Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka.     

Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances.     

非线性和非高斯性共存的序批次反应处理过程故障诊断

序批式反应器(SBR)的处理过程的数据具有非高斯分布和高度非线性的特点,传统特征提取方法在进行特征提取时仅仅考虑信息最大化而忽略数据的簇结构信息导致数据特征提取的不完整.由于多向核熵成分分析是一种新的监测方法,在监测过程中的应用表明能够克服传统监测方法的缺陷,减少误报警率.因此本文结合多向核熵成分分析的的优势,提出多向核熵独立成分分析方法用于SBR过程监测及故障诊断.首先,将三维SBR过程数据利用一种新的数据展开技术变为二维数据;其次,利用核熵成分分析将展开后的二维数据映射到高维空间用独立成分分析进行独立成分提取;最后提出一种基于多向核熵独立成分分析的故障诊断方法进行故障诊断.将该方法和传统方法应用于80升的SBR处理过程的监测结果表明,本文提出的方法优于传统的多向独立成分分析方法.     

一种融合遥感和地面观测资料的雪深空间插值方法

针对积雪观测站点稀少的问题,提出一种考虑海拔影响,能够融合MODIS积雪面积产品和站点观测的雪深空间插值方法,该方法利用去云后MODIS积雪面积产品构建的无积雪“虚拟站点”弥补站点分布不均匀和稀少的不足,利用泛协克里金插值方法考虑海拔对雪深的影响。利用北疆地区50个气象站点的逐日雪深观测资料、逐日MODIS积雪面积产品和AMSR-E被动微波雪水当量和雪深产品,对普通克里金、泛克里金、普通协克里金和泛协克里金插值结果进行了比较研究。研究结果表明:积雪覆盖范围较大时,站点雪深与海拔之间相关系数较大,利用泛协克里金插值结果精度高且稳定;否则利用普通克里金插值精度较高且稳定。通过增加“虚拟站点”,能够提高雪深插值精度,并在一定程度上修正了克里金插值中存在的平滑效应。
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Assessment of water quality variation of a monitoring network using exploratory factor analysis and empirical orthogonal function

This study suggests a systematic assessment method that jointly uses the exploratory factor analysis (EFA) and empirical orthogonal function (EOF-patterns) of Principal Component Analysis (PCA) to assess the water quality variation of the monitoring network of Nakdong River, Korea, in which 28 stations measuring 15 water quality parameters are located. The EFA results showed the monitoring stations to be distinguished by two main factors. The representative stations of which the variance was almost explained by the specific factor were selected. We applied PCA to the monitoring data of representative stations, and then analyzed the EOF-patterns that indicate the characteristics of water-quality variation for each factor. With the interpretation of main factors and EOF-patterns causing dominant water quality variations, the monitoring network of Nakdong River could be spatially and seasonally evaluated according to the contribution of each factor.     

基于稀疏主元分析的过程监控研究

主元分析（principal component analysis）是一种多元统计技术,在过程监控和故障诊断中具有广泛的应用。针对过程监控中数据量大的特点,提出一种稀疏主元分析（sparse principal component analysis）方法,通过引入lasso约束函数,构建稀疏主元分析的框架,将PCA降维问题转化为回归最优化问题,从而求解得到稀疏化的主元,并提高了主元模型的抗干扰能力。由于稀疏后主元相关的数据量减少,利用数据建立过程监控模型,减少了计算量,并缩短了计算时间,进而提高了监控的实时性。利用田纳西伊斯特曼过程（TE processes）进行实验仿真,并与传统的主元分析方法进行对比研究。结果表明,新提出的稀疏主元分析方法在计算效率和监控实时性上均优于传统的主元分析方法。     

基于二维主元分析的间歇过程故障诊断

传统的多向主元分析(MPCA)已广泛应用于监视多变量间歇过程。在MPCA算法中,三维的间歇过程数据需要转换为高维的二维向量,导致计算量和存储空间大,同时不可避免地丢失一些重要信息。因此,提出一种新的基于二维主元分析(2DPCA)的故障诊断方法。由于每个批次的间歇过程数据是一个二维向量(矩阵),应用以各个批次矩阵为分析对象的2DPCA算法,避免矢量化,存储空间和存储需求小;另外,2DPCA采用各个批次的协方差的平均值来进行建模,能够更加准确地反映出不同类型的故障,在一定程度上增强了故障诊断的准确性。半导体工业实例的监视结果说明,2DPCA方法优于MPCA。     

