Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky–Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.     

光子计数探测与成像实验装置设计

光子计数探测是微弱光学辐射探测和超快速成像研究的关键技术.利用工作于盖革模式的雪崩光电二极管(APD)结合适当的驱动熄灭电路和NI-PCI高速图像数据采集卡构成实验系统硬件框架,通过Labview和VC++混合编程实现系统的数据采集处理程序和显示界面,设计建立了一套光子计数探测与成像实验装置,详细介绍了实施方案.该实验装置实现了光子计数脉冲信号的探测、处理与显示,为微弱光学辐射信号的探测与超快速成像研究提供了实验平台.     

电机定、转子双排自动叠装硬质合金级进模设计

在对电机定、转子冲片加工工艺分析的基础上,确定了双排错位的排样方案,设计了定、转子冲片同模套冲、自动叠装的级进模.在模具设计中,通过落料转子扭斜机构实现转子自动叠装,应用叠片自动计数分离机构实现了叠片的自动记数.该模具可有效解决定、转子冲片叠铆加上费时、费工、质量低的问题.     

基于改进Hough变换的电梯轿厢内人数统计算法

考虑电梯轿厢内人数的电梯群控调度算法,可以在有限的时间内,找到更优解,对电梯的调度具有重要意义。针对目前基于视觉的人数统计算法正确率不够稳定,速度较慢的问题,提出了一种基于改进Hough变换检测人头轮廓的电梯轿厢内人数统计算法。该算法将标准Hough变换检测圆时的三维空间转换为一维空间,利用圆的几何特性遍历图像数据,通过一维Hough变换进行半径累积,并和阈值进行比较来确定人头轮廓的半径和圆心。结果表明,和用Hough变换检测人头轮廓的传统方法相比,该算法具有速度快,正确率高,抗噪性能好等优点。     

基于卷积神经网络的虫情图像分割和计数方法

为提高虫情图像的分割和计数的准确率,提出了一种基于卷积神经网络的虫情图像分割和计数方法。该方法基于U-Net模型构造了一种昆虫图像分割的模型Insect-Net,将完整的虫情图像和切割后的虫情图像分别输入模型后,提取两者特征进行融合。将融合后的特征输入1个1×1的卷积层得到最终分割结果,再将得到的结果二值化后,采用轮廓检测算法将昆虫目标与背景分离并计数。实验结果表明,该方法在虫情图像中取得了较高的分割正确率和计数正确率,分别为94.4%和89.2%。用深度学习和卷积神经网络的方法有效提高了虫情图像的计数精度,并且为昆虫识别分类提供了大量的无背景数据集。     

基于多视角信息融合的棒材识别计数方法

棒材出厂前要按根数打捆,基于单视角视觉识别计数方法难以处理多个目标之间的互遮挡等问题。文章提出多视角信息融合方法,从2个视角分别捕获目标图像,分别识别,再进行融合,对单视角目标识别信息的补偿与纠错,达到消除互遮挡、准确计数的目的。实践证明,该方法能很好地解决单视角的遮挡目标识别问题,对于因存在遮挡和光照条件限制而难以处理的多目标识别和跟踪问题具有一定的普适性。     

老年人身体平衡能力与坐起- 行走地反力的样本熵 特性的实证研究

坐起-站立是平时生活中最常出现的动作之一。对于下肢功能不断衰弱的老年人而言,坐起-站立过程中常会发生跌倒。文章主要研究的是老年人坐起-站立过程中足底压力的动态特征与身体平衡能力的关系。本次实验在马连洼社区(北京老年人口比例最高的社区之一)进行,项目组共搜集到128例数据,其中男性51人,女性77人,年龄都不小于65岁。利用平衡能力测试对老年人群进行评分,分为正常组、低风险组、中等风险组、高风险组4组。实验时,要求所有的实验对象以最舒适的状态完成坐起-行走动作。左右足的足底压力和力矩数据被记录,并且被样本熵量化。研究结果表明,左右足的足底压力和力矩变量的样本熵的统计特性具有差异,并且在不同老年人群的足底压力和力矩之间也存在差异,尤其以下变量的样本熵在不同组别间存在显著性差异：行走过程中左足在内外侧方向的足底压力(LFx,P＝0.049)、在前后方向的足底压力(LFy,P＝0.025)、在垂直方向上的足底压力(LFz, P＝0.036),行走过程中右足在前后方向上的足底压力(RFy,P＝0.043)、在垂直方向上的足底压力(RFz,P＝0.006)、在内外侧方向上的力矩(RMx,P＝0.041)。以上结果表明,在采集和分析老年人的足底压力数据时应该分开处理。     

从儒家教育思想看人文教育的基本内涵——以《礼记·学记》为诠释中心

以《礼记·学记》为中心的儒家教育思想,对于人文教育的贡献主要表现在三个方面:其一,从尽心知性的精神中表现其宗教情怀;其二,从开而弗达的理论中表现其学求自得;其三,从退息居学的生活中表现其持敬存养。这三者相互联系、相互影响,最后完成了儒家对人文教育的化民成俗功能之建构。     

基于数据库分组技术的决策树算法的研究

提出了一种利用数据库分组记数技术构造决策树的算法,利用数据库系统的结构化查询语言来实现主要的样本统计计算任务。并将构造决策树的过程与相关属性的选择方法有机地结合在一起。使用这些方法和策略,该算法能较快速地生成决策树,与ID3算法相比,在不影响决策树分类准确度的前提下,具有较高的执行效率。     

Distributed Tip Decomposition on Large Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs. It is a popular research topic with wide applications in document clustering, spam group detection, and analysis of affiliation networks. With the explosive growth of the bipartite graph data scale in these scenarios, it is necessary to use distributed methods to realize its effective storage. For this reason, this paper studies the problem of the tip decomposition on a bipartite graph in the distributed environment for the first time. Firstly, a new relay-based communication mode is proposed to realize effective message transmission when the given bipartite graph is decomposed in a distributed environment. Secondly, the Distributed Butterfly Counting (DBC) algorithm and the Distributed Tip Decomposition (DTD) algorithm are designed. In particular, a controllable parallel vertex activation strategy is proposed to solve the problem of memory overflow when DBC decomposes large-scale bipartite graphs. Finally, the message pruning strategy based on vertex priority and message validity pruning strategy are introduced to further improve the efficiency of the algorithm by reducing redundant communication and computing overhead. The experiment is deployed on the high-performance distributed computing platform of the National Supercomputing Center. The effectiveness and efficiency of the proposed algorithms are verified by experiments on several real datasets.