微机硬盘不能引导的一种软故障分析

作者在教学工作中碰到因微机硬盘主引导扇区内容被破坏，而不能正常启动的情况。现将出现的现象和解决办法介绍如下。微机配置为：ＣＰＵ：８０３８６ＤＸ；Ｄｉｓｐｌａｙ：ＶＧＡ；Ａ：１．２ＭＢ，Ｂ：１．４４ＭＢＢａｓｅＭｅｍｏｒｙＳｉｚｅ６４０ＫＢ；ＥｘｔＭｅ...     

在VB6.0中实现base64编码/解码

ｂａｓｅ６４是Ｉｎｔｅｒｎｅｔ中传送二进制数据常用的ＭＩＭＥ编码类型。它的基本原理是将每个连续的三个字节（２４位）数据组合表示为四个六位数值，再把每六位数值当作一个ＮＶＴ ＡＳＣⅡ字符来传输，通常情况下，这种编码程序都是用Ｃ／Ｃ＋＋的位操作运算实现的，而ＶｉｓｕａｌＢａｓｉｃ中没有提供针对字节的位运算符。本文通过数学方法得到了移位和节字重组的替代形式，从而用ＩＳＵＡＬｂＡＳＩＣ６．０ＰＵＧＭＢ     

NURBS双向蒙皮造型方法的研究与实现

本文通过对ＮＵＲＢＳ造型方法技术的研究，在自主开发的ＳｕｐｅｒＭａｎＣＡＤ／ＣＡＭ集成系统中成功地实现了基于复杂曲线的ＮＵＲＢＳ双向蒙皮曲面的造型功能，从而建立了完全基于ＮＵＲＢＳ方法的ＣＡＤ／ＣＡＭ系统．通过大量的工程应用和实例验证表明所研究的ＮＵＲＢＳ方法是一种适用于复杂外形产品设计与制造的、灵活有效的造型方法．     

Solid Modeling Error : Analysis and Compensation

ＳｏｌｉｄＭｏｄｅｌｉｎｇＥｒｏｒ：ＡｎａｌｙｓｉｓａｎｄＣｏｍｐｅｎｓａｔｉｏｎ①ＸｕＺｈｉｇａｎｇＨｕａｎｇＫｅｚｈｅｎｇＡｉＸｉｎｇＳｈａｎＬｉａｎｙｅＣｏｌｅｇｅｏｆＭｅｃｈａｎｉｃａｌＥｎｇｉｎｅｅｒｉｎｇ，ＳｈａｎｄｏｎｇＵｎｉｖｅｒｓｉ...     

驱动升级公告

ＥＣＳ精英 Ｋ７Ｓ６Ａ Ｖ１．０Ｘ主板最新ＢＩＯＳ ０１／１５／２００２版新增支持 Ａｔｈｌｏｎ ＸＰ ２０００＋处理器，增加了 ＵＳＢ唤醒功能，修正了在安装Ｗｉｎ９８时，ＵＳＢ键盘不能使用热键的问题［１８２Ｋ］ＥＣＳ精英 Ｐ６ＶＸＡＴ Ｖ１．０Ｘ主板最新 ＢＩＯＳ１．０ｂ版修正了与 Ｔｕｌａｔｉｎ１．２Ｇ ＣＰＵ的兼容性问题 ［２０２Ｋ］下载：ｗｗｗ．ｅｃｓ．ｃｏｍ．ｔｗＡｓｕｓ华硕Ｐ４５３３３主板（无板载网路及声音芯片）最新ＢＩＯＳ １００３Ｃ版［１７９Ｋ］华硕Ａ７Ｖ２６６－Ｅ／ＡＡ主板（无板载网络及声音…     

AN OBJECT-ORIENTED DISTRIBUTED MULTIMEDIA EDITOR

ＡＮＯＢＪＥＣＴ－ＯＲＩＥＮＴＥＤＤＩＳＴＲＩＢＵＴＥＤＭＵＬＴＩＭＥＤＩＡＥＤＩＴＯＲ￥ＸｕＤａｎ（ＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅＤｅｐａｒｔｍｅｎｔＹｕｎｎａｎＵｎｉｖｅｒｓｉｔｙＫｕｎｍｉｎｇ，ＹｕｎｎａｎＰｒｏｖｉｎｃｅ，６５００９１Ｐ．Ｒ．...     

