基于Windows邮槽机制的程序调试模块

调试是软件实现的一大关键步骤，程序开发环境提供了多种调试工具，其中打印各类数据及状态信息是很重要的工具。本文采用Windows进程间通信的邮槽mailslot来实现状态信息的输出，以此实现的调试模块简单而且功能可扩展。     

神经网络应用范例及其实现方法

本文在描述神经网络的几个应用范例基础上讨论了神经网络应用的实现方法，通过本文，希望对研究神经网络应用的同行们能有一些启示。一、反向传把网络人工神经网络有各种不同的模式，在这里介绍反向传播网络。这种网络由输入层、输出层和隐层组成。隐层可以由一层或多层构成。各个层内的神经元数目主要依据具体求解的实际问题及采用的求解方式来确定。对于输入信号由输入层传递到隐层，经过传递函数，把隐层的输出信息传递到输出层，最后由输出层输出结果，这种认输人层到输出层的传递顺序称为正向传播。在这种传播中，每一层神经元的状态只…     

FIR神经网络在导航数据滤波中的应用

本文以寻北仪为例,介绍了一种新的惯性测量元件输出数据的滤波方案。即利用输出数据对一种叫做基于实时反向传播算法的FIR神经网络进行训练,继而通过神经网络来预测输出数据,再与实际输出数据加权平均,从而提高了数据的准确性。     

多输入—多输出离散时间系统的辨识和最优控制设计的软件

对任何多输入—多输出离散时间系统,如果其输入—输出关系可用引言中所定义的第二类差分方程来描述,那么只要按照本文的要求做出试验数据,就可以使用这个软件算出此模型的阶数和参数,并求出稳态情况下的卡尔曼滤波增益矩阵、最优反馈系数矩阵和在线校正输入设定值需要用到的系数矩阵,同时以数字模拟的方法对所建立的数学模型进行校验(包括输入—输出数据拟合和状态反馈控制模拟等)。特别是对有时滞的线性定常随机系统,应用这个软件,也不难建立实现最优反馈控制的数学模型。在本文中,除了介绍软件的功能和粗框图外,还说明了软件的使用方法及其有关约定,并给出实时控制的计算公式和框图,同时还简单介绍应用这个软件成功地解决了一个工业生产过程控制问题的实例。     

基于异常处理机制的程序“故障-差错-异常/失效”传播机理分析

如何有效地获取具有代表性的差错数据进行差错注入仍是故障注入技术一个有待深入研究的问题。文中通过故障注入实验分析了程序的"故障-差错-异常"传播机理,说明了从异常层次进行程序错误行为分析及其差错数据收集的合理性。该研究为当前具有较大规模的、具有异常处理机制的程序进行差错数据的收集提供了新途径。     

位置自动报告系统的设计与应用

针对民用GPS应用的局限性,设计了一种能自动地将实时位置状态信息发布出去,并在接收端的计算机上图形化的表示这些数据的位置自动报告系统.系统将GPS接收机输出的数据调制成音频信号,经无线电台发射,接收端利用解调软件将信号还原为位置状态数据来确定发射部分的位置及状态.     

电力变压器局部放电超高频检测仿真分析

局部放电作为变压器的常见故障,若能及时发现处理并将其恢复到正常运行状态,将大大减少损失,提高供电可靠性。本文通过超高频检测法与时域有限差分法,分析了局部放电引起的超高频电磁波在变压器箱体中的传播特性,以及变压器箱体结构和铁芯绕组对电磁波信号的传播影响。在软件XFDTD上对局部放电辐射的电磁波在变压器内部的传播情况进行仿真,预测障碍物引起的PD信号到达时延,计算其与理论时延的差值,以此来评估对变压器PD定位精度的影响,并对其进行分析。仿真结果显示,从两个模型输出的数据结果中发现,在障碍物的存在下,引入的PD信号到达会出现估计微分时延。结论为变压器箱体对电磁波有衰减作用,所以传感器应放置于变压器内部;铁芯对电磁波也有畸变与衰减作用。     

空中交通数据统计分析软件的研究与开发

随着民航空中交通管理水平的普遍提高，能够为管理人员提供灵活、准确、全面的运行状态评估和管理决策支持信息的自定义统计分析软件的需求日益强烈：为此，基于统计学理论中的相关知识，给出符合现代空中交通管理的数据特征统计量，给出数据特征统计量的计算方法以及从数据采集到特征统计量输出的实现手段，实现了针对空中交通流量的常用数据统计及处理软件，并介绍了以Visud C＋＋为开发工具的软件实现过程及其主要特点．验证结果真实可靠，到达预期目的。     

一种高精度数控直流电流源的设计与实现

采用自行设计制作的高精度电压源,利用单片机、D/A转换器、运算放大器等器件来控制场效应管导通状态的原理,达到了输出恒流的目的.整个系统采用89C52单片机作为主控部件,实现了电流可预置、可步进调整、输出的电流信号可直接数字显示的功能.采用硬件闭环、软件闭环、软件实时积分、实时滤波的方法,锁定输出电流,从而实现了高精度恒流源的目的.软件对相应数据进行数据分段查值补偿采样电阻的温度变化,段间采用线性插值补偿的方法,进一步提高了输出精度.该电流源具有精度高、结构简单、工作稳定、操作方便、成本低廉、带负载能力强等优点.     

