Plasticity of feature-based selection in triple-conjunction search.

Two experiments examined the disruption of feature-based selection in triple-conjunction search at multiple target transfers. In Experiment 1, after 10 training sessions, a new target possessing previous distractor features was introduced. This produced disruption in RT and fixation number, but no disruption in feature-based selection. Specifically, there was a tendency to fixate objects sharing the target's contrast polarity and shape and this did not change even upon transfer to the new target. In Experiment 2, 30 training sessions were provided with three target transfers. At the first transfer, the results replicated Experiment 1. Subsequent transfers did not produce disruption on any measure. These findings are discussed in terms of strength theory, Guided Search, rule-based approaches to perceptual learning, and the area activation model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

面向对象的设计特征与实现语言

在管理信息系统的开发以及分布式数据库系统的设计中，已开始使用面向对象的技术．本文论述了面向对象的设计特征，介绍了用于面向对象设计的各种实现语言，并着重分析了各种实现语言支特面向对象设计的能力．     

Perceptually Grounded Language Learning: Part 2—DETE: A Neural/Procedural Model

In Part 1 of this two-part series, we introduced Katamic memory—a neural network architecture capable of robust sequence learning and recognition. In Part 2, we introduce the Blobs World taskjdomain for language learning and describe the DETE language learning system, which is composed of over 50 Katamic memory modules. DETE currently learns small subsets of English and Spanish via association with perceptual! motor inputs. In addition to Kaiamic memory, DETE employs several other novel features: (1) use of feature planes, to encode visual shapes, spatial relationships and the motions of objects, (2) phase-locking of neural firing, in order to represent focus of atention and to bind objects across multiple feature planes, and (3) a method for encoding temporal relationships, so that DETE can learn utterances involving the immediate past and future. We compare DETE to related models and discuss the implications of this approach for language-learning research.     

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.     

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.     

基于类间分散度和类内集中度的文本特征选择。

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。论文首先定义了两种特征分类能力：一种是特征对类间文档的分散程度,该分散度越大越好;另一种是特征对类内文档的聚集程度,该集中度越大越好。然后把这两种特征影响度有机地结合起来设计了一个新的特征选择方法,该方法能够对所选特征进行综合考虑,从而使获得的特征集具有较好的代表性。仿真实验表明所提特征选择方法在一定程度上能够提高文本分类性能。