An effective screening design for sensitivity analysis of large models

In 1991 Morris proposed an effective screening sensitivity measure to identify the few important factors in models with many factors. The method is based on computing for each input a number of incremental ratios, namely elementary effects, which are then averaged to assess the overall importance of the input. Despite its value, the method is still rarely used and instead local analyses varying one factor at a time around a baseline point are usually employed.In this piece of work we propose a revised version of the elementary effects method, improved in terms of both the definition of the measure and the sampling strategy. In the present form the method shares many of the positive qualities of the variance-based techniques, having the advantage of a lower computational cost, as demonstrated by the analytical examples.The method is employed to assess the sensitivity of a chemical reaction model for dimethylsulphide (DMS), a gas involved in climate change. Results of the sensitivity analysis open up the ground for model reconsideration: some model components may need a more thorough modelling effort while some others may need to be simplified.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

基于OCTEON芯片的高精度流回放系统的设计与实现

介绍网络多核处理器OCTEON和基于OCTEON开发的基本需求,讨论在Linux平台下基于该芯片开发高精度流回放系统的设计与实现过程,包括主机与OCTEON间PCI的通信,高精度流回放的时间戳实现,以及多个流同时回放的设计与实现。对该系统的性能测试表明,基于OCTEON的流回放实现高度并行并且高精度的处理,系统的实现对于在高速Gbps环境下的网络数据流检测、过滤与入侵检测系统具有很高的应用价值。     

Synthesis of ultrathin semicircle-shaped copper nanowires in ethanol solution for low haze flexible transparent conductors

Nano Research - Copper nanowires (CuNWs) are becoming an indispensable item for next-generation transparent optical devices due to their excellent conductivity and transparency. In this work,...     

基于IXP422平台的Redboot移植分析

Redboot是一个常用在嵌入式系统上的开机启动引导程序,由于不同的嵌入式平台在硬件上存在着很大的差异,因此需要对Redboot进行移植来满足不同的平台硬件要求。该文阐述了Redboot在IntelIXP422平台移植的一般方法及步骤,移植过程同样适用于IXP系列的其它平台。     

Numerical solutions of Duffing equations involving both integral and non-integral forcing terms

In this paper, an improved variational iteration method is presented for solving Duffing equations involving both integral and non-integral forcing terms. The main advantage of this modification over the standard variational iteration method (VIM) is that it can avoid unnecessary repeated computation in determining the unknown parameters in the initial solution. Numerical results reveal that the improved method is simple and efficient.     

A new approach to the prediction of passenger flow in a transit system

A non-linear model is proposed for predicting the rate of passenger flow in a transit system, and its chaotic characteristic is observed. Using wavelets analysis, the passenger flow data for a whole day are decomposed in a multi-scale way to obtain decomposition sequences. Subsequently, a neural network approach is used to predict the sequences. Finally the passenger flow value can be predicted when the predicted sequences are reconstructed. Results show that the present approach is a feasible method for passenger flow prediction.