差。     

Dynamic pricing in a trade-in program with replacement and new customers

Trade-in programs have been widely adopted to enhance repeat purchase from replacement customers. Considering that a market consists of replacement and new segments, we study the joint and dynamic decisions on the selling price of new product (hereafter, “selling price”) and the trade-in price involved in the program. By adopting a vertical product differentiation choice model, we investigate two scenarios in this paper. In the base model, the manufacturer has sufficiently large production capacity to fulfill the customer demand. We characterize the structural properties of the joint pricing decisions and compare them with the optimal pricing policy under regular selling. We further propose a semi-dynamic trade-in program, under which the new product is sold at a fixed price and the trade-in price can be adjusted dynamically. Numerical experiments are conducted to evaluate the performance of the dynamic and semi-dynamic trade-in programs. In an extended model, we consider the scenario in which the manufacturer stocks a batch of new products in the beginning of the selling horizon and the inventory cannot be replenished. Following a revenue management framework, we characterize the structural properties with respect to time period and inventory level of new products.     

Dynamic pricing and matching in ride-hailing platforms

Ride-hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride-hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride-hailing platforms. We provide a review of matching and DP techniques in ride-hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.