CART软件计算的红外大气透过率和实测值比较

初步完成了一套通用大气辐射传输软件的研制,它是一套基于我国大气模式的采用独特算法的大气辐射传输软件,可用来较快速地计算大气光谱透过率、大气热辐射、大气散射辐射和太阳直接辐照度,光谱分辨率为1cm-1。利用中近红外傅里叶光谱辐射计FTIR测得的水平红外大气透过率来验证CART软件计算水平路径上红外大气透过率的精度。比较结果表明：CART软件计算的水平大气透过率和实测值的误差最大小于0.1,均方根大约为0.04。这可以说明CART软件计算水平红外大气透过率是可靠的。     

Evaluation of statistical models for predicting Escherichia coli particle attachment in fluvial systems

Modeling surface water Escherichia coli fate and transport requires partitioning E. coli into particle-attached and unattached fractions. Attachment is often assumed to be a constant fraction or is estimated using simple linear models. The objectives of this study were to: (i) develop statistical models for predicting E. coli attachment and virulence marker presence in fluvial systems, and (ii) relate E. coli attachment to a variety of environmental parameters. Stream water samples (n = 60) were collected at four locations in a rural, mixed-use watershed between June and October 2012, with four storm events (>20 mm rainfall) being captured. The percentage of E. coli attached to particles (>5 μm) and the occurrences of virulence markers were modeled using water quality, particle concentration, particle size distribution, hydrology and land use factors as explanatory variables. Three types of statistical models appropriate for highly collinear, multidimensional data were compared: least angle shrinkage and selection operator (LASSO), classification and regression trees using the general, unbiased, interaction detection and estimation (GUIDE) algorithm, and multivariate adaptive regression splines (MARS). All models showed that E. coli particle attachment and the presence of E. coli virulence markers in the attached and unattached states were influenced by a combination of water quality, hydrology, land-use and particle properties. Model performance statistics indicate that MARS models outperform LASSO and GUIDE models for predicting E. coli particle attachment and virulence marker occurrence. Validating the MARS modeling approach in multiple watersheds may allow for the development of a parameterizing model to be included in watershed simulation models.     

Tree-structured smooth transition regression models

This paper introduces a tree-based model that combines aspects of classification and regression trees (CART) and smooth transition regression (STR). The model is called the STR-tree. The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure. The resulting model can be analyzed as a smooth transition regression with multiple regimes. Decisions about splits are entirely based on a sequence of Lagrange multiplier (LM) tests of hypotheses. An alternative specification strategy based on a 10-fold cross-validation is also discussed and a Monte Carlo experiment is carried out to evaluate the performance of the proposed methodology in comparison with standard techniques. The STR-tree model outperforms CART when the correct selection of the architecture of simulated trees is discussed. Furthermore, the LM test seems to be a promising alternative to 10-fold cross-validation. Function approximation is also analyzed. When put into proof with real and simulated data sets, the STR-tree model has a superior predictive ability than CART.     

Learning in the feed-forward random neural network: A critical review

The Random Neural Network (RNN) has received, since its inception in 1989, considerable attention and has been successfully used in a number of applications. In this critical review paper we focus on the feed-forward RNN model and its ability to solve classification problems. In particular, we paid special attention to the RNN literature related with learning algorithms that discover the RNN interconnection weights, suggested other potential algorithms that can be used to find the RNN interconnection weights, and compared the RNN model with other neural-network based and non-neural network based classifier models. In review, the extensive literature review and experimentation with the RNN feed-forward model provided us with the necessary guidance to introduce six critical review comments that identify some gaps in the RNN’s related literature and suggest directions for future research.     

Implementation of a COM-based decision-tree model with VBA in ArcGIS

The problem of soil pollution by heavy metals has been receiving an increasing attention in the last few decades. Geostatistics module in ArcGIS, could not however efficiently simulate the spatial distribution of heavy metals with satisfied accuracy when the spatial autocorrelation of the study area severely destroyed by human activities. In this study, the classification and regression tree (CART) has been integrated into ArcGIS using ArcObjects and Visual Basic for Application (VBA) to predict the spatial distribution of soil heavy metals contents in the area severely polluted. The overall CART accuracy of assigning samples to the right Pb classes is 89.62% and 85.71%, the Kappa coefficient is 0.8444 and 0.7575, respectively for learning data and test data. This is a great improvement comparing with ordinary Kriging method in ArcGIS. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil heavy metals pollution. The methods and results described in this study are also valuable for understanding the relationship between heavy metals pollution risk and environmental factors.     

Bootstrapping to Assess and Improve Atmospheric Prediction Models

Bootstrapping is a simple technique typically used to assess accuracy of estimates of model parameters by using simple plug-in principles and replacing sometimes unwieldy theory by computer simulation. Common uses include variance estimation and confidence interval construction of model parameters. It also provides a way to estimate prediction accuracy of continuous and class-valued outcomes regression models. In this paper we will overview some of these applications of the bootstrap focusing on bootstrap estimates of prediction error, and also explore how the bootstrap can be used to improve prediction accuracy of unstable models like tree-structured classifiers through aggregation. The improvements can typically be attributed to variance reduction in the classical regression setting and more generally a smoothing of decision boundaries for the classification setting. These advancements have important implications in the way that atmospheric prediction models can be improved, and illustrations of this will be shown. For class-valued outcomes, an interesting graphic known as the CAT scan can be constructed to help understand the aggregated decision boundary. This will be illustrated using simulated data.     

一种基于Straight的语音焦点合成方法

针对汉语焦点的特性,设计了接近自然语流风格的实验语料。通过对语料的分析,运用CART技术,建立了焦点的韵律模型。在语音合成阶段,使用韵律模型生成语音的韵律参数,结合Straight算法,实现了语音焦点的合成。对合成效果的评测表明,该方法能够合成自然度很高的语音焦点。     

Feature-based and Clique-based User Models for Movie Selection: A Comparative Study

The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an ‘information filtering agent’ which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available. This revised version was published online in July 2006 with corrections to the Cover Date.