一种用VHDL设计实现的有线电视机顶盒信源发生方案

介绍了一种有线电视机项盒的信源发生方案。该方案采用可编程逻辑器件来完成计算机EISA总线输出数据的格式转换,从而提高数据输出速率,满足信源要求。而且,该方案采用了开放性的结构,可以通过软件修改来实现功能扩充。还详细叙述了采用VHDL来进行可编程逻辑器件的功能设计过程。     

Applying a dynamic testability technique to debugging certain classes of software faults

Testability, the tendency for software to reveal its faults during testing, is an important issue for verification and quality assurance. But testability can also be used to good advantage as a debugging technique. Although this concept is more general, we will illustrate it with a specific example: propagation analysis.Propagation Analysis (PA) is a technique for predicting the probability that a data state error affects program output. PA is a technique that produces information about a piece of software's testability. PA bases its prediction on empirical measurement of the probability that an artificial data state error affects program output. After obtaining propagation analysis information for a program and obtaining a failure probability estimate for the program during execution we build a model that can be used to identify possible sites of missing-assignment faults of the form x f(x). Thus we can apply the testability technique PA as a debugging tool.This work supported by a National Research Council NASA-Langley Resident Research Associateship and NASA-Langley Grant NAG-1-884.     

面向数据挖掘的静态电源综合故障诊断研究

静态电源是机场桥载设备最重要的组成部件之一,应用非常广泛。然而,其产生的频发故障会造成设备利用率低、修复率时长和经济损失等问题,在基于桥载设备的安监系统上,提出了静态电源综合故障诊断方法,通过数据挖掘软件,建立了静态电源故障诊断预测模型。通过在线数据库测试结果表明得到了综合故障诊断方法在预测静态电源故障上显现的特点,得到了静态电源的未来状态,实现了对静态电源的实时故障进行预测,进而为解决故障提供方向和目标,最终达到降低经济损失最大化的目的。     

虚拟仪器系统误差的软件消除算法

为了提高测量精度,设计并实现了虚拟仪器系统误差的软件消除算法。以LabVIEW为开发软件,PCI输入/输出卡为信号采集和输出控制构建虚拟仪器系统;根据组成将误差分为：软件误差和硬件误差。软件误差来源于开发软件,消除方法具有多样性;硬件误差消除算法：采集一定量的数据值并按大小排序,删除序列中较大和较小的数据值,将剩下的数据值求平均得平均值,再将它加上表头检定值即为测试值。实验结果表明,算法能有效消除误差。     

基于数据挖掘的静变电源故障预测研究

静变电源是机场桥载设备最重要的组成部件之一,应用非常广泛,但与此同时,其产生的高频故障会造成设备利用率低、修复率时长和重大经济损失等问题,在基于桥载设备的安监系统上,设计了静变电源安监信息采集节点,通过数据挖掘软件,建立了静变电源故障预测模型.经过比较Apriori算法和其他典型数据挖掘算法的性能,结合在线数据库测试结果表明,得到了不同的典型算法在预测静变电源故障上显现的特点,通过选择最优算法高效地预测了静变电源的未来状态,实现了对静变电源的实时故障预测,进而为解决故障提供了方向和目标,最终达到了降低经济损失最大化的目的,具有很深的实际意义.     

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.     

Nonlinear observer design via passivation of error dynamics

We present a new design scheme of nonlinear state observers (global, full order, asymptotic observers) through passivation of the error dynamics. In order to consider passivity of the error dynamics for the observer problem, we place a conceptual input and output on the generalized error dynamics which also includes the plant, and the strictness of passivity is extended with respect to a set in which the estimation error becomes zero. Then, output feedback passivation for the error dynamics will lead to the construction of a state observer. It is also shown that a nonlinear observer is generally vulnerable to measurement disturbance, in the sense that even an arbitrarily small measurement disturbance can lead to a blowup of the error state. However, due to the passivity of the error dynamics, the proposed nonlinear injection gain can be easily modified for the observer to be robust to measurement disturbances.     

‘Integronsters’, integral and integrated modeling

In many cases model integration treats models as software components only, ignoring the fluid relationship between models and reality, the evolving nature of models and their constant modification and recalibration. As a result, with integrated models we find increased complexity, where changes that used to impact only relatively contained models of subsystems, now propagate throughout the whole integrated system. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, when efficiency is supposed to be gained from independent development of modules. Treating models only as software in solving the integration challenge may give birth to ‘integronsters’ – constructs that are perfectly valid as software products but ugly or even useless as models. We argue that one possible remedy is to learn to use data sets as modules and integrate them into the models. Then the data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a module-independent baseline dynamics, which is then incremented when scenarios are to be run. In this case it is not the model output that is directed into the next model input, but model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. However still with growing overall complexity, calibration can become an important limiting factor, giving more promise to the integral approach, when the system is modeled and simplified as a whole.     

Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks

We propose to fit a recurrent feedback neural network structure to input–output data through prediction error minimization. The recurrent feedback neural network structure takes the form of a nonlinear state estimator, which can compactly represent a multivariable dynamic system with stochastic inputs. The inclusion of the feedback error term as an input to the model allows the user to update the model based on feedback measurements in real-time uses. The model can be useful in a variety of applications including software sensing, process monitoring, and predictive control. A dynamic learning algorithm for training the recurrent neural network has been developed. Through some examples, we evaluate the efficacy of the proposed method and the prediction improvement achieved by the inclusion of the feedback error term.     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.