基于时间序列差分特性的LMD端点效应处理方法

局部均值分解方法在计算过程中会使信号在端部产生端点效应,为减弱LMD中存在的端点效应,提出了一种基于信号时间序列差分特性的端点延拓方法。将信号曲线看作一段离散函数,利用该函数段末端的一阶差分和二阶差分的符号判断曲线的特征和发展趋势,根据具体情况判定所延拓极值点的值。使用该方法对仿真信号和实验轴承外圈故障和内圈故障信号进行了分解,并将其结果与镜像延拓改进的LMD方法分解的结果进行了对比。该方法在分解中的良好表现验证了其在信号分析中的有效性。

GPS手持机时间序列差分技术研究

针对大部分GPS手持机处理程序进行相位差分比较困难的问题,提出基于时间序列的位置差分方法提高手持GPS接收机定位精度,并利用天宝手持机Geo Explorer系列中的Geo XT手持机,对基于时间序列的位置差分方法进行了实验验证,实验结果表明,在观测条件良好情况下,经过基于时间序列的位置差分方法的后处理可以使Geo XT手持机定位精度达到亚米级。

基于高分辨率SAR影像的上海临港新城沉降格局分析

上海临港新城是通过围垦造地工程建设而成,特殊的地质条件和地理位置,使其地质环境变化备受关注。而建立和完善该新城的水准测量网需要一定时间,且基于点的水准监测难以获得区域沉降情况。时间序列InSAR方法可通过空间信息实施大范围监测,对于此类海塘新区的地面沉降监测具有独特优势。本文处理和分析了11景TerraSAR-X影像,并与之前的研究成果进行分析。实验结果表明,临港新城的沉降情况与围垦成陆的建设施工时序密切相关,九四塘以西的老冲填土区已趋于稳定,并有回弹趋势;九四塘以东的新冲填土区形变量较大,沉降速率近35mm/a。利用14个水准点的监测数据进行验证,其平均误差为1.510mm、中误差为2.016,说明该方法可以满足城市形变监测的需求。

单整序列数据的差分性质

一般认为(韩德瑞、秦朵,1998),时间序列的差分实质上是剔除时序当中某种固有的规律性,因此经数次差分后的时序主要含随机性的独立误差信息,自然趋向于正态分布。这与我们通常假设衍生的误差服从正态分布是一样的道理。但是,差分剔除掉的这种“固有的规律性”究竟是什么?所谓“经过数次差分”的“数次”究竟是多少次?为什么?本文对此进行探讨,以求抛砖引玉。

义能矿区开采沉陷演化的TS-DInSAR监测与影响分析

为研究济宁市义能矿区开采沉陷演化特点及其对周边建筑物的影响,本文采用69景长时间序列中等分辨率Sentinel-1影像,以TS-DInSAR技术体系中的小基线集DInSAR分析为研究方法,获取了2016年初至2018年底该矿区开采面周边的沉降信息。结果表明,义能矿区在监测时段内沉陷范围较为集中,最大累计沉降量约为627 mm,通过居民点位置与沉陷结果的叠加分析,发现周边3处村庄受到了不同程度的影响,该结果能为煤矿开采沉陷控制方案的效果评估及设计优化提供重要参考作用。

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Anomaly Detection in MODIS Land Products via Time Series Analysis

With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated.

An Empirical Study on the stock Price of Modeling

the model in time series analysis are widely used in the field of economy. We often use the model in time series to analyze data, but without regard to the rationality of the model. In this paper, we introduce and analyze Ping An Of China（601318） shares at the opening price（2013/01/04-2013/07/04）.The model is established by analyzing data. Modeling steps of ARIMA model and GARCH model are presented in this paper. The data whether ARIMA model is suitable by white noise. Or the data whether GARCH model is suitable by since the correlation of variance test. By comparing the analysis, it selects a more reasonable model.

Numerical analysis of wave hazards in a harbor

Resonance may occur when the periods of incoming waves are close to the eigen-periods of harbor basin.The amplified waves by resonance in harbor will induce serious wave hazards to harbor structures and vehicles in it.Through traditional theoretical approaches,the eigen-periods of harbor basin with regular shapes can be obtained.In our study,we proposed a numerical model to simulate the behavior characteristics of the harbor waves.A finite difference numerical model based on the shallow water equations(SWE) is developed to simulate incoming tsunami and tidal waves.By analyzing the time series data of water surface wave amplitude variations at selected synthetic observation locations,we estimate the wave height and arrival time in coastal area.Furthermore,we use frequency spectrum analysis to investigate the natural frequencies from the data recorded at the synthetic observation stations.