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.     

高维空间下求解MDA的扰动算法及其在人脸识别中的应用

本文提出了一种在高维空间下直接求MDA(Multiple discriminant analysis)最佳解的扰动算法，并把这一算法应用于人脸的识别中。在传统的MDA求解算法中，一般要求训练样本的个数足够大，以至于其类内散射矩阵为非奇异矩阵，即所谓的“小样本”问题。但是，在入脸识别中，由于人脸空间的维数非常大，而训练样本的个数有限，造成类内散射矩阵为奇异矩阵，从而使得用传统的求解方法失效。为了能在高维空间中求出MDA的最佳解，本文采用扰动的思想，巧妙地避开了矩阵的奇异性问题并找到了最佳变换矩阵。此外，把这一算法用于人脸的识别中，对ORL人脸图像库的实验显示，采用本文提出的算法达到比较低的错误率，其错误率仅为特征脸(Eigenface)方法的49．9％，为Fisher脸(Fisherface)的7914％。     

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。     

A serial version of the Pohlig-Hellman Algorithm for computing discrete logarithms

We describe a method for computing discrete logarithms in any nonsimple cyclic group, which may be considered as a serial version of the Pohlig-Hellman Algorithm. The complexity is the same as that of the P-H Algorithm, but does not need the Chinese Remainder Theorem.