SiC颗粒增强铝基复合材料切削表面分形维数及其与抗磨损性能的关系

做为一种耐磨性能很好的材料 ,SiC颗粒增强铝基复合材料切削加工表面具有分形特征。实际研究表明SiC颗粒增强铝基复合材料切削加工表面分形维数与抗磨损性能有密切关系 ,本文还分析了表面分形维数越大其抗磨损性能越强的机理。     

基于极化不变量的飞机目标识别

基于宽带毫米波极化雷达体制背影，通过极化不变量之一的去极化系数，研究了飞机目标的极化散射特性，并提取了极化特征，设计了飞机目标的分类或识别器结构，它能对五种飞机目标进行有效的分类或识别。     

超辐射发光二极管的结构特性及其应用

近几年来,由于超辐射发光二极管在光纤陀螺和光时域反射仪等方面的重要应用,已引起了人们的极大关注,许多国家都在竞相研制和开发.本文综述了目前超辐射发光二极管的主要结构和特性及其应用前景.     

我国第一套Φ170mm三辊轧管机组

介绍了我国第一套Φ１７０ｍｍ三辊轧管机组热轧设备和产品规格及技术特点。     

National and regional corporate spatial structure

Much is known about the macro-impacts of business activities on national and regional economies; however, comparatively little is known at the micro level about the impact spatial agglomeration of corporate wealth and performance has on urban systems. This paper reports on a collaborative research project between the Australian Housing and Urban Research Institute and Dun and Bradstreet, an international financial credit agency, to analyse the spatial implications of corporate structure in metropolitan regions. This paper analyses spatial patterns of medium to large businesses in three Australian State capital cities at the postcode level in order to assess the agglomeration of corporate capital, sales, and performance. The research indicates that ratio analysis can provide a useful insight into the competitiveness of metropolitan regions and the control corporate headquarters have over decision making and investment processes. The findings of this research are of special interest to capital markets, business and urban researchers in the Asia Pacific Region. Received: September 1997/Accepted: September 2001     

FEATURE SOLID MODELING TOOL SYSTEM:FSMTS

In conformity with the principle of Design for Manufacture,feature-based design strate-(?)es have been developed.As the“feature”is relevant to the“macro process plan”and“macro NCprograms”,obviously,“feature”is beyond the power of conventional solid modellers.Neverthe-less,substantial breakthrough has not been made in the solid modeling field,except“feature at-taching”or“feature recognizing”methods have been taken on.In this paper,the theory,concepts,system architecture,and algorithm principles of solid modeling tool system have beenrepresented.The practice of Feature Solid Modeling Tool System (FSMTS) developed atHuazhong University has proved that the tool may be a new foundation of Feature-Based Design.     

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.