Dynamic analysis of the NPP pressure sensor system

The aim of the work was to develop on-line methods of control and diagnostics of pressure sensors at a NPP. The analysis was carried out along two lines:

1. i) The detection system including the sensor itself was modeled theoretically to obtain and study its transfer function, and to establish correspondence between the spectral characteristics of the pressure signal.

2. ii) The numerical processing of the signal using the AR technique to reconstruct the transfer function and evaluate the system's response, to a step impulse, as well as the spectral analysis. The hydraulic model presented indicate that the spectral lines observed at 10 Hz are due to longitu-dinal oscillations of water in the system branches, while the response time of the detection system is effectively the time of signal retardation in the porous ceramic diaphragms of the δ-cell of the sensor itself.

     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

Bivariate Scientific Function Finding in a Sampled,Real-Data Testbed

This article reports the results of a study of domain-independent function finding based on a collection of several hundred real scientific data sets. Prior studies have introduced the controversial idea of discovering functional relationships of interest to scientists directly from the data they collect. The evidence presented here supports the view that this is sometimes possible, but it also suggests how often purely data-driven discovery is not possible and how much more difficult it may be than has often been assumed. Experience with sampled examples of real scientific data suggests as well that emphasis on search in prior studies may have been misplaced. For the function-finding problems studied here, scientists typically propose only a handful of different functional relationships. The difficulty is not in searching through a large space of relationships but in evaluating a few common ones to determine if they are likely to be of scientific interest.     

智能化红外焊缝检测仪的实时信号自相关分析

详细介绍了在改进红外焊缝检测仪的过程中实时信号的一种抗干扰方法－－自相关方法。重点离自相关方法的数学模型，软件框图及设计思想，并给出了实验结果。     

Stability analysis and antiwindup design of uncertain discrete-time switched linear systems subject to actuator saturation

This paper investigates the stability analysis and antiwindup design problem for a class of discrete-time switched linear systems with time-varying norm-bounded uncertainties and saturating actuators by using the switched Lyapunov function approach.Supposing that a set of linear dynamic output controllers have been designed to stabilize the switched system without considering its input saturation,we design antiwindup compensation gains in order to enlarge the domain of attraction of the closed-loop system in the presence of saturation.Then,in terms of a sector condition,the antiwindup compensation gains which aim to maximize the estimation of domain of attraction of the closed-loop system are presented by solving a convex optimization problem with linear matrix inequality(LMI)constraints.A numerical example is given to demonstrate the effectiveness of the proposed design method.     

一种改进的灰度预测模型

灰色预测模型通常是GM模型,但预测精度有时不令人满意。在对模型GM做了进一步研究的基础上,提出了一个预测精度较高的新灰色预测模型,并从理论上证明了这种模型可以有效提高建模数据序列的光滑度。最后把此方法应用于东部某镇GDP的建模中,试验结果表明该方法是可行且有效的,所建模型的精度优于传统GM（1,1）模型的精度。     

基于森林火灾平台的红外图像识别技术研究

森林火灾探测一直是森林资源保护工作中的老大难问题。早期的火灾探测技术多是基于火灾的烟雾和温度特征的,但其判别标准过于单一,误报率较高。随着计算机技术的发展和红外探测器的发明,出现了基于红外图像处理的森林火灾防护技术,该技术主要对红外探测器获得的森林现场的红外图像进行一定的处理后识别图像中是否有火灾出现。本文的主要工作是进行红外图像增强算法的研究,通过对各种算法的描述和仿真实验结果分析,提出一种相对较适合森林背景红外图像的处理算法一将基于频域处理的提升小波变换与直方图修正结合起来的红外图像增强算法。     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.     

锡膏激光扫描三维测量系统光强自适应调节技术

根据锡膏激光扫描三维测量系统在测量PCB电路板时底板和锡膏对激光光强的散射效果不同,设计并研制了一套光强自适应调节光源。该光源以程控电源为基础,分析光条中心提取效果与激光光强的关系,应用小波分解与频带能量比相结合的光强自适应调节算法实现测量系统光强自适应调节功能。进行了相关实验,实验结果表明该光源能够很好地实现锡膏激光扫描三维测量系统的激光光强自适应调节,并能选出最适合测量的激光光强,具有很好的适用性。     

曲线曲面小波变换的边界连续性保持

现有的小波分解边界连续性保持方法由于将去除的节点全部插回,摒弃了数据压缩特性.为此在曲线小波分解边界处理方面提出2种基于数据压缩的连续性保持方法:一是插入与Cr连续相关的节点,然后调整相应的控制顶点与高分辨曲线的控制顶点保持一致;二是不插入节点,直接调整与Cr连续相关的控制顶点.在曲面方面,提出了曲面分裂法和T曲面法:通过插入重节点把曲面分裂成边界部分和中心部分并对边界部分插入节点,再调整相应的控制顶点与高分辨曲面的重合;构造了T网格,使其边界部分与高分辨曲面一致,中心部分与低分辨曲面一致.最后把T曲面方法推广到周期曲面小波变换边界处理上.实验结果表明,文中方法既保证了小波分解后模型的边界连续,又保留了数据压缩特性.