区间数多属性决策中一种带有可能度的排序方法

为解决具有不确定性区间数的多属性决策中的方案排序问题,在考虑落在区间内的评价值（即被认为随机变量）服从正态分布的情况下,针对方案综合评价值所在的区间存在相互交叉部分的情形,提出了关于区间数之间相互比较的可能度的概念,并结合给出了有可能度的方案排序方法。通过此方法可计算出一个方案优于另一个方案的可能度。     

基于快速模糊规则搜索的快速模糊系统建模

本文提出了一种基于快速模糊规则搜索的快速模糊系统建模方法,首先使用本文提出的快速模糊规则搜索方法进行模糊规则的粗略搜索,然后基于所得到的模糊规则集建立模糊神经网络模型,即利用规则前件确定网络结构,规则后件初始化部分网络权值。使用遗传算法对网络模型的权值和阈值进行学习训练。实验结果验证了快速模糊系统建模方法在描述较大规模非线形系统中的实用性和有效性。     

面向工程CAD的图形库设计

传统的图形库技术有两种：一是用户定制固定的图符;二是对图形分类,每一类图形对应一段程序,即图形库与程序库对应。文中在分析这两种图形库技术的基础上,提出了一种通用的图形库结构,该结构支持元件的参数化表示,支持元件的LOD表示,支持设计过程中元件全生命周期内的图形表述,并支持工程设计的项目管理等。     

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

Predicting high-risk students using Internet access logs

Predicting student performance (PSP) is of great use from an educational perspective, especially for high-risk students who need timely help to complete their studies. Previous PSP studies construct prediction models mainly on data collected from questionnaires or some specific learning systems. Instead, students’ Internet access logs were used in this study to predict high-risk students. Since the raw data in log files are high-dimensional, complex and full of noise, several methods were proposed for the preprocessing of the data source. A high-dimensional feature selection framework is then designed to prepare features for the construction of a prediction model with good trade-off between computational efficiency and prediction performance. Experiments showed that the proposed prediction model can identify about 85% of high-risk students. Some online characteristics of high-risk students were also discovered, which might help student counselors and educational researchers better understand the relationship between students’ Internet use and their academic performance.     

工业现场级ZigBee手持控制器的设计与实现

考虑将ZigBee这种新兴的无线通信技术应用到实际工业现场,文中设计开发了一种便携式ZigBee手持控制器.该文首先概述了手持控制器工作原理,然后详细介绍了系统硬件结构及实现的总体方案,最后描述了系统软件设计部分并给出了相应流程图.目前该ZigBee手持控制器工作稳定、性能可靠,达到了相应性能指标,可满足一般工业应用的要求.     

交通干线无线宽带覆盖网络传输性能研究

对交通干线无线宽带覆盖网络骨干链路的传输性能进行研究,建立了WMN骨干链路的传输模型,研究了对其多跳传输性能产生影响的主要因素。针对WMN骨干链路多跳传输带宽急剧下降和无线骨干链路的带宽瓶颈等两个多跳传输难题,提出了基于802.11n的多模Mesh节点结构,实现了其原型系统。实验结果表明其具有超过165Mb/s的基准带宽,在60Mb/s的限速应用环境下,每跳的带宽衰减小于1%,满足了地面无线网络覆盖的需求。     

常识问题——常识,人工智能与数理逻辑

本文提出了常识问题，通过确立常识准则和常识模型，指出一个建立常识逻辑的技术途径。     

采用确定放置式布网的WSN监测界面设计

本文针对采用确定放置式布网的WSN系统,基于VB和SQL Server2000数据库,设计了一种WSN传感器节点采集参量监测界面。包括监测系统中主菜单界面设计、各子功能界面设计以及VB和SQL Server2000中各数据库相关操作程序设计等。系统能实现WSN系统中各节点位置信息的显示;节点工作状态显示;实时参量值显示;参量变化曲线的实时绘制;阈值设置;声光报警;数据信息的存储查询等功能。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.