大锻件统计学Chi-square test的研究和应用

根据大锻件生产的特点和统计学的基本理论方法,首次深入浅出地论述了大锻件的Chi-square test,即X2检验,并给出了具体应用示例。该方法可用于大锻件工艺参数的科学分析和生产验证,可用于大锻件质量分析和判断,可帮助逐步建立起大锻件的工序能力,帮助实现稳定并提升大锻件工艺和质量控制水平。

Large Deviations and Moderate Deviations for the Chi-Square Test in Type Ⅱ Error

We study the asymptotics tot the statistic of chi-square in type Ⅱ error. By the contraction principle, the large deviations and moderate deviations are obtained, and the rate function of moderate deviations can be calculated explicitly which is a squared function.

Use of Pearson’s Chi-Square for Testing Equality of Percentile Profiles across Multiple Populations

In large sample studies where distributions may be skewed and not readily transformed to symmetry, it may be of greater interest to compare different distributions in terms of percentiles rather than means. For example, it may be more informative to compare two or more populations with respect to their within population distributions by testing the hypothesis that their corresponding respective 10th, 50th, and 90th percentiles are equal. As a generalization of the median test, the proposed test statistic is asymptotically distributed as Chi-square with degrees of freedom dependent upon the number of percentiles tested and constraints of the null hypothesis. Results from simulation studies are used to validate the nominal 0.05 significance level under the null hypothesis, and asymptotic power properties that are suitable for testing equality of percentile profiles against selected profile discrepancies for a variety of underlying distributions. A pragmatic example is provided to illustrate the comparison of the percentile profiles for four body mass index distributions.

A Simple Chi-Square Statistic for Testing Homogeneity of Zero-Inflated Distributions

Zero-inflated distributions are common in statistical problems where there is interest in testing homogeneity of two or more independent groups. Often, the underlying distribution that has an inflated number of zero-valued observations is asymmetric, and its functional form may not be known or easily characterized. In this case, comparisons of the groups in terms of their respective percentiles may be appropriate as these estimates are nonparametric and more robust to outliers and other irregularities. The median test is often used to compare distributions with similar but asymmetric shapes but may be uninformative when there are excess zeros or dissimilar shapes. For zero-inflated distributions, it is useful to compare the distributions with respect to their proportion of zeros, coupled with the comparison of percentile profiles for the observed non-zero values. A simple chi-square test for simultaneous testing of these two components is proposed, applicable to both continuous and discrete data. Results of simulation studies are reported to summarize empirical power under several scenarios. We give recommendations for the minimum sample size which is necessary to achieve suitable test performance in specific examples.

基于Chi-square检验的分布式网络入侵检测系统

针对网络攻击的新特点,本文提出了一种基于Chi-square检验的分布式网络入侵检测系统模型CTDIDS。设计并实现了一个基于异常检测的入侵分析引擎。通过对网络数据包的分析,运用Chi-square值比较对系统的行为进行检测。与现有的入侵检测方法相比,本文提出的方法具有更好的环境适应性和数据协同分析能力。实验证明,分布式入侵检测系统CTDIDS具有更高的准确性和扩展性。

Chi-Square Distribution: New Derivations and Environmental Application

We describe two new derivations of the chi-square distribution. The first derivation uses the induction method, which requires only a single integral to calculate. The second derivation uses the Laplace transform and requires minimum assumptions. The new derivations are compared with the established derivations, such as by convolution, moment generating function, and Bayesian inference. The chi-square testing has seen many applications to physics and other fields. We describe a unique version of the chi-square test where both the variance and location are tested, which is then applied to environmental data. The chi-square test is used to make a judgment whether a laboratory method is capable of detection of gross alpha and beta radioactivity in drinking water for regulatory monitoring to protect health of population. A case of a failure of the chi-square test and its amelioration are described. The chi-square test is compared to and supplemented by the t-test.

GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System, GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System, SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS/SINS/视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS/SINS/视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

HFACS-MI改进模型在危险化学品仓储事故人因分析中的应用

为了研究危险化学品仓储事故的关键人因,通过人因分析与分类系统-煤矿(Human Factors Analysis and Classification System-Mine,HFACS-MI)改进模型从员工的不安全行为、影响不安全行为的前提条件、监督行为、组织影响和企业外部因素5个层次系统地分析65起危险化学品仓储事故,并运用卡方检验和让步比分析了产生不安全行为的原因和各层次间的内在关系。结果表明,外部管理、组织过程、监督不充分、个人因素、技能差错和违规是导致危险化学品仓储事故发生的重要因素。HFACS-MI改进模型上下层级的人为因素间存在显著的因果关系,且主要的危险化学品仓储事故致因路径共有5条,其中“外部管理因素—组织过程漏洞—监督不充分—个人因素—违规”是最关键的致因路径。改进的HFACS-MI模型可以有效分析危险化学品仓储事故人因因素。

融合反讽语言特征的反讽语句识别模型

反讽是采用内隐的形式来表达情感的一种方法,反讽语句在文字和所想表达的情感上存在着不同,这使得对反讽语句进行情感分类变得更加困难。针对这一现象,提出一种融合反讽语言特征的反讽语句识别模型,通过加入反讽语言特征来提高反讽语句的识别准确率。首先,采用卡方检验算法对反讽语言进行分析并获取语言特征;然后,利用Word2Vec对语言特征进行训练获取语言特征的特征表示,同时使用注意力机制与Bi-GRU(双向门控循环神经单元)模型获取句子的特征表示;最后,将语言特征的特征表示与句子的特征表示进行融合并作为情感分类层的输入,对反讽语句进行识别。与CNN-AT、CNN-Adv、EPSN等3种模型进行对比,实验结果表明,该模型可以有效提高对于反讽语句的识别准确率。

基于统计分析方法对古代玻璃制品的成分探究

利用斯皮尔曼相关系数和卡方检验分析了古代玻璃的成分,得出玻璃类型、纹饰、颜色与表面风化的相关性和差异性;对风化玻璃制品数据借助中位数、平均值、标准差等指标进行对比分析,通过加权平均值预测模型,预测风化前玻璃状态,得出玻璃类型的鉴别方法。利用敏感度系数公式,得到二氧化硅含量增减10%与氧化铅含量比值,说明氧化铅和二氧化硅含量的微量改变不会对类别产生影响。借助相关性分析和数据透视表得出高钾玻璃和铅钡玻璃二氧化硅含量较多,风化前氧化铝、氧化钙、氧化铜含量均较高,钾玻璃风化后化学成分关联性较弱,铅钡玻璃风化后化学成分关联性较强。

北美CHIHUAHUAN荒漠啮齿动物群落动态Ⅲ．变量的频次分布和预测

本文用ｘ￣２方和柯尔莫哥洛夫检验分析了北美Ｃｈｉｈｕａｈｕａｎ荒漠啮齿动物群落６个生态学变量的频次分布，并用移动平均和指数平滑方法拟合了这些变量的动态变化。结果显示：１）结合种群密度、生物量、物种均匀性和生物量均匀性服从正态分布；２）物种数和物种多样性的频次为向右偏斜的分布，无法用常见的理论分布来近似表示；３）单移动平均和双移动平均分别较好地描述了物种多样性和物种均匀性；４）其余４个变量可用单指数平滑来较好的描述。

基于Chi-quare检验与词义分析的试题重复检测算法

针对无纸化考试系统入库试题重复检测问题;提出基于卡方检验与词义分析的试题重复检测算法,首先自动提取试题的特征信息词项,利用卡方检验改进公式进行特征词分析并删除冗余词;其次,结合中文WordNet词典对特征词进行词义分析,并利用Tf-Idf方法计算入库试题的特征词向量与不同题型特征词的余弦相似度;最后,根据所得相似度值判别该试题是否与题库试题重复。实验结果表明,在重复度阀值选取0.8时,算法耗时少、准确性高。

P值大小不等价于差异或相关性大小

目的探讨P值与差异或相关性大小的关系。方法固定样本统计量[均数和标准差、样本率、相关系数、OR(95%CI)],分别模拟独立样本t检验、两组样本率比较的卡方检验、Pearson相关分析、单因素Logistic回归分析的数据,观察不同样本量下P值差异。结果两组样本统计量相同或单组样本统计量一致但样本量不同时,P值可能大于0.05、介于0.001~0.05或小于0.001。结论P值受样本量影响大,其大小不等价于差异或相关性大小。

