大滞后温度智能控制系统的研究与实现

本文研究的是以MCS-51系列单片机为核心的计算机控制系统,用以实现化工催化剂活性检测过程床层温度程序控制。被控对象是具有3～8分钟的纯滞后,时变,非线性特性的催化剂电加热炉。控制难度大。 采用了广义最小方差自校正控制算法,具有时变滞后的极点自校正PID算法,极点对消直接PID三种算法进行了仿真与现场试验研究。前两种,由于工作过程允许的信号对系统激励不够充分,对象在线辩识有困难,试验结果表现,不能实现预期的控制要求。最终采用了极点对消直接PID算法,成功地实现了催化剂活性温度自动控制。该系统工作稳定、可靠,控制精度达到了检测标准要求。     

延误航班停机坪作业运行控制研究

随着航班航次的增加，机场变得越来越繁忙．地面作业操作不当所导致的飞机地面事故日益增加：在介绍航班停机坪作业的基础上，分析了地面作业的特点，在此基础上，根据航班延误时在作业资源充足和不足情况下，研究了航班停机坪作业的一般方法。在资源充足情况下，航班延误的地面作业控制主要采用AOE方法，缩短关键路线的作业时间。而资源不足情况下，除了控制关键路线作业时间外，还要根据排序算法合理分配关键作业的资源，以实现缩短延误时间的目的。     

45°(N)纬度带的Klobuchar like电离层延迟季节修正模型与评估

电离层延迟是全球卫星导航系统（GNSS）的主要误差源之一。对于装配GNSS单频接收机的航空器,选择简单有效的Klobuchar广播电离层模型来改正电离层延迟误差,其修正率为50%～60%。针对45°（N）纬度带,提出了更高电离层修正需求。考虑到季节因素对中高纬度地区电离层的显著影响,利用GIMs（Global Ionospheric Maps）分析了昼夜中TEC（Total Electron Content）的峰值和谷值随季节（年积日）的变化,建立了一种适用于45°（N）纬度带的Klobuchar like电离层模型。该模型不增加广播模型系数,新模型的夜间和VTEC高峰时电离层修正率分别达到了82%和80%,表明在穿刺点集中的45°(N)纬度地区使用该模型可以更精确地描述该地区的电离层,帮助航空器实现更高精度的定位。     

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.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.     

Maximal admissible mode decision delay in terminal guidance

Mode decision-maker is a critical component in the logic-based Integrated Estimation and Guidance(IEG) system. For the best possible estimation and guidance performance, the mode decision delay of the mode decision-maker should be limited to a range as small as possible. This paper presents a numerical method for computing the maximal admissible mode decision delay that varies with time-to-go. Particular attention has been paid to highly maneuvering target interception in terminal guidance. The results of this research offer useful guidelines for the design of the mode decision-maker in IEG systems.     

Statistical study of the time delay of ionospheric TEC storms to geomagnetic storms in Taoyuan,Taiwan

We have studied the time delay of ionospheric storms to geomagnetic storms at a low latitude station Taoyuan (25.02°N, 121.21°E), Taiwan using the Dst and TEC data during 126 geomagnetic storms from the year 2002 to 2014. In addition to the known local time dependence of the time delay, the statistics show that the time delay has significant seasonal characteristics, which can be explained within the framework of the seasonal characteristics of the ionospheric TEC. The data also show that there is no correlation between the time delay and the intensity of magnetic storms. As for the solar activity dependence of the time delay, the results show that there is no relationship between the time delay of positive storms and the solar activity, whereas the time delay of negative storms has weakly negative dependence on the solar activity, with correlation coefficient −0.41. Especially, there are two kinds of extreme events: pre-storm response events and long-time delay events. All of the pre-storm response events occurred during 15–20 LT, manifesting the Equator Ionospheric Anomaly (EIA) feature at Taoyuan. Moreover, the common features of the pre-storm response events suggest the storm sudden commencement (SSC) and weak geomagnetic disturbance before the main phase onset (MPO) of magnetic storms are two main possible causes of the pre-storm response events. By analyzing the geomagnetic indices during the events with long-time delay, we infer that this kind of events may not be caused by magnetic storms, and they might belong to ionospheric Q-disturbances.     

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。