An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

基于GM(1,1)模型的造纸业上市公司财务研究——以冠豪高新为例

财务风险是企业调整发展战略、提升管理水平的重要参考指标之一。财务风险预测模型的建立能够预测企业在中短期内的财务风险,为企业敲响警钟。以造纸业上市公司冠豪高新为例,选取近五年财务杠杆系数为风险指标,建立GM(1,1)风险预测模型,结果显示该公司短期内财务风险呈下降趋势,调查发现这主要得益于产品成本的降低以及营销策略的改变。     

黑龙江森工林区林业产业结构演变趋势及优势产业选择

采用灰色预测方法对黑龙江森工林区2018—2027年林业三次产业产值进行预测,并在此基础上利用动态偏离-份额分析方法对此阶段黑龙江森工林区的林业优势产业进行选择。结果表明:2019年黑龙江森工林区林业产业结构将演变为"三、一、二"模式;林业第一产业中的林木育种和育苗、经济林产品的种植和采集业可作为优势产业进行重点发展和管理。因此,提出优化黑龙江森工林区林业产业结构的相关建议:继续巩固发展林木育种和育苗、经济林产品的种植和采集这两个优势产业;推动加工制造业向深加工、精加工的方向转变;加强林业旅游与休闲服务业的竞争力。     

一种高能效低时延的LLN路由修复算法

由于低功耗有损网络(LLN)中无线链路的不稳定性和有损性，外部环境的干扰极易导致网络出现故障，从而严重影响网络性能，而LLN网络中现有路由修复算法存在控制开销冗余和修复时延较大等问题。为此，提出了一种高能效低时延的LLN路由修复算法(EELDR-RPL)。该算法通过采用“零额外控制开销通告链路故障及邻居节点信息”机制，使得链路故障节点的子节点能够及时获知链路故障以及链路故障节点的邻居情况；通过采用“自适应调整节点网络深度值”机制，使得链路故障节点能够快速地重新接入网络；通过采用“链路故障节点子节点自适应切换”机制，能够达到优化网络拓扑的目的。仿真结果表明，与现有路由修复算法相比，EELDR-RPL算法能够有效地降低路由修复时延和减少控制开销。     

大跨度天基数据链的TDD时隙调度分析与优化

针对天基数据链系统卫星间的传播时延较大且节点间距离变化较大等特点，提出了一种提高时频资源利用率、提升系统容量、减小时延的方法。在时分多址与空分多址相结合的接入方式下，进行了时隙调度的分析与优化，利用卫星星座的空间复用与大跨度传播时延特性，采用时隙编排、收发状态调整、帧格式优化等手段，将信道利用率和系统容量提升了1倍，将卫星节点在某一方向获得服务的平均等待时间缩短为原来的1/4，提高了天基数据链系统的服务质量。     

“北斗”救生终端海上自适应功率退避算法

受海况、浸水、结冰、对星等因素影响，飞行员海上遇险后报位可靠性较低，工作时长较短。为此，提出一种自适应功率退避方法，在对报位影响因素分析基础上，利用深度神经网络，通过对经纬度、高度、俯仰角、方向、加速度、速度、浸水、信号、电量等多维度数据进行学习，拟合出覆冰、浪涌、液面等信号特征与环境特征联想模型，自适应控制信号发射时机，实现功耗的有效控制。试验表明，该方法将通信成功率及工作时长分别由36.3%、6.0 h提升至73.3%、8.6 h。     

微纳卫星组网星间链路设计及关键技术分析

微纳卫星组网应用可大幅度提高航天装备的能力，较单颗大卫星具有明显的优势。首先，介绍了国内外微纳卫星组网的研究情况，以组网侦察应用为例分析了星间测量与通信链路的技术需求，并进行了系统架构和体制设计；其次，在星座设计基础上开展系统仿真，并提出了一体化设备实现方案；最后，分析了微纳卫星组网系统中几项重要的关键技术及其解决方案，对系统的设计和工程实现具有参考价值。     

The role of fundamental motivations in willingness-to-pay online

This study aims to determine whether an understanding of chronic fundamental consumer motivations can help determine the mechanisms of willingness-to-pay for products online. To do so, it employs a simulated buying task on a fictional e-commerce site for a consumer product (branded either as a “new arrival” or a “classic”) to investigate the effects of two fundamental motivations (mate acquisition vs. self-protection) on willingness-to-pay for the product online. The primary focus of the paper to investigate the capacity of mate acquisition and self-protection motives to moderate the relationship between attitude toward the product and willingness-to-pay, as well as, the effects of the motives on willingness-to-pay are considered. Through regression and interaction effect analyses, it is shown that chronic fundamental motivation for mate acquisition is directly correlated with an increased willingness-to-pay for both product types, and it moderates the relationship between attitude toward a product and willingness-to-pay. Self-protection motivation increases willingness-to-pay for classic products but not new arrivals. By offering a rare look at chronic fundamental motivation in the consumer context and potentially being the first investigation of the moderating effects of fundamental motivations, the results mostly support the notion of predictable motivation induced behavioral tendencies.     

航班计划优化研究概述

从航班计划优化的不同时间阶段分析,可以将航班计划优化分为航班计划静态编排优化、基于航班延误预测的航班计划动态反馈优化和基于机场协同决策(A-CDM)的航班计划动态调整角度三类;进而从航班时刻、机型指派、航班频率等编制环节分析了航班计划静态编排优化;随后利用延误波及预测与数据挖掘预测的优化方法分析了基于航班延误预测的航班计划动态反馈优化的相关研究。最后,根据航班计划优化复杂性分析,给出了航班计划优化的发展趋势和未来研究方向。     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.