一种新模糊似然函数在聚类分析中的应用

运用一种新的模糊似然函数，用以表示模糊集合之间的相似程度。举例说明在气象时间序列中的应用，并与常用的度量样本相似程度的指标进行对比，得出几点有意义的结论。     

Kalman filter estimation for periodic autoregressive-moving average models

An exact maximum likelihood procedure is presented for estimating the parameters of a periodic autogressive-moving average (PARMA) model. To develop an estimator which is both statistically and computationally efficient, the PARMA class of models is written using a state-space representation and a Kalman filtering algorithm is used to estimate the parameters. In order to demonstrate how to fit PARMA models in practice, the most appropriate types of PARMA models are identified for fitting to two average monthly riverflow time series and the new estimator is employed for estimating the model parameters.     

The loghyperbolic: An alternative to the lognormal for modeling oil field size distribution

The lognormal distribution is widely used to represent the distribution of deposit or reserve size of oil and gas fields. The purpose of this paper has been to investigate the potential usefulness of the loghyperbolic distribution as an alternative to the lognormal distribution. This hypothesis is tested using a set of data from the Denver basin. The results indicate that the loghyperbolic distribution shows a better fit to the empirical data than the lognormal distribution.     

A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models

Semi-parametric geographically weighted generalized linear models (S-GWGLMs) are a useful tool in modeling a regression relationship where the impact of certain explanatory variables on a non-Gaussian distributed response variable is global while that of others is spatially varying. In this article, we propose for S-GWGLMs a new estimation method, called two-stage geographically weighted maximum likelihood estimation, and further develop a likelihood ratio statistic-based bootstrap test to determine constant coefficients in the models. The performance of the estimation and test methods is then evaluated by simulations. The results show that the proposed estimation method performs as well as the existing method in estimating both constant and spatially varying coefficients but it is more efficient in terms of computation time; the bootstrap test is of accurate size under the null hypothesis and satisfactory power in identifying spatially varying coefficients. A real-world data set is finally analyzed to demonstrate the application of the proposed estimation and test methods.     

等维BP神经网络在沉降预报中的应用研究

为解决神经网络在沉降预报应用中的局限性,结合灰色理论等维信息策略和BP神经网络建模思想,利用数据序列本身构建训练样本,建立等维BP神经网络预报模型,并利用数学工具MATLAB编程实现,进行实际的变形预测,通过与灰色理论GM(1,1)模型的预报效果进行比较,表明该等维BP网络模型具有更高的预报精度,可以达到更好的预报效果。     

矩形沟埋涵洞顶部垂直土压力试验和理论研究

针对无黏性填土,通过模型试验和理论分析的方法研究矩形沟埋刚性涵洞顶部的垂直土压力。考虑胸腔土体的作用,建立了沟埋涵洞土压力的计算模型和计算公式,结果表明,计算值与试验值相吻合。沟槽宽度等于涵洞外径时,洞顶土压力系数随填土高度的增大呈单调减小的变化规律;沟槽宽度大于涵洞外径时,洞顶土压力系数随填土高度的增大呈先增后减的变化规律;洞顶填土厚度等于初始等沉面高度时,土压力系数最大。槽洞宽比加大时,土压力系数、等沉面高度按双曲线规律增大,7倍洞径的沟槽宽度近似为沟埋式涵洞和上埋式涵洞的临界槽宽。填土内摩角增大时,土压力系数增大。洞顶填土厚度增大,等沉面高度减小。     

MODES月预测产品在贵州的释用

基于符号一致率评分(Pc)法,对多模式解释应用集成预测系统(MODES)的6种模式产品开展贵州85站2要素月预测释用,根据评估结果进一步采用等权平均方案和最优方案对6种模式产品展开2012—2015年48个月的回算统计。分析表明:不同的模式产品的预报性能各不相同,其中ECMWF的模式产品的预测性能较NCC和NCEP来说相对较高;模式产品释用时,最优方案的预报评分均高于其它6个模式产品的Pc评分平均值,且最优方案的预报评分高于等权平均方法的预报评分;回算结果,无论是气温还是降水,最优方案的Pc评分均都高于省级发布的月预测产品评分,这说明利用最优方案可以有效地提高MODES对贵州月气温和降水预测能力。     

Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure

Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI_L·NDMI_H (the multiplication of NDVI_L by NDMI_H, L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers.     

Evaluation of multisource data for glacier terrain mapping: a neural net approach

Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings.     

雾霾天气下DAML空间相干光通信系统的研究

大气湍流引起的折射率起伏会导致光信号的振幅和相位发生随机波动,同时日益严重的城市雾霾现象带来的系统性能降低问题也不可忽视.基于湍流相位波动和激光器相位噪声的缓慢时变特性,本文提出了将DAML相位估计算法应用到空间相干光通信系统中来提高系统性能,并推导了在大气湍流、激光器相位噪声以及各种天气状况衰减效应影响下,针对DQPSK信号的DAML相位估计系统的误码率,最后重点分析了在雾、霾天气条件下该系统的传输距离限制和波长选择特性.仿真结果表明：湍流相位波动造成的信噪比损失低于0.1 dB;在雾和霾天气下,相较于传统的OOK IM／DD FSO通信系统,DAML相位估计系统在不同的大气湍流环境中至少有500 m的传输距离优势;在霾和薄雾天气,传输波长为1 550 nm的DAML系统比波长为850 nm的系统的性能更优,但在能见度低于200 m的浓雾天气,系统性能基本与波长选择无关.