消息动态

ＩＢＭ重夺数据库全球霸主地位等根据Ｄａｔａｑｕｅｓｔ最近发布的统计数据,ＩＢＭ数据库产品的市场占有率在１９９８年再次超越Ｏｒａｃｌｅ,重新占据了全球第一的宝座。１９９８年,ＩＢＭ数据库产品的市场占有率从１９９７年的２８．９％上升到３２．３％,而Ｏｒａｃｌｅ的市场份额由２９．４％下降至２９．３％,尾随其后的是Ｍｉｃｒｏｓｏｆｔ、Ｉｎｆｏｒｍｉｘ和Ｓｙｂａｓｅ等公司。Ｄａｔａｑｕｅｓｔ公司首席分析家ＣａｒｏｌｙｎＤｉ－ｃｅｎｚｏ说：“ＩＢＭ在Ｓ／３９０和ＡＳ／４００系统的销售方面增长强劲,Ｕｎｉｘ与…     

CE公司炉膛安全监控系统的分析和比较

ＣＥ公司炉膛安全监控系统的分析和比较王啸，王志祥，崔大勇，包龙海ＡｎａｌｙｓｉｓａｎｄＣｏｍｐａｒｉｓｏｎｏｆＦｕｒｎａｃｅＳａｆｅｇｕａｒｄＳｕｐｅｒｖｉｓｏｒｙＳｙｓｔｅｍｏｆＣＥＣｏｍｐａｙ（ＵＳＡ）￥Ｗａｎｇｘｉａｏ；ＷａｎｇＺｈｉｘｉａｎｇ...     

HP AdvanceStack桥接器

ＨＰＡｄｖａｎｃｅＳｔａｃｋ桥接器１ＨＰＡｄｖａｎｃｅＳｔａｃｋＢｒｉｄｇｅＬＢＬＡＮ桥接器（Ｊ２８６８１Ａ）经济型无管理桥接器。适用于中型网络工作组。具有１个ＡＵＩ接口，１个ＡＵＩ或ＢＮＣ接口。支持２５６个ＭＡＣ地址。提供优秀的性能／价格比。２ＨＰ...     

STD工业微机在催化裂化装置优化控制中的应用

ＳＴＤ工业微机在催化裂化装置优化控制中的应用刘文斌ＡｐｐｌｉｃａｔｉｏｎｏｆＳＴＤ－ＢＵＳＩｎｄｕｓｔｒｉａｌＭｉｃｒｏｃｏｍｐｕｔｅｒｉｎＦＣＣＵｎｉｔＯｐｔｉｍｉｚｉｎｇ￥ＬｉｕＷｅｎｂｉｎ１引言我们根据石化工业生产过程的特点，开发了催化裂化装置...     

Environmental time series analysis and forecasting with the Captain toolbox

The Data-Based Mechanistic (DBM) modelling philosophy emphasises the importance of parametrically efficient, low order, ‘dominant mode’ models, as well as the development of stochastic methods and the associated statistical analysis required for their identification and estimation. Furthermore, it stresses the importance of explicitly acknowledging the basic uncertainty in the process, which is particularly important for the characterisation and forecasting of environmental and other poorly defined systems. The paper focuses on a Matlab^® compatible toolbox that has evolved from this DBM modelling research. Based around a state space and transfer function estimation framework, Captain extends Matlab^® to allow, in the most general case, for the identification and estimation of a wide range of unobserved components models. Uniquely, however, Captain focuses on models with both time variable and state dependent parameters and has recently been implemented with the latest methodological developments in this regard. Here, the main innovations are: the automatic optimisation of the hyper-parameters, which define the statistical properties of the time variable parameters; the provision of smoothed as well as filtered parameter estimates; the robust and statistically efficient identification and estimation of both discrete and continuous time transfer function models; and the availability of various special model structures that have wide application potential in the environmental sciences.     

Deep open-source machine translation

This paper summarizes ongoing efforts to provide software infrastructure (and methodology) for open-source machine translation that combines a deep semantic transfer approach with advanced stochastic models. The resulting infrastructure combines precise grammars for parsing and generation, a semantic-transfer based translation engine and stochastic controllers. We provide both a qualitative and quantitative experience report from instantiating our general architecture for Japanese–English MT using only open-source components, including HPSG-based grammars of English and Japanese.     

Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements

Radiation reflected from vegetation canopies exhibits high spatial variation. Satellite-borne sensors measure the mean intensities emanating from heterogeneous vegetated pixels. The theory of radiative transfer in stochastic media provides the most logical linkage between satellite observations and the three-dimensional canopy structure through a closed system of simple equations which contains the mean intensity and higher statistical moments directly as its unknowns. Although this theory has been a highly active research field in recent years, its potential for satellite remote sensing of vegetated surfaces has not been fully realized because of the lack of models of a canopy pair-correlation function that the stochastic radiative transfer equations require. The pair correlation function is defined as the probability of finding simultaneously phytoelements at two points. This paper presents analytical and Monte Carlo generated pair correlation functions. Theoretical and numerical analyses show that the spatial correlation between phytoelements is primarily responsible for the effects of the three-dimensional canopy structure on canopy reflective and absorptive properties. The pair correlation function, therefore, is the most natural and physically meaningful measure of the canopy structure over a wide range of scales. The stochastic radiative transfer equations naturally admit this measure and thus provide a powerful means to investigate the three-dimensional canopy structure from space. Canopy reflectances predicted by the stochastic equations are assessed by comparisons with the PARABOLA measurements from coniferous and broadleaf forest stands in the BOREAS Southern Study Areas. The pair correlation functions are derived from data on tree structural parameters collected during field campaigns conducted at these sites. The simulated canopy reflectances compare well with the PARABOLA data.     

