多污染源随机排污的水质影响分析方法及其应用

针对污水排放特征,将污染源分为连续排放型污染源和间隙排放型污染源。对连续排放型污染源,采用确定性方法定义污染源强;而对于间隙排放型污染源,运用不确定性理论,根据其排放污染源的随机统计特征,对污染源进行随机概化。将不确定性理论应用于多污染源随机排污对环境水体的水质影响分析,给出了基于不确定性理论的水质影响预测方法,建立了二维随机水质数学模型。并将该方法应用于某市水源地水质影响的随机分析,得到了在多污染源随机排放协同影响下水质浓度随机变化的统计特征。     

Effect of the particle size ratio on the conductivity of conductor-insulator powder composites: Numerical simulation

A theoretical method for determining the percolation limit and effective conductivity of composites based on polydisperse mixtures of insulator and conductor powders is proposed. The effect of the particle size ratio between the insulator and conductor on the percolation limit and effective conductivity is studied. Numerical simulation of the microstructure, effective properties, and percolation limit in composites is discussed. __________ Translated from Poroshkovaya Metallurgiya, Vol. 46, No. 1–2(453), pp. 31–40, 2007.     

川东北五宝场构造沙溪庙组储层特征

五宝场构造中侏罗统沙溪庙组气藏是目前川东北地区在沙溪庙组地层中首先发现的气藏，也是川渝地区低效油气合作开发的重点目标。为此，通过沉积相、物性、横向分布等多方面分析研究，确定沙溪庙组储层具有以下特征：以中砂质粗粒、粗砂质中粒岩屑长石砂岩为主，岩性质纯、性脆；具有低胶结物含量、低成分成熟度、高结构成熟度岩石学特征；储集空间可分为洞穴、孔隙、裂缝和喉道3大类12个亚类，以次生溶孔为主要储集空间；裂缝是油气渗滤的主要通道，储集类型以裂缝-孔隙型为主；孔隙结构特征表现为：分选中等偏低、中低孔隙度中小喉型；加砂压裂对沟通储层、改造储层效果较好；沙二段底部嘉祥寨砂岩储层物性好、横向可追踪，是勘探首选产气层。     

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.     

汽车制造用切削液的技术特点及发展趋势

介绍了汽车制造用切削液的技术特点及发展趋势：主要包括环保对切削液的影响、新材料和新工艺发展对切削液使用性能的推动，以及集中供液系统的应用维护技术和管理模式等内容。     

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.