火灾中考虑整体性的钢梁破坏形态研究

1990年英国broadgate火灾之后,结构整体性以及由于温度而在整体结构中产生的附加应力得到了人们的重视。处于整体结构中的梁的破坏形态与单个构件的梁的破坏形态有较大差别,当考虑梁的悬链线效应时其抗火承载力明显提高。文章采用大型通用有限元程序ANSYS,对T.C.HLiu所做试验进行了验证,表明了有限元计算悬链线效应的可靠性,同时通过计算分析,提出了处于整体结构中钢梁在火灾中破坏的5个阶段,并给出了钢结构防火的概念设计建议。     

黑板型信息融合专家系统在水环境监测中的应用

现代水环境监测系统是一个具有现代高性能、多层次的复杂系统,这要求多个(种、异质)传感器在不同尺度上对水环境进行观测。探讨和分析了信息融合技术应用于水环境监测的特点,提出了一种运用专家系统和黑板理论的水环境监测信息融合结构框架。     

小高跨比单层单跨实腹门式刚架平面内弹性整体稳定分析

采用精确的有限元非线性分析方法对小高跨比单层单跨实腹门式刚架的平面内弹性整体稳定性进行了大量的参数分析，指出在梁上均布荷载作用下，当柱梁高跨比小于某一数值时，一般不发生传统理论所认为的反对称失稳模式，而是以对称失稳模式为主。给出了修正的柱计算长度系数，并与规范给出的数值进行了对比，认为这种对称失稳模式更不利于门式刚架的整体稳定，应该引起足够的重视。     

基于模糊匹配的平面连杆机构反求设计研究

对平面连杆机构曲线进行了分析,提出了用一组特征参数来描述连杆曲线,建立了连杆曲线图谱库,并将模糊论应用于连杆机构的曲线匹配,从而获取所求的机构参数.最后给出了应用实例,证实了该方法的可行性与有效性.     

Concealed weapon detection and visualization in a synthesized image

Images acquired by heterogeneous image sensors may provide complementary information about the scene, for instance, the visual image can provide personal identification information like the facial pattern while the infrared (IR) or millimeter wave image can detect the suspicious regions of concealed weapons. Usually, a technique, namely multiresolution pixel-level image fusion is applied to integrate the information from multi-sensor images. However, when the images are significantly different, the performance of the multiresolution fusion algorithms is not always satisfactory. In this study, a new strategy consisting of two steps is proposed. The first step is to use an unsupervised fuzzy k-means clustering to detect the concealed weapon from the IR image. The detected region is embedded in the visual image in the second step and this process is implemented with a multiresolution mosaic technique. Therefore, the synthesized image retains the quality comparable to the visual image while the region of the concealed weapon is highlighted and enhanced. The experimental results indicate the efficiency of the proposed approach.This material is based on part of the work carried out at the SPCR laboratory of Lehigh University and the work is partially supported by the U. S. Army Research Office under grant number DAAD19-00-1-0431. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.     

Contextual cues aid recovery from interruption: The role of associative activation.

A series of experiments introduced interruptions to the execution phase of simple Tower of London problems and found that the opportunity for preparation before the break in task reduced the time cost at resumption. Retrieval of the suspended goal was facilitated when participants were given the opportunity to encode retrieval cues during an "interruption lag" (the brief time before engaging in the interrupting task) but was impeded when these visual cues were subsequently altered following interruption. The results provide useful support for the goal-activation model (E. M. Altmann & G. J. Trafton, 2002), which assumes that context--at the points of both goal suspension and goal retrieval--is critical to efficient interruption recovery. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

碳/环氧复合材料的冲击拉伸

该文研究了T300碳纤维单向增强的环氧复合材料,在应变率从10~(-3)/s到10~3/s范围内的冲击拉伸行为.通过对实验数据进行拟合,得出该范围内材料对应变率具有弱的敏感性,表现在破坏强度及破坏应变随应变率增加不显著变化,平均模量几乎不受应变率的影响.分析了试件的几何尺寸效应,讨论了应力波作用对破坏形态的影响以及实验中观察到的拔出现象.从应变率在10~2～10~3/s附近材料行为某些非确定性,指出在更宽范围内了解其性能的必要性.     

基于自组织神经网络的实时统计特征抽取方法

应用人工神经网络讨论统计了特征抽取的原理，提出了相应的抽取方法。该方法具有实时性，容错性，高数据压缩率和良好的特征分辨率。通过计算机仿真和桥梁索力状态的实例计算分析证实了该技术具有广泛的应用价值。     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.