Network optimization algorithms and scenarios in the context of automatic mapping

Many different algorithms can be used to optimize the design of spatial measurement networks. For the spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria to evaluate and compare algorithms. In many practical situations networks are not designed from scratch but instead the objective is to modify an existing network. The goal then is to add new measuring stations optimally or to withdraw existing stations with as little damage done as possible. The objective of this work is to compare the performance of different optimization algorithms for both computation time and accuracy criteria. We describe four algorithms and apply these to three datasets. In all scenarios the mean universal kriging variance (MUKV) is taken as the interpolation accuracy measure. Results show that greedy algorithms that minimize the information entropy perform best, both in computing time and optimality criterion.     

基于地理加权回归克里金的中国PM_(2.5)浓度空间制图方法北大核心CSCD

空气细颗粒物健康暴露风险等研究需要准确的PM_(2.5)浓度时空分布信息作为健康评估的重要输入。然而,由于监测台站稀疏分布,通常需要融合遥感等辅助信息,通过空间制图模型得到PM_(2.5)浓度的分布状况。如何在估计模型中将PM_(2.5)浓度的空间分布特征融入制图模型将是提高PM_(2.5)制图精度的关键。发展了一种融合地理加权回归和克里金插值方法的混合模型:地理加权回归克里金(Geographically Weighted Regression-Kriging,GWRK),地理加权回归模型考虑PM_(2.5)浓度分布的空间异质性,克里金模型对回归后的残差中存在的空间自相关性进行建模。基于该方法,利用中国空气质量监测站数据,采用遥感、模式模拟数据作为辅助信息,对2017年中国逐月的PM_(2.5)浓度分布进行估计空间制图。交叉验证结果表明,GWRK相较于传统制图方法(最小二乘回归、地理加权回归、回归克里金)具有更高的精度,决定系数R2为0.824,平均绝对误差为6.96μg/m3,均方根误差为10.94μg/m3。2017年逐月的PM_(2.5)浓度制图结果显示,在时间上,冬季是PM_(2.5)污染最严重的时段,夏季最轻,空间上,东部经济较为发达的城市如长三角地区是污染严重区,西南地区污染程度较轻。     

Adaptive local kriging to retrieve slant-range surface motion maps of the Wenchuan earthquake

Kriging is a widely used technique for raster data interpolation from point samples, such as in the generation of digital elevation models and geochemical maps. The quality of the result depends on both spatial distribution of the sampled values and nature of the semivariogram model, which fits an empirical global function to the sample data set to predict values at the unknown locations. However, such a semivariogram model may not be suitable for data sets with complex local trends in spatial distribution, such as those observed in differential interferometric synthetic aperture radar (DInSAR) data of the Wenchuan earthquake. Here we propose a modified kriging method, adaptive local kriging (ALK), for the retrieval of data lost through decoherence in Advanced Land Observing Satellite (ALOS) phased array L-band synthetic aperture radar (PALSAR) DInSAR data, within the intensely deformed fault zone of the 2008 Wenchuan earthquake. In ALK, a series of dynamic linear local semivariogram models is used rather than a global semivariogram for the whole data set. The localized adaptive approach ensures accurate interpolation in the areas of good DInSAR data with small decoherence gaps and avoids drastic errors in the extensive decoherence gaps; the overall value prediction is thus significantly improved, as confirmed by comparison with the original DInSAR data and fidelity verification experiments.     

