Seasonal precipitation in the south-central Helan Mountain region, China, reconstructed from tree-ring width for the past 224 years

IEECAS SKLLQG

Methanol synthesis from CO2 using a silicone rubber/ceramic composite membrane reactor

A one-dimensional isothermal pseudo-homogeneous parallel flow model was developed for the methanol synthesis from CO2 in a silicone rubber/ceramic composite membrane reactor. The fourth-order Runge-Kutta method was adopted to simulate the process behaviors in the membrane reactor. How those parameters affect the reaction behaviors in the membrane reactor, such as Damkohler number Da, pressure ratio p(r), reaction temperature T, membrane separation factor alpha, membrane permeation parameter phi , as well as the non-uniform parameter of membrane permeation L-1, were discussed in detail. Parts of the theoretical results were tested and verified; the experimental results showed that the conversion of the main reaction in the membrane reactor increased by 22% against traditional fixed bed reactor, and the optimal non-uniform parameter of membrane permeation rate, L-1.opt ,does exist. (C) 2003 Elsevier B.V All rights reserved.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.