基于Struts2框架的在线课程平台系统的设计

随着信息时代的发展,在线课程教学模式等网络教学弥补了传统教学模式的一些缺陷,突破了时间以及地理空间的限制。本文基于struts2框架MVC的开发模式,采用bootstrap、MySQL等技术针对web端和移动端分别进行在线课程平台的搭建,实现了相应的教学和学习功能。     

Bootstrap方法对晶振稳定度估计

针对小样本条件下晶振能够快速稳定实现时钟同步的问题,提出了在蒙特卡洛模拟实验下结合传统和改进后Bootstrap方法与卡尔曼滤波方法消除相关噪声,从而维持晶振稳定性的一种方法.由于传统Bootstrap方法限制再生样本的生成范围,使计算结果远远偏离真实情况,而改进Bootstrap方法可以拓展再生样本的生成范围,避免了只抽取原始数据的情况,使得频偏和时偏的估计分布尽可能地接近真实分布.仿真结果表明,改进Bootstrap对频偏和时偏的校正优于传统Bootstrap方法,利用改进Bootstrap与卡尔曼相结合的方法能够实现对晶振的快速稳定.     

A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing

In this study, a novel deep convolutional neural network-bootstrap-based integrated prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is developed. The proposed architecture includes two main parts: 1) a deep convolutional neural network–multilayer perceptron (i.e., DCNN–MLP) dual network is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and to predict the RUL of bearings, and 2) the proposed dual network is embedded into the bootstrap-based implementation framework to quantify the RUL prediction interval. Unlike other deep-learning-based prognostic approaches, the proposed DCNN-bootstrap integrated method has two innovative features: 1) both 1D time series-based and 2D image-based features of bearings, which can multi-dimensionally characterize the degradation of bearings, are comprehensively leveraged by the proposed dual network, and 2) the RUL prediction interval can be effectively quantified without relying on the bearing’s physical or statistical prior information based on bootstrap implementation paradigm. The proposed approach is experimentally validated with two case studies on rolling element bearings, and comparisons with other state-of-the-art techniques are also presented. Subsequently, our code will be open sourced.     

基于Bootstrap的信用风险度量

针对目前主流的风险度量模型大都建立在已给定的或假设的样本以及模型参数分布假设的基础上,但在实际中有效大样本数据又很难获取,且可供参考的有效历史数据更少,造成误差比较大,甚至会无法计算的问题,介绍了一种新的风险度量方法,即基于Bootstrap的风险度量法,特别是在样本很小的情况下,该方法能更好地管理信用风险。     

基于小子样Bootstrap法的雷达辐射源特征分选稳定性分析

雷达辐射源的分选识别算法通常是以正确分选率或识别率为重要指标,但该指标通常是一个点估计值,无法反映系统识别的稳定性和估计精度.本文利用小子样Bootstrap法在有限的实验样本的基础上进行区间估计,并提出了一个特征分选识别率的稳定性判定因子,通过该因子能够量化系统识别率的稳定性.     

混凝土坝运行初期安全监控指标拟定方法研究

针对混凝土坝在运行初期安全监测数据有限、常规大坝安全监控指标拟定方法误差较大的问题,提出基于自助法(Bootstrap)及核密度估计(KDE)理论的大坝安全监控指标拟定方法。通过再抽样构造安全监测数据的自助样本,对扩充后的样本采用核密度估计方法确定监测数据的概率密度函数,在此基础上拟定混凝土坝运行初期安全监控指标。结合仿真算例与某混凝土坝的实测数据,并与置信区间法结果对比分析,结果显示新方法误判率低,表明该方法所拟定的安全监控指标可以作为混凝土坝运行初期识别险情的标准,具有有效性和可行性。     

马氏转移对数正态模型参数置信区间的Bootstrap估计

马氏状态转移对数正态模型参数的区间估计很难得到。文章提出的Bootstrap法不仅能在Excel环境下克服似然函数关于参数求导以及求导后对随机变量求期望的困难,而且能弥补使用费希尔信息量的倒数I（Θ）^-1低估估计量波动的缺陷,同时能方便地得到估计量的分布和协差阵,为研究模型系数估计的稳定性提供了切实可靠的统计方法。     

改进灰自助法及其在可靠性评定中的应用

极小子样情况下的抽样仿真一直是可靠性仿真评估的热点和难点,目前没有较好的解决办法。运用灰自助法的思想,改进灰自助法的抽样方法克服自助法在极小子样下的重复抽样问题,运用GM(1,1)二次数据拟合模型克服Bootstrap方法在极小子样下仿真结果不可信问题。将改进的灰自助法运用到极小子样可靠性评估中,多个算例表明该方法仿真评估精度较高,并具有较高的可信度和较强的适用性。     

指数分布产品的小样本可靠性参数评估方法

介绍了小样本可靠性参数评估的Bootstrap方法和随机加权法的基本思想,以及指数分布随机样本的Monte Carlo抽样过程;分析了指数分布随机观测样本的平均寿命估计的相对误差,研究了小样本情况下,相对误差的统计特性随样本量的变化关系及其分布情况,并对仿真结果进行了对比分析。     

On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-inflated Poisson model. A frequentist analysis, a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered. In addition, an EM-type algorithm is developed for performing maximum likelihood estimation. Then, the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived. In order to study departures from the error assumption as well as the presence of outliers, residual analysis based on the standardized Pearson residuals is discussed. The relevance of the approach is illustrated with a real data set, where it is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart.