Evolution of airports from a network perspective——An analytical concept

Analyzing airports' role in global air transportation and monitoring their development over time provides an additional perspective on the dynamics of network evolution. In order to understand the different roles airports can play in the network an integrated and multi-dimensional approach is needed. Therefore, an approach to airport classification through hierarchi-cal clustering considering several parameters from network theory is presented in this paper. By applying a 29 year record of global flight data and calculating the conditional transition probabil-ities the results are displayed as an evolution graph similar to a discrete-time Markov chain. With this analytical concept the meaning of airports is analyzed from a network perspective and a new airport taxonomy is established. The presented methodology allows tracking the development of airports from certain categories into others over time. Results show that airports of equal classes run through similar stages of development with a limited number of alternatives, indicating clear evolutionary patterns. Apart from giving an overview of the results the paper illustrates the exact data-driven approach and suggests an evaluation scheme. The methodology can help the public and industry sector to make informed strategy decisions when it comes to air transportation infras-tructure.     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

计算机网络安全及安全审计技术研究

通过对计算机安全和安全审计问题的简要分析，并结合网络系统的特点，力图较系统深入地探讨计算机网络安全及其审计技术；在此基础上，提出了一个网络安全审计模型。     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T) has changed the way that network services are developed, deployed, and operated. Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and t...     

网络分析仪法测量稠油介电常数

用网络分析仪在Ｘ波段８～１１．５ＧＨｚ的６个频率点上,对大港、胜利、辽河油田稠油的介电常数进行测量,测量结果可以为微波采油工艺中的井下轴射器的设计及微波能利用率的估算提供理论依据,并对测量结果进行了误差分析。     

航天器热网络模型及其系数修正方法

叙述了目前航天器热网络模型与航天器实际换热之间的偏差,提出了利用航天器稳态热平衡试验结果进行热网络模型及其系数修正的方法。通过对某仪器舱热平衡试验数据的处理,实现了对该仪器舱热网络模型及其系数的修正,并对修正的结果进行了讨论。     

网络远程教育的优势与反思

网络教育是现代信息技术条件下的一种新的教育形式,网络教育具有双向互动、内容丰富生动、可进行个性化远程教育等优势。在我国不少网络课程已日渐成熟,但网络课程的教学目标和课程型态,网络课程与教学平台的功能,网络课程的评价机制,网络教育的定位等方面尚有一些问题需要认真研究解决。网上课堂最终会形成比传统课堂更好的教学环境,但目前只是对传统课堂教学的补充。     

基于效用价值驱动的旅客出行动力学研究与建模

为了探究交通体系中旅客出行行为的特征与机理,以某机场离港旅客的陆侧出行数据为研究对象,对旅客出行模式进行了统计。分析发现,旅客聚集行为具有强烈的阵发性,不同出行维度旅客群体的聚集时间间隔分布具有幂律特征,且幂指数和阵发性强度呈正相关关系。同时,研究认为旅客出行换乘决策是综合时间、费用、便捷舒适度、突发情况等多因素的期望实现,是一种基于效用价值驱动的人类行为。基于此构建了效用价值驱动模式下的旅客出行行为动力学模型,可输出幂指数为参数可调的幂律分布,且仿真输出和实证数据分析结果相吻合。      

基于VxWorks的无人机模拟器设计与实现CSCD

无人机模拟器通过模拟与地面发控设备的电气接口及逻辑关系和无人机空中飞行全过程实现了对无人机的模拟,是一套用于飞行协同训练及地面发控设备检查的地面辅助设备。该设备采用基于CPCI总线结构、Vx Works操作系统的计算机,实现了串口通信、UDP网络通信、图形人机交互界面等功能。