数控系统PLC编辑器的设计与实现

介绍了一种在数控系统中用C语言设计PLC编辑器的方法。首先概括性地介绍该数控系统的系统结构,然后根据ANC_45M加工中心的需求和系统的设计原理从软件角度简要介绍了主程序的基本算法,并着重阐述本PLC编辑器的输入处理以及查错、容错和格式化处理的设计原理和具体实现算法。该编辑器融合了大多数应用软件所有的复制、剪切、粘贴以及按关键字搜索等功能。     

通用数据库输出系统

信息系统是一个收集、储存、处理和输出信息的系统，它的目的是提供信息服务、输出信息产品，因此，每个信息系统的输出子系统都是很重要的。本文介绍笔者近年来开发的一个通用数据库输出系统，该系统是在486微机上用B0rlandC完成的，目前应用于本院文献数据库处理系统上，该系统输出三种信息：文字、图形和报表。一、格式化输出顾名思意，格式化输出是把数据库中的数据按一定格式输出，无论是屏幕显示或是打印，格式化输出都是必要的，通常当用户流览数据库记录或是进行数据检索时，我们并不希望数据以内部格式显示在屏幕上，而是愿意提供…     

图像在数据库中的处理和存储

随着电脑和手机的普及,越来越多的多媒体数据需要存储和处理,数据库管理系统不仅要处理数字、文本等格式化数据,还要处理图像等非格式化数据。本文介绍了图像在数据库中存储和处理的三种方法:分离法、简单集成法、复杂集成法。     

用Delphi处理格式化的文本文件

Delphi中的Read和Readln只能读取以空格分隔的格式化文本文件,若要处理其它分隔符的格式化文本文件,必须通过编制一段程序来实现。本文分析了Delphi处理文本文件的机制,介绍了用Delphi处理格式化文本文件的方法,给出了两个示例程序。     

关于Windows NT网络操作系统中的容错处理

本文介绍了WindowsNT操作系统的RAID（廉价磁盘的冗余阵列），在此基础上论述了其具备容错功能RAIDI和RAIDS的两种方式。通过对其不同特点的分析和比较，阐述了在实际工作中选择容错处理的方法。     

基于故障诊断观测器的输出反馈容错控制设计

针对自适应故障诊断观测器需要误差系统满足苛刻的严格正实条件(Strictly positive real, SPR)和难于处理输出存在扰动的不确定性系统等问题, 提出了一种新型的增广故障诊断观测器的设计方法, 不仅显著地拓宽了自适应故障诊断观测器的适用范围, 而且其具有处理系统扰动的良好性能. 在故障估计的基础上, 提出了动态输出反馈容错控制的设计方法, 避免了基于观测器的状态反馈容错控制的设计难点. 同时, 故障诊断观测器和输出反馈容错控制是分开设计的, 并且又考虑了各自的性能, 简化了设计过程. 最后, 通过仿真实验验证了所提方法的有效性.     

综合化航电核心处理系统容错设计

新一代综合航空电子系统正向开放式、综合化、模块化的方向发展,作为其基础平台的核心处理系统(ICPS)属于机载实时分布式计算机系统,它的设计直接地关系到综合航电系统的性能和成本;它的可靠性要求很高,必须采用容错技术;介绍了综合航电核心处理系统软/硬件结构,重点对综合处理处理系统硬件系统的动态冗余结构与容错处理进行了设计,提出了增加三模—单模冗余单元(TSRU)的方法,通过该方法提高了系统的容错能力;最后对软件系统容错机制进行了研究。     

一类非线性系统的输出反馈容错控制

针对一类非线性系统,研究了故障情况下基于输出反馈的容错控制问题．首先基于Zhang的自适应观测器和Lin的输出反馈控制律设计了正常系统的标称控制律,分析了该控制律的性能,并得到了故障发生后仍然采用该控制律时闭环系统状态有界的充分条件．在此基础上,设计了故障系统的容错控制律,证明了若发生的故障满足前述的充分条件,则故障系统在该容错控制律下稳定．数值仿真表明了该方法的有效性．     

巧用VSFlexGrid控件(vsFlex8.ocx)

数据网格控件是最常用的界面设计工具。本文分别介绍了VSFlexGrid控件的几种使用方法,包括通用数据记录编辑,自定义参数输入,数据自定义输出,这些方法对开发用户界面类程序有很大的实用价值。     

巧用VSFlexGrid控件(VSFlex8.ocx)

数据网格控件是最常用的界面设计工具.本文分别介绍了VSFlexGrid控件的几种使用方法,包括通用数据记录编辑,自定义参数输入,数据自定义输出,这些方法对开发用户界面类程序有很大的实用价值.     

Confidence-based classifier design

In this paper, a new classifier design methodology, confidence-based classifier design, is proposed to design classifiers with controlled confidence. This methodology is under the guidance of two optimal classification theories, a new classification theory for designing optimal classifiers with controlled error rates and the C.K. Chow's optimal classification theory for designing optimal classifiers with controlled conditional error. The new methodology also takes advantage of the current well-developed classifier's probability preserving and ordering properties. It calibrates the output scores of current classifiers to the conditional error or error rates. Thus, it can either classify input samples or reject them according to the output scores of classifiers. It can achieve some reasonable performance even though it is not an optimal solution. An example is presented to implement the new methodology using support vector machines (SVMs). The empirical cumulative density function method is used to estimate error rates from the output scores of a trained SVM. Furthermore, a new dynamic bin width allocation method is proposed to estimate sample conditional error and this method adapts to the underlying probabilities. The experimental results clearly demonstrate the efficacy of the suggested classifier design methodology.     

