Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究。该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相。Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层。     

神经网络在缺陷识别中的应用

为了准确、快速地进行缺陷识别，介绍了一种新型的前馈神经网络模型，即径向基概率神经网络。与以往的算法相比，该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.     

320×240 Pixels CMOS Digital Image Sensor with Wide Dynamic Range

A 320×240 CMOS image sensor is demonstrated,which is implemented by a standard 0.6 μm 2P2M CMOS process.For reducing the chip area,each 2×2-pixel block shares a sample/hold circuit,analog-to-digital converter and 1-b memory.The 2×2 pixel pitch has an area of 40 μm×40 μm and the fill factor is about 16%.While operating at a low frame rate,the sensor dissipates a very low power by power-management circuit making pixel-level comparators in an idle state.A digital correlated double sampling,which eliminates fixed pattern noise,improves SNR of the sensor, and multiple sampling operations make the sensor have a wide dynamic range.     

朝阳沟裂缝性低渗透油田井网适应性研究

通过对比分析朝阳沟裂缝性低渗透油藏与裂缝主方向分别成11.5°和22.5°的两种不同井网的开发效果,表明与裂缝成11.5°的井网与裂缝匹配性较差,开发效果相对较差。并从油藏的水驱控制程度、压力传导情况、采收率及经济效益等方面研究评价了朝1.55区井网的适应性,为指导油田的加密调整,在技术和经济两方面综合评价确定了合理的井距和井网密度,并针对朝1-55区的地质特征,提出了井网和注水方式调整办法,在朝55区块实施后,取得了较好的现场试验效果。     

Representing temporal knowledge in conceptual graphs

This study was motivated by some difficulties encountered by the authors when trying to express temporal knowledge using Sowa's conceptual graph (CG) approach. An overview of Sowa's approach is given and the difficulties encountered when trying to model temporal knowledge are outlined: the disparity of notations allowed by CG theory for expressing temporal information; the ambiguity and incompleteness of tense sspecification; the difficulty of harmonizing tenses and intergraph temporal relations. Various approaches suggested for representing time both in artificial intelligence and linguistics are presented, and an extension to Sowa's approach is proposed in which temporal and nontemporal knowledge are differentiated. In this model points in time are represented as well as time intervals. A semantic interpretation of verbs is provided based on an extension of Reichenbach's model of temporal markers. The authors show how their approach enables the representation of tenses as well as the aspectual properties of natural language sentences.     

最优特征子集选择问题

机器学习和模式识别面临的一个重要问题，就是特征子集的选择问题，即从一个大的已生征特集合，选择一个子集合来一致地描述已知例。特别，最优特征子集选择问题，即最小的特征子集问题的 计算杂性至今学不清楚。     

裂隙岩体中掏槽爆破的影响因素分析

对裂隙岩体中掏糟爆破布孔的影响因素进行了研究，并根据裂分布的情况提出相应的布孔方法和原则。对有关的工程施工具有实际意义。