CIMSCLASS─—面向CIM的零件分类软件开发工具

介绍了一个能为工厂设置零件编码系统和开发零件分类系统的工具系统。该系统能适应用户的具体环境，能对零件的特征信息做较详细的描述和建立相应目标的零件分类系统，从而能在ＧＴ（成组技术）基础上为机械加工零件开发ＣＡＤ／ＣＡＰＰ／ＣＡＭ集成系统提供支持，以满足ＣＩＭＳ的需要。     

基于神经网络的VQ图象编码

文章给出将神经网络与矢量量化相结合的两种编码方法，介绍了其原理和实现的算法。经计算机模拟表明，在比特率低于０．３ｂｐｐ时仍可得到高质量的编码图象。     

OFA-II型定量荧光仪在油气勘探中的应用

定量荧光仪在现场应用中受到各种因素的影响:如岩屑样品浸泡时的干、湿;图谱是否出现了淬灭;环境温度的影响等等。文章通过具体事例给出解决这些问题的办法。     

热重分析对高分子材料中碳酸钙的定量研究

碳酸钙是高分子材料中常用的无机填料，在高温时会同二氧化碳而失重。利用热重分析可准确定出高分子材料中碳酸钙的含量，同时还可测出聚合物，挥发物的含量，热重分析法样品用量小，灵敏度高，所需时间短。     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

特低渗透油气藏成岩储集相的定量评价方法

针对陕北斜坡东部特低渗透储层受沉积作用和成岩作用两大因素控制的特点，利用灰色系统理论对成岩储集相进行了研究。分析了成岩作用综合效应及其相应的视压实率、视肢结率、视微孔率、成岩综合系数等定量指标和岩心分析数据，建立了成岩储集相综合评价参数、指标和分析方法。通过在安塞油田低孔隙度特低渗透长6。油藏储层的实际应用，对目的层段开展了Ⅰ，Ⅱ，Ⅲ和Ⅳ类成岩储集相的定量评价和油气富集描述，有机地集成和综合了多种信息，实现了成岩储集相对沉积学、岩石学和成岩作用等特征的综合表征，并划分出有利成岩储集相区域，为指导油田有利含油区块的筛选和开发决策提供了重要依据。     

恢复地层剥蚀厚度方法综述

恢复地层剥蚀厚度是对盆地进行定量研究的重要基础工作。本文综述、评价了目前常用的恢复剥蚀厚度的方法,并对作者曾提出的天然气平衡浓度法作了进一步的阐述。     

定量电子晶体学硼对Ni3Al的电荷密度分布的影响

阐述了定量电子晶体学测定晶体电荷密度分布的基本原理和方法，不足之处和改善途径。以硼对Ｎｉ３Ａｌ的电荷密度分布的影响为实例，介绍了定量电子晶体学在研究晶体电子结构方面的应用前景。     

Prediction of fire classification for wood based products. A multivariate statistical approach based on the cone calorimeter

Wood has long traditions as a building material, and is often used in construction elements, and as interior and exterior surfaces in the Nordic countries. In most applications, there are reaction to fire requirements to products used as surfaces, e.g. in escape routes and larger public spaces. Most wood products will therefore have to be treated with fire retardant (FR) agents to fulfil the strict requirements to properties connected to heat release and flame spread. Unfortunately, FR agents usually also increase the smoke production, as they cause a more incomplete combustion of the wood. The wood product manufacturers seek to find the optimal amount of FR additives where both heat release and smoke production in the classifying test are within the requirements given in the building regulations. This paper describes models for prediction of the European reaction to fire classes of wood products. The models are based on multivariate statistical analysis, and use test results from the cone calorimeter test as input. The presented models are, with very good precision, able to predict which Euroclass and additional smoke class a wood based product would obtain if it were to be tested in the single burning item test. Copyright © 2006 John Wiley & Sons, Ltd.     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.