基于先验信息的残余克里金法的研究

论文在可视化插值过程中所遇到的精确数据点少,而先验信息比较丰富的情况下,以传统克里金插值法原理为基础,提出了基于先验信息的残余克里金插值法。该插值方法是一种精确的内插方法,在精确数据点密集的区域,插值的结果主要反映精确数据的变化,而在精确数据点少,先验数据多的区域,插值的结果反映的是先验数据的变化趋势。两个应用实例的结果验证了这一方法的特点。     

Temperature sensor signal reconstruction for failure detection of vapor compression system

Large effort has been made to fill up databases of failures and diagnosis for all types of technologies and they will remain open lists as systems are dynamically improving. This paper’s purpose is to provide support in decision making, quantification and diagnosis of failures in temperature sensor signal for a vapor compression system. The main challenge regarding this topic has been to distinguish and adapt methods to suit vapor compression systems. The temperature sensor signal failure was experimentally induced to a vapor compression system set-up, failure consequences were analyzed and three detection methods were evaluated: Principle Component Analysis, Fuzzy- Principle Component Analysis and Complex Fuzzy- Principle Component Analysis. All these methods are sensor reconstruction models, trained by the non-failure measured data to build the expected signal. Since faultless measured data are not always available, the possibility to use polynomial extrapolated data (as provided by manufacturer datasheets) is evaluated. The three selected methods showed to be suitable for similar heating, ventilation and air conditioning systems.     

基于EDA算法的改进KPCA的某型测角仪的状态监测与故障预测研究

基于电子系统状态监测为研究背景,传统的Kernel Principal Component Analysis(核主成份分析法,简称KPCA)在状态监测过程中做数据特征降维处理,使得电路状态数据在消除冗余信息的同时,也能在相应的模型算法计算中很大程度的减少计算步骤,但是KPCA法的降维数据处理过程对数据样本贡献率的识别能力有不足之处,虽然达到了降维的目的,但是对特征样本数据的信息保留能力存在不足。本文中采用经验模态分解法(Empirical Mode Decomposition,简称 EMD)对输出信号进行采集处理作为样本数据,设计基于 Fisher准则的状态信息识别能力分析,采用 Estimation of Distribution Algorithms(种群算法,简称 EDA)对KPCA分析法进行改进研究,通过对数据处理,最大限度的保留状态主信息,使得在电路系统状态监测过程中减小实验误差,为后续故障预测打下基础。     

基于动态核独立分量分析的高含硫天然气净化过程异常检测与诊断

目前高含硫天然气净化过程存在多参数动态相关的特性,导致基于静态多元统计过程监控方法对于异常状态检测效果较差。提出一种考虑参数时序自相关性的动态核独立分量分析(DKICA)异常检测与诊断方法。首先,引入自回归(AR)模型,通过参数辨识确定模型阶次,描述监控过程的时序自相关性;然后,将原始变量投影到核独立元空间,通过监控独立元对应的T²和SPE统计量是否超出正常状态设定的控制限,实现异常检测;最后计算所述T²统计量对原始变量的一阶偏导数,绘制贡献图实现异常诊断。以某高含硫天然气净化厂采集的数据进行分析,结果表明基于DKICA高含硫天然气净化过程异常检测精度要优于静态独立分量分析所得的检测精度。     

一种增量PCA算法及其在人脸识别中的应用

主成分分析（PCA）是模式识别领域一种重要的方法,现在已被广泛地应用于人脸识别算法中,但基于PCA人脸识别系统在应用中面临着一个重要障碍：增量学习问题。针对这个问题,提出了一种适用于成批增量数据的IPCA算法,该算法在原始PCA分解的基础上,利用空间投影变换,使得可以在一个低维空间求解整体PCA,从而降低了求解的复杂度,在此基础上对该增量算法进行了核化,并在ORL人脸数据库上验证了算法的有效性。     

鲁棒主元分析在掌纹识别中的应用

为了对存在异常值的图像构建低维线性子空间的描述,提出用鲁棒主元分析(RPCA)的新方法进行掌纹识别。运用图像下抽样方法降低掌纹空间的维数,在低维图像上应用RPCA提取低维的投影向量,然后将训练图像和待识别图像向投影向量上投影得到鲁棒主元特征,计算特征向量间的余弦距离进行掌纹匹配。运用PolyU掌纹图像库进行测试,结果表明,与主元分析(PCA)、独立元分析(ICA)和核主元分析(KPCA)相比,RPCA算法的识别率最高为99%,特征提取和匹配总时间0.032 s,满足了实时系统的要求。     