Data-based mechanistic modelling of stochastic rainfall-flow processes by state dependent parameter estimation

Due to the inherent nonlinearity in the process of transformation of rainfall into river flow, a simple direct input-output transfer function (TF) model may not sufficiently capture the catchment's hydrological dynamics. This paper presents an application of state dependent parameter (SDP) models for nonlinear, stochastic dynamic system to identify the location and form of the nonlinearity in the rainfall-effective rainfall dynamics. The objective was to develop an effective rainfall input time series that was then used to improve the performance of an originally developed direct input-output TF model of daily rainfall-flow relationship. The CAPTAIN Toolbox in the MATLAB^® environment was used in the model identification in which the recursive filtering and smoothing procedures formulated within a stochastic state space setting were applied to the time series data in order to identify the location and form of nonlinearities within a generic TF model. The nonparametric estimation as well as the parametric optimisation of the resulting nonlinear models was done using the Curve Fitting Toolbox in MATLAB^®. The results showed an improved and more parsimonious TF model. The model improved from explaining only 13% of the data to 56% presenting an improvement of 43% in the model fit. The study demonstrates that simple stochastic but robust tools can be successfully applied to develop and improve applicable hydrological models.     

乙烯裂解炉反应管数学模拟新方法

综述了不同类型的乙烯裂解炉反应管数学模型，分析了各类模型的缺点。为此提出了一种进行反应管数学模拟的新方法。即应用CFD方法对反应管内流体流动、传热、传质及裂解反应过程，不经任何简化直接进行数值求解，得到了管内的流场、温度场和浓度场等详细信息。模拟计算得到了反应管长度方向上的温度、速度、压力和组分浓度的变化规律；在反应管径向上存在着明显的速度和温度分布，而组分浓度变化程度不如速度和温度明显；计算结果为提出结焦抑制方法提供了依据。     

Piecewise constant timed continuous PNs for the steady state estimation of stochastic PNs

Reliability analysis is often based on stochastic discrete event models like Markov models or stochastic Petri nets. For complex dynamical systems with numerous components, analytical expressions of the steady state are tedious to work out because of the combinatory explosion with discrete models. The computation of numerical approximations is also time consuming due to the slow convergence of stochastic simulations. For these reasons, fluidification can be investigated to estimate the asymptotic behaviour of stochastic processes. The contributions of this paper are to point out that timed continuous Petri nets may lead to biased estimators of the stochastic steady state and to introduce fluid Petri nets with piecewise-constant maximal firing speeds and sufficient conditions in order to obtain unbiased estimators.     

Fluid computation of passage-time distributions in large Markov models

Recent developments in the analysis of large Markov models facilitate the fast approximation of transient characteristics of the underlying stochastic process. Fluid analysis makes it possible to consider previously intractable models whose underlying discrete state space grows exponentially as model components are added. In this work, we show how fluid-approximation techniques may be used to extract passage-time measures from performance models. We focus on two types of passage measure: passage times involving individual components, as well as passage times which capture the time taken for a population of components to evolve.Specifically, we show that for models of sufficient scale, global passage-time distributions can be well approximated by a deterministic fluid-derived passage-time measure. Where models are not of sufficient scale, we are able to generate upper and lower approximations for the entire cumulative distribution function of these passage-time random variables, using moment-based techniques. Additionally, we show that, for passage-time measures involving individual components, the cumulative distribution function can be directly approximated by fluid techniques.Finally, using the GPA tool, we take advantage of the rapid fluid computation of passage times to show how a multi-class client-server system can be optimised to satisfy multiple service level agreements.     

基于PSO求解随机期望值模型的混合智能算法

随机期望值模型是一类有着广泛应用背景的随机规划问题,为了寻找更为高效的求解随机期望值模型的算法,采用随机仿真产生样本训练BP网络以逼近随机函数,然后应用微粒群算法并以逼近随机函数的神经元网络作为适应值估计和实现为了检验解的可行性,从而提出了一种求解随机期望值模型的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。     

Applications of principal component analysis and factor analysis in the identification of multivariable systems

The identification of a multivariable stochastic system, usually, involves the estimation of a transfer function matrix, which is a general function of frequency. This estimation involves inversion of a large Hermitian matrix, which sometimes may become unwieldly. In this paper we describe how "principal component analysis" in the frequency domain may be used to replace the input/output variables by some function of smaller dimensions without much "loss of information." The analogy between the "factor analysis" of time series in frequency domain and the minimal realization of state space models is pointed out. The principal component approach described in this paper is applied in the case of a simulated system.     

Order estimation for linear time-invariant systems using frequencydomain identification methods

A two-step coarse-fine order estimation technique is proposed to determine the order of the numerator and the denominator polynomials of rational transfer function models for single-input/single-output (SISO) linear time-invariant systems. The coarse order estimation is based on rank detection by verification of the stochastic significance of the singular values of a linearized problem. The fine order estimation is based on a statistical analysis of the maximum likelihood cost function. The method is tested on measurements of low-(4 zeros, 6 poles) and high- (58 poles, 58 zeros) order systems