利用LS_AR模型对极移参数的中长期预报

为了满足深空探测器自主导航定位对极移参数中长期预报的需求,阐述了基于LS_AR模型的极移参数中长期预报和精度评定的原理,提出了4种改进方案对LS_AR模型的构建进行优化,并利用IERS提供的1990～1996年的极移参数的时间序列检验4种优化方案,得到了最优的预报模型,在400 d跨度上,其预报结果的平均绝对误差比未优化的模型小3 mas左右。     

Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.     

基于遥感影像的土地动态变化监测

土地利用/覆盖变化是目前研究全球及区域环境的一个重要领域,在城镇化加速的今天,城镇的土地利用格局也发生了飞速的变化。本文通过其一研究区内的Landsat TM遥感影像进行处理,获取了2007~2016年10个时相土地利用/覆盖信息,通过不同的预测模型对监测到的数据进行处理及比较,根据相应的最优预测方法预测了2017~2019年南昌市各土地类型的数据,由此研究并探讨了南昌市土地利用/覆盖的时空格局变化。     

GIS支持下的油气多因素综合预测方法——以苏里格气田苏120区为例

苏里格气田为一致密沙岩气田,油气富集区的预测优选为气藏开发奠定基础。常规的油气富集区预测方法存在着多学科成果综合分析困难、预测过程复杂、效率低下等弊端,本文针对苏里格120区块开展了基于GIS的油气富集区多因素综合预测方法的研究。首先借助GIS的空间数据库,实现多学科成果图件的集成管理;然后基于空间定位,从多学科成果图件中提取预测多因子,构建油气多因素综合预测模型;再次,运用GIS强大的叠加分析方法进行多因素综合预测,自动生成油气富集区预测平面图。本文的研究,为油气富集区的预测提供了新的技术方法,有效地提高了预测效率。     

改进灰色马尔科夫模型在基坑预测中的研究

基坑预测问题关系到工程施工的安全,在施工过程中对基坑进行周密的监测和变性预测分析显得尤为重要。针对传统预测模型存在固有偏差和可靠性低的缺点,采用新陈代谢的原理对无偏灰色加权马尔科夫模型进行改进。该模型先用无偏灰色模型拟合系统的总体变化趋势,然后,对拟合残差进行马尔可夫状态划分,并根据各阶权重对不同步长的转移矩阵进行加权处理,用加权后的无偏灰色马尔科夫模型进行预测。在每一步的预测中,利用新陈代谢的原理不断更新建模所使用的数据。将该模型用于基坑沉降预测,并通过实例进行验证。实验表明:基于新陈代谢的无偏灰色加权马尔科夫模型提高了基坑沉降预测的精度和可靠性,预测精度与未改进模型相比提高了8.54%。     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

UKF算法及其在GPS卫星轨道短期预报中的应用

分析了GPS卫星预报星历,在比较分析EKF和UKF优缺点的基础上,将UKF引入GPS卫星轨道预报研究中。数值模拟和结果分析表明,UKF方法预报更稳定,能有效地提高轨道预报精度和稳定性。     

单中继星单低轨卫星的联合定轨精度分析

针对由单中继星和单低轨卫星组成的联合定轨系统,给出了系统内不同轨道卫星摄动项的选取方案和卫星间的可见性判别模型。在模拟出含有白噪声的四程测距观测数据文件的基础上,研究了测距精度和采样弧段对联合定轨中高、低轨卫星定轨精度的影响。得出如下结论:联合定轨更有利于对低轨卫星的轨道改进;同样的采样时间条件下,测距噪声越小定轨精度越高,并且采样时间越短它的影响越明显;同样的测距噪声条件下,所用资料的采样时间越长精度越高,但当测距精度很高时,TDRS达到最好定轨精度所需的采样时间相应有所缩短。并在定出轨道后进行了轨道预报,分析了轨道预报的趋势及精度。     

动态最小二乘支持向量机学习算法

针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法。该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率。大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点。     

网络安全态势感知关键实现技术研究

建立了网络安全态势感知的分层实现模型,并针对每个层次提出了基于多分类器融合的安全态势提取方法、基于统计学习的分层态势评估方法以及基于遗传神经网络态势的动态预测方法。经仿真实验验证,每个层次的实现方法都是可行有效的。