Comparaison de differentes methodes de traitement par analyses en composantes principales des donnees Thematic Mapper de LANDSAT-5 appliquees al'inventaire forets mediterranneennes du sud de la France

Abstract

The existence of remotely sensed data with high spatial resolution, like the ones produced by the Thematic Mapper of LANDSAT, set the problem of the reduction of the number of spectral dimensions to be analysed. The work presented hereby is in this context and aims to compare the performances of the supervized classification applied to the perception of forested and sub-forested mediterranean ecosystems processed with: original TM data selected only under spectral consideration; components factors provided by a classical Principal Component Analysis (PCA); components factors provided by a selective Principal Component Analysis (PCA). The analysis of the results shows that the classification done using the three axes extracted by a classical Principal Component Analysis of the six Thematic Mapper bands gives better results than all the other combinations (original data or data provided by selective PCA). On the other hand, and for all the classifications processed, it appears that the performances are excellent (near 90 per cent) for units representing stable systems, climatic or definitely degraded. At the opposite end, performances are quite good (near 60 per cent) for the evolutive systems (in regression or progression).     

融合多源信息的地表气温插值研究

为了提高地表气温的插值精度,提出了融合多源信息的地表气温插值方法,该方法以地表气温和辅助信息之间显著相关为前提条件,利用多元地统计（拟协同克里金、基于局部变化均值的简单克里金、带外部漂移的克里金）来实现多源信息的融合。对中国720多个气象站2008年8月的月平均地表气温进行了空间插值实验,实验结果表明,综合考虑两种辅助信息的SKlm和KED插值方法最优,其原因在于：1）地表气温和海拔及地表温度显著相关,海拔反映地表气温的总体趋势,而地表温度更侧重反映它的局部趋势,综合考虑它们能更准确地预测地表气温。2）SKlm和KED均是基于非二阶平稳的插值方法,而地表气温的空间分布往往呈非平稳性,因此它们要优于其他方法。     

基于IEC 61850的能源站智能电气监控管理方法

针对传统电气监控管理系统信息管理能力差的问题,提出了新型的能源站智能电气监控管理方法。该方法建立在DL/T 860（IEC 61850）标准规范的基础上,采用微机应用交流采样算法对能源站电气设备运行数据进行处理,并将该算法进行系统集成优化设计,构建出全智能自动化的电气监控管理系统。本研究系统加强了各监控设备的联系,系统误差数据低。实验表明,本研究系统的监测数据误差低于10%,解决了传统电气监控误差较大的问题。     

降水时空关联特征可视分析

目的 降水是影响全球气候变化和系统环境的重要因素,面向降水数据开展时空关联分析,对于区域气候特征探索及异常情况监测具有重要的意义。然而,降水时空关联特征的分析是一个复杂且耗时的过程,与气象站点的空间分布以及降水的时间序列密切相关。本文综合考虑降水的时空变化特征,研究和设计面向降水数据时空关联特征分析的可视化系统工具。方法 利用地图和矩阵图呈现降水数据的空间分布和周期变化特征,设计径向盒须图对降水数据的时空变化异常特征进行捕获;通过局部Moran''s I指数的计算和热力图的呈现表达降水的空间相关性,支持用户交互式地探索空间相关性的时序变化特征;利用普通克里金插值模型获得降水空间插值图,并对插值结果的准确性进行可视化评估。结果 以中国安徽省1971-2014年气象观测站长时间序列月降水数据集为例进行分析,实验结果证明本研究可视化交互系统能够直观高效地探索区域降水长时间序列时空变化特征和极端降水情况;有效探究区域降水空间分布模式、不同站点降水信息间空间依赖性和异质性,并快速发现降水奇异点;分析区域不同时间尺度降水气候特征空间变化。结论 系统工具集成便捷的交互模式,支持用户探索式地分析降水数据的时空关联特征,进而有效地探究区域气候变化规律和特征分布关系。基于真实降水数据的实验结果以及降水领域专家的反馈,进一步验证了本文系统工具的有效性和实用性。