Some properties of the output error method

The output error identification method is studied in various respects. The stationary points of the associated loss function are investigated. Sufficient conditions for a unique local minimum are given. The loss functions can be minimized using a quasilinearization algorithm. Such an algorithm will give good local convergence. It is, however, shown that global convergence does not always occur. The output error method is also compared with some other estimation methods from the accuracy point of view. It is proved that a prediction error method will give better accuracy. An instrumental variable technique may give better or worse accuracy depending on the actual noise correlation.     

Sampled-data feedback and stability for a class of uncertain nonlinear systems based on characteristic modeling method一类非线性不确定系统基于特征建模方法的采样控制及稳定性研究

In this paper, we investigate the tracking problem for a class of second-order uncertain nonlinear systems using sampled-data output feedback. Our controller is designed based on the characteristic modeling method. We first derive the corresponding characteristic model and then give the sampled-data feedback control law, which is referred to as “golden-section adaptive control based on characteristic models”. The closed-loop system is shown to be stable and, concurrently, it is demonstrated that the tracking error can be made arbitrarily small by taking a sufficiently small sampling period. Our results improve upon the findings of previous work by removing the persistent excitation condition, and also lay certain theoretical foundations for practical applications of golden-section adaptive control.     

面向功率键合图的通用仿真软件的研究

该文介绍了一种面向功率键合图的通用的系统动态性仿真软件－ＰＢＧＳＩＭ。该软件在ＭＡＴＬＡＢ平台上二次开发而成。充分发挥了功率键合图法在分析系统动态特性方面的优势,可以处理包含各种环节的复杂系统,软件从系统的键合图模型出发,自动辨错揭示、自动建立数学模型（字符型和数值型）、进行仿真计算并按要求输出仿真结果,用户可以方便地修改元件参数或模型直至得到满意结果。还可以给定 经变量,对系统的动态特性进行参数     

IP城域网流量预警模型研究

主要完成城域网网络流量监控系统的设计及其预警模型研究,采用SNMP协议监视网络性能、检测分析网络差错,可实现对网络中各种设备端口的流量进行采集,并将监控数据自动保存在数据库文件中。同时也可将记录的数据利用计算机技术图形化,按时间显示聚合组的流量情况。通过该软件可监控、分析记录对象的流量,确保对该监控对象的流量进行记录,并对流量不均衡、流量异常的对象进行预警提示。     

基于模糊神经网络的模型参考自适应控制

用模糊神经网络作为控制器,依靠参考模型产生理想的控制系统闭环响应,从而随时得 到控制系统的输出误差.用梯度法实时修正模糊控制器的输入和输出隶属度参数,得到一种 在线模糊自适应控制的新方法.通过倒立摆的仿真实验表明,该方法是可行的并能适应对象 特性的大范围变化.     

A self-calibration method for error of photoelectric encoder based on gyro in rotational inertial navigation system

As a precision goniometer, photoelectric encoder plays an important role in rotational inertial navigation system (RINS). The encoder is used to measure the rotate angle of rotating mechanism for attitude calculation and feedback of motor control. However, the experiment shows that there are position-related errors in the encoder output, so calibration is necessary in high precision applications. This paper presents a calibration method designed by the characteristics of encoder errors and the structure of a tri-axis RINS. In RINS, the gyro sensitive axis can be coincided with the motor axis by gimbals rotation, and then the gyro and the photoelectric encoder measure the same angle, so the error can be calculated as the difference between the encoder output and the integral of gyro output. The error will be analyzed through Fourier method, then the error will be fitted by least squares method and a harmonic model will be established. The verification experiments demonstrate that the angle measurement error is reduced from ± 40″ to ± 2″, and the attitude output error drops 75″. The calibration method is proved to be experimentally effective.

     

Weighting sequence estimation using pseudo-random sequences with arbitrary experiment length

A method is derived whereby cross-correlation techniques using pseudo-random sequences can be used to estimate the weighting sequence from experimental data of arbitrary lengths without incurring error duo to partial pseudo-random sequence periods. The method consists of forming an ensemble average of the output data before cross-correlation is performed. Slight modifications of the method give increased accuracy when the non-zero off-peak value of the autocorrelation function is not ignored, or when it is known that the weighting sequence is shorter than the pseudo-random sequenco period.     

Frequency domain identification of continuous-time output error models, Part II: Non-uniformly sampled data and B-spline output approximation

This paper treats several aspects relevant to the identification of continuous-time output error (OE) models based on non-uniformly sampled output data. The exact method for doing this is well known in the time domain, where the continuous-time system is discretized, simulated and the result is fitted in a mean square sense to measured data. The material presented here is based on a method proposed in a companion paper (Gillberg & Ljung, 2010) which deals with the same topic but for the case of uniformly sampled data. In this text it will be shown how that method suggests that the output should be reconstructed using a B-spline with uniformly distributed knots. This representation can then be used to directly identify the continuous-time system without proceeding via discretization. Only the relative degree of the model is used to choose the order of the spline.     

灰色-神经网络综合预测模型

该文提出一种灰色-神经网络综合预测模型。该模型由背景值构造、加权GM(1,1)模型和神经网络补偿器三部分组成。其建模机理为:首先对于原始数列进行背景值构造,然后构建加权GM(1,1)模型,同时利用神经网络补偿器获得误差补偿信号,则最终的预测值为加权GM模型的输出值加上补偿值。仿真结果验证了所提方法的有效性。