激光指示器多参数检测系统软件设计

针对激光指示器多参数检测系统的结构特点,结合面阵CCD图像采集原理,开发了集数据采集技术和图像处理技术于一身的应用测试软件。介绍了软件的总体设计方案和具体实现过程,解决了激光指示器性能参数无法定量测量的问题,填补了国内多数兵器厂家没有专门针对激光指示器多参数检测的现代测试手段的空白。     

Semiparametric Bayes hierarchical models with mean and variance constraints

In parametric hierarchical models, it is standard practice to place mean and variance constraints on the latent variable distributions for the sake of identifiability and interpretability. Because incorporation of such constraints is challenging in semiparametric models that allow latent variable distributions to be unknown, previous methods either constrain the median or avoid constraints. In this article, we propose a centered stick-breaking process (CSBP), which induces mean and variance constraints on an unknown distribution in a hierarchical model. This is accomplished by viewing an unconstrained stick-breaking process as a parameter-expanded version of a CSBP. An efficient blocked Gibbs sampler is developed for approximate posterior computation. The methods are illustrated through a simulated example and an epidemiologic application.     

紧密排列燃料组件交混系数试验研究

单相及两相工况下的交混系数是燃料组件热工分析、特别是子通道分析的一个关键参数。本文介绍19根棒束绕丝结构紧密排列燃料组件交混系数单相试验的试验本体结构、试验内容、试验方法及试验结果。试验研究获得了燃料组件出口温度分布及紧密排列燃料组件交混系数,最后对试验结果、交混系数值以及热工参数对交混系数的影响等进行分析。     

烧结过程的模糊控制系统设计

根据烧结过程是一个多变量、非线性、时变的复杂系统,提出带参数预估的模糊控制方法去调节物料平衡系统和烧透点系统。经一年的运行,系统具有较强的鲁棒性。     

Adaptive parameter identification of linear SISO systems with unknown time-delay

An adaptive online parameter identification is proposed for linear single-input-single-output (SISO) time-delay systems to simultaneously estimate the unknown time-delay and other parameters. After representing the system as a parameterized form, a novel adaptive law is developed, which is driven by appropriate parameter estimation error information. Consequently, the identification error convergence can be proved under the conventional persistent excitation (PE) condition, which can be online tested in this paper. A finite-time (FT) identification scheme is further studied by incorporating the sliding mode scheme into the adaptation to achieve FT error convergence. The previously imposed constraint on the system relative degree is removed and the derivatives of the input and output are not required. Comparative simulation examples are provided to demonstrate the validity and efficacy of the proposed algorithms.     

一个银行柜面系统的设计与实现

计算机系统在金融界的应用日趋重要，银行拥有高效，完善的计算机系统就意味着有更高的经济效益。本文基于银行内部企业网的银行柜面系统的设计与实现，作了一些探讨与研究。     

基于Proteus仿真的小器件故障诊断器性能探究

为解决复杂环境下圆周轨道上小器件高速运动状态的监测问题,研制了一种光电反射式故障诊断器,并进行了系统参数的整定.上述装置应用光反射原理,光敏管接收来自小器件反射的红外光线,光信号经光电转换形成便于测量的电信号.基于系统仿真,通过对系统参数整定使故障诊断器适于应用系统,实现了对的圆周轨道上小器件运动的监测.并将装置应用于纺织行业的环锭细纱机的断纱检测系统,考虑到环锭细纱机纺纱的实际工业环境,包括光环境的变化、环境噪声的干扰、器件小等系统稳定性问题,通过对钢丝圈的运动监测,解决了纺纱断纱监测难题.     

Use Hausdorff metric to analyze convergence of parameter estimation in system identification

For a linear time-invariant system model, this paper analyzes the convergence of parameter estimations as the length of the input–output data tends to infinity through the prediction error method. It is known that the sequence of the criterion functions converges uniformly in the parameter with probability one as the data length tends to infinity. The parameter estimation is represented by a set in general, instead of by a single point, on which the criterion function takes its minimum. Thus a mathematical feature of the convergence problem of parameter estimation is in that we are needed, from the convergence of a sequence of functions, to infer the convergence of the sequence of their sets of minimizing arguments. The Hausdorff metric is suggested to measure the distance between sets and then is used to discuss the convergence problem here. According to the Hausdorff metric, the convergence of parameter estimation is not guaranteed in general. A condition guaranteeing such convergence is given.     

ASP．NET安全问题及解决策略

首先提出在web应用开发中会面临的一些基本安全问题，及相应的解决措施。然后对基于ASP．NET开发环境下面临的一些安全问题．给出具体的解决方案。     

Convergence analysis of central and minimax algorithms in scalar regressor models

In this paper, the estimation of a scalar parameter is considered with given lower and upper bounds of the scalar regressor. We derive non-asymptotic, lower and upper bounds on the convergence rates of the parameter estimate variances of the central and the minimax algorithms for noise probability density functions characterized by a thin tail distribution. This presents an extension of the previous work for constant scalar regressors to arbitrary scalar regressors with magnitude constraints. We expect our results to stimulate further research interests in the statistical analysis of these set-based estimators when the unknown parameter is multi-dimensional and the probability distribution function of the noise is more general than the present setup.