A frame-based knowledge representation model and its mapping to nested relations

The first part of this paper introduces the basic constructs of a frame representation model and gives a formal definition for them. Subsequently the nested relational model (NF²) is described as an extension of the classical relational model to support relation-valued attributes. In the second part of this paper both models are compared with each other and a mapping of frame representation structures to NF² structures is specified. The structural similarities between frames and NF² relations are made clear and it is shown that their main difference is due to the type polymorphism introduced by the concept hierarchy of the frame model. This causes type collisions to occur in the strictly typed NF² model when frames are directly mapped to NF² structures. Two solutions to this problem are suggested and compared. The paper concludes with a reformulation of query operations of the frame model in terms of NF² algebra operations.     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

MDT的特征建模及其在冶金设备设计中的应用

介绍MDT软件三维实体建模的特点及方法。从草图设计开始 ,通过施加尺寸约束和几何约束生成草图特征 ,再应用尺寸驱动技术自动生成零件或设备的三维实体模型。在冶金设备及零部件设计应用中 ,根据设备或零件构成特征 ,对规则形体采取拉伸、旋转、扫掠处理 ,对不规则形体则根据特征进行切割处理 ,经过编辑就能得到符合实际的三维实体模型 ,进一步进行有限元分析、运动学和动力学仿真     

高校电视台节目漫谈

结合高校校园电视台的职能和特点 ,深刻剖析了高校电视台几种类型节目的特点以及对高校校园文化的作用。     

论科技论文英语摘要的表现形式

论述科技论文中英语摘要的文体风格、时态、语态和基本要求，并结合例句介绍了常用的表现形式，使之简洁、准确和流畅。     

信息融合的飞机识别方法

以信息融合的理论为基础，利用从可见光图像序列和8-12μm长波红外图像序列中提取的信息对不同种类飞机进行识别。采用矩特征并结合BP神经网络的方法，分别在特征级和决策级两个不同层次上实现了信息融合。实验结果表明，通过信息融合进行飞机识别的准确率可达到90％以上。     

Orientation-based unique representation for planar curves and shapes

This paper presents an orientation-based representation for planar curves and shapes. The new representation can uniquely represent all types, of planar shapes, be it convex, nonconvex, polygonal, smoothly curved, or piecewise smooth shapes. It is based on a new parameterization, theabsolute integral orientation. This representation is invariant under translation and rotation. The absolute integral orientation is a parameter invariant under scaling. As a result, matching of similar shapes (i.e., determination of the relative orientation and the scaling factor) using the absolute integral orientation as the parameter is easier than using the arclength as the parameter. In addition, the new representation has the feature of adaptive sampling, making it more compact and efficient than arc-length-based representations.     

基于参考点的模糊知识表示

本文分析了模糊关系模型的知识表示途径，并由此提出基于参考点的模糊知识表示，分析了它与人类记忆经验性知识的相信性，并证明模糊关系模型能以任意精度逼近具有有限个极值点的有界连续函数。