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.     

计算机技术图书分类标引存在的问题及对策

从文献分类的实践出发，提出了对计算机技术图书分类标引工作存在的问题，分析了问题的原因，提出了解决问题的方法。     

Measurement selection for parametric IC fault diagnosis

This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers and feedforward neural networks trained with various measurement selection criteria. The feedforward networks consistently perform as well as or better than the other classifiers in term of accuracy. Training using BBD consistently reduces the FFN training efforts without degrading the performance. Experimental results suggest that feedforward networks provide a cost efficient method for IC fault diagnosis in a large scale production testing environment.This work is supported by NSF-IUC CDADIC, Project 90-1.     

Review of Language in the Americas.

Reviews the book, Language in the Americas by Joseph H. Greenberg (1987). Greenberg's work on universals of language has crossed disciplinary boundaries to become known to many psychologists interested in language. His equally important work in language classification, of which Language in the Americas is one part, remains unfamiliar to psychologists. This book is a pioneering work in that it is the first genetic classification of the indigenous languages of both North and South America. Greenberg maintains that all of the languages of the Americas belong to three families: Eskimo-Aleut, Na-Dene, and a family that he has named Amerind. This assertion is both important and controversial; the controversy lies in the grouping of most of the languages including all of those of South and Central America into the Amerind family. Most of Language in the Americas consists of the Amerind etymological dictionary, arranged so that the reader can assess lexical similarities within and between subgroups. Greenberg's discussion is informed, articulate, profound, and to the point. He draws on relevant sources and examples from diverse fields of knowledge. The depth and breadth of his scholarship and the clarity of his presentation create a work that is an intellectual pleasure to read. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Congestion pricing of multi-modal transportation systems

This paper deals with the pricing and investment decision problem of multi-modal transportation systems in which the congestion is a significant factor in the assessment of system costs. This study approaches this congestion pricing problem with a number of social welfare maximization problems, all of which have the identical structure except financial constraints. One welfare maximization problem examined in this study yields the pricing and investment decision, which can attain the first-best social optimality for multi-modal transportation systems. Other welfare maximization problems show he second-best optimal solutions that can address two policy questions in transportation economics, namely, the cross-subsidy between competing modes and peak-period congestion charges on a single highway link. Received: February 2001/Accepted: December 2001 This work was supported in part by funds from BK21 Research Division of Seoul National University for Social Infrastructure and Construction Technology.     

雷达目标识别技术综述

针对雷达自动目标识别技术进行了简要回顾。讨论了目前理论研究和应用比较成功的4类目标识别方法：基于目标运动的回波起伏和调制谱特性的目标识别方法、基于极点分布的目标识别方法、基于高分辨雷达成像的目标识别方法和基于极化特征的目标识别方法，同时讨论了应用于雷达目标识别中的4种模式识别技术：统计模式识别方法、模糊模式识别方法、基于模型和基于知识的模式识别方法以及神经网络模式识别方法。最后分析了问题的可能解决思路。     

虚拟现实技术的若干问题及发展展望

虚拟现实技术是上个世纪后50年发展起来的一门新兴边缘学科,本文对虚拟现实技术的概念、分类、研究现状、其关键技术及技术难题等方面做了阐述,并对虚拟现实技术的发展进行展望。     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.