融合小波与2D PCA的贝叶斯人脸识别

提出了融合小波和2DPCA进行贝叶斯人脸识别的方法。对原始图像采用小波分解后,利用2DPCA计算人脸的特征矢量空间。首先对低频子图进行贝叶斯人脸识别,然后对得分前五名的图像再次利用高频子图并行进行识别,通过加权排序得到最后结果。实验表明,与传统的方法相比较,该方法降低了运算量,提高了识别率。     

软件可靠性预测的增强贝叶斯组合模型

针对当前可靠性预测模型的预测精度问题,提出一种增强贝叶斯组合的短期软件可靠性预测模型。该模型以基于小波分解的单个可靠性预测模型作为基本预测模型, 根据当前相邻几个失效时间间隔的预测精度,更新组合模型中各个基本预测模型的权重,解决了贝叶斯组合模型权重计算采用全部历史数据而导致某个基本预测模型权值占主导地位的缺陷,提高了贝叶斯组合模型对软件可靠性的预测精度。实验结果表明,增强贝叶斯组合预测模型的预测精度不仅优于单一的预测方法,而且也优于传统的软件组合预测模型,能显著提高软件可靠性预测的精度和模型对数据的适应性。     

基于CVMD和DBO-SVM的光纤周界安防信号识别方法

为降低光纤周界安防信号中噪声对分类结果的影响,提升信号分类的准确率和运行效率,提出一种融合了相关变分模态分解(Correlation Variational Mode Decomposition, CVMD)、蜣螂算法(Dung Beetle Optimizer, DBO)和支持向量机(Support Vector Machine, SVM)的分类方法。利用CVMD去除原始信号中的噪声分量,并提取去噪后信号的能量、能量熵和峭度作为特征向量。采用DBO算法优化SVM,得到最佳惩罚因子和核函数参数,并构建DBO-SVM分类模型。搭建了基于相位敏感光时域反射(Φ-OTDR)技术的周界安防系统,采集了攀爬、敲击、踩踏和无入侵四类信号。实验结果表明,CVMD-DBO-SVM的分类准确率相比CVMD-PSO-SVM和CVMD-GA-SVM更高,达到了98.75%,同时运行时间更短,综合性能最优。     

考虑边信息的多层贝叶斯需求预测模型

随着工业互联网经济的发展,需求的不确定性日益增大,为提高需求预测的准确性,提出一个考虑边信息的多层贝叶斯需求预测模型(DFSI)。DFSI模型通过构造隐层的网络结构以实现对客户需求更加精确的刻画,该隐层结构主要包含两组参数:一组用于描述客户需求在时间上固有的连续性特征,另一组则用于融合相关的边信息特征。进一步,以贝叶斯推断为理论基础,以最大化后验概率为目标,推导出了DFSI的优化目标,并基于梯度下降方法设计了相应的求解算法。使用京东商城及某制造企业的真实销售数据对提出的模型进行了检验。结果显示,与常用的需求预测模型相比,DFSI能获得更好的预测结果。     

五阶非线性作用下光纤中类明孤子的演化特性

根据含五阶非线性微扰修正项的非线性薛定谔方程,采用变分法,导出了光纤中类明孤子各参数随传输距离演化的方程组,研究了脉宽与距离、啁啾与脉宽之间的关系,并对描述相位和频率的方程组用龙格-库塔算法进行了数值求解.结果表明:五阶非线性使类明孤子的脉宽压缩,可在一定程度上抵消初始啁啾和三阶色散对脉宽的展宽作用;五阶非线性对类明孤子的相位和频率演化特性的影响不明显,因而适度的五阶非线性(γ在0.0001到0.1之间)有利于类明孤子的稳定传输.     

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.     

基于局部变分贝叶斯推断的分布式交互式多模型估计

针对目前部分多模型算法预先设定运动模型转移概率矩阵对状态估计精度的不利影响,本文提出了一种基于局部变分贝叶斯推断的分布式交互式多模型估计算法.不同于传统交互式多模型估计中运动模型转移概率矩阵为先验已知的假设条件,在分布融合估计框架下,首先基于最小化Kullback-Leibler散度准则的递归优化策略实现对运动模型转移概率矩阵的预测与更新;在此基础上,结合变分贝叶斯推断实现对当前时刻目标状态与模型概率的联合估计;最后依据协方差交叉融合策略完成对局部状态估计融合.仿真结果表明:新算法通过对运动模型转移概率矩阵以及模型概率自适应在线估计,有效提升了机动目标的状态估计精度.     

应用Bayes方法进行末敏弹抛射过程可靠性分析

针对末敏弹是几种高新技术相结合的新型弹药．试验费用昂贵．靶场试验次数的特点．采用Bayes方法对其抛射过程的可靠性进行分析。详细分析了末敏弹的抛射过程．利用继承因子和环境因子对其各环节的验前信息进行了处理．并对抛射过程的可靠度进行了数值仿真计算．计算结果可以为下一步进行末敏弹的效能分析提供参考和依据。     

The influences of an anisotropic parabolic potential on the quantum dot qubit* Zhao Cui-Lan(赵翠兰), Cai Chun-Yu(蔡春雨), Xiao Jing-Lin(肖景林)?

To study the influence of an anisotropic parabolic potential(APP)on the properties of a quantum dot(QD)qubit,we obtain the eigenenergies and eigenfunctions of the ground and first excited state of an electron,which is strongly coupled to the bulk longitudinal optical(LO)phonons,in a QD under the influence of an APP by the celebrated Lee–Low–Pines(LLP)unitary transformation and the Pekar type variational(PTV)methods.Then,this kind of two-level quantum system can be excogitated to constitute a single qubit.When the electron locates at the superposition state of its related eigenfunctions,we get the time evolution of the electron’s probability density.Finally,the influence of an APP on the QD qubit is investigated.The numerical calculations indicate that the probability density will oscillate periodically and it is a decreasing function of the effective confinement lengths of theAPPindifferentdirections.Whereasitsoscillatoryperiodisanincreasingoneandwilldiminishwithenhancing the electron–phonon(EP)coupling strength.     

Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection.

Researchers often have expectations about the research outcomes in regard to inequality constraints between, e.g., group means. Consider the example of researchers who investigated the effects of inducing a negative emotional state in aggressive boys. It was expected that highly aggressive boys would, on average, score higher on aggressive responses toward other peers than moderately aggressive boys, who would in turn score higher than nonaggressive boys. In most cases, null hypothesis testing is used to evaluate such hypotheses. We show, however, that hypotheses formulated using inequality constraints between the group means are generally not evaluated properly. The wrong hypotheses are tested, i.e.. the null hypothesis that group means are equal. In this article, we propose an innovative solution to these above-mentioned issues using Bayesian model selection, which we illustrate using a case study. (PsycINFO Database Record (c) 2010 APA, all rights reserved)