红外光谱结合统计学方法对纸质包装盒的检验研究

目的建立一种高效准确的红外光谱检验纸质包装盒的研究方法。方法利用傅里叶变换红外光谱仪测得了56个不同类型不同来源的纸质包装盒的红外光谱数据;根据纸盒主要填料的不同,将样品初步分为三大类;利用主成分分析对初步分类后的数据降维,提取出4个主成分,再利用系统聚类将样品最终分为6组,使用K-means聚类算法结合Pearson卡方检验进行验证,与最终分类的结果基本吻合。基于该分组,训练朴素贝叶斯分类、随机森林模型、XGBoost分类3种判别模型,实现对新样品组别的分类预测。结果56个快递包装纸盒样品被分为3类,而后进一步细分为6组,3种判别预测模型均有较高的准确率,其中随机森林模型的准确率最高。结论该方法快速方便地实现了对样品的区分,并且可以实现无损检验,为犯罪现场纸质包装盒的鉴别提供依据,从而为公安侦查工作的开展提供帮助。

船舶修造企业火灾事故特征挖掘与致因分析

为探究船舶修造企业(SRBE)火灾事故的特征和致因,运用数理统计方法研究118起火灾事故,从事故的时空属性、火灾三要素和伤害严重程度进行事故特征挖掘;并基于改进的人因分析与分类系统(HFACS-SRBE)事故致因模型,采用卡方检验、让步比(OR)、克莱姆V系数(Cramer's V)方法,分析致因因素之间的相关性及其强弱。结果表明:14:00—16:00最易发生火灾事故和人员伤亡;火灾事故主要发生地点在船舶区域,占比为33.90%;59.19%的点火源是动火作业;无人员伤害、轻伤和轻微伤、重伤和死亡数量比值为61:54:3。通过相关性及其强弱检验后确定15组相邻层级致因因素存在相关性,并得到26条完整事故致因链。其中,沟通与协调→运行计划不适当→不良心理状态→决策差错是导致船舶修造火灾事故发生关联性强度最大的事故致因链。

关于对景德镇陶瓷知识产权侵权现象及维权对策的研究

2019年3月,国家知识产权局、工信部、工商总局、版权局联合发布了《关于加强陶瓷产业知识产权保护工作的意见》。于此,本文从陶瓷行业相关人群和非相关人群两类视角出发,对景德镇陶瓷知识产权发展现状及陶瓷行业从业人员对维权机制满意度的调查研究,并采用单因素方差分析、决策树分析等方法进一步研究如何保护陶瓷创作者权益和健全维权机制,并提出相应建议。

Practical integrated navigation fault detection algorithm based on sequential hypothesis testing

In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system.The simulation result shows that the algorithm can accurately detect the fault information of global positioning system（GPS）,eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.

基于BIG-WFCHI的微博信息关键特征选择方法

特征选择是用机器学习方法提高转发预测精度和效率的关键步骤,其前提是特征提取.目前,特征选择中常用的方法有信息增益(Information Gain,IG)、互信息和卡方检验(CHI-square test,CHI)等,传统特征选择方法中出现低频词引起的信息增益和卡方检验的负相关、干扰计算等问题,导致分类准确率不高.本文首先针对低频词引起的信息增益和卡方检验的负相关、干扰计算等问题进行研究,分别引入平衡因子和词频因子来提高算法的准确率;其次,根据微博信息传播的特点,结合改进的IG算法和CHI算法,提出了一种基于BIG-WFCHI(Balance Information Gain-Word Frequency CHI-square test)的特征选择方法.实验分析中,本文采用基于最大熵模型、支持向量机、朴素贝叶斯分类器、KNN和多层感知器5种分类器对两个异构数据集进行了测试.实验结果表明,本文提出的方法能有效消除无关特征和冗余特征,提高分类精度,并减少运算时间.

A Modified Chi-Squared Goodness-of-Fit Test

In goodness-of-fit tests,Pearson's chi-squared test is one of most widely used tools of formal statistical analysis.However,Pearson's chi-squared test depends on the partition of the sample space.Different constructions of the partition of the sample space may lead to different conclusions.Based on an equiprobable partition of sample space,a modified chi-squared test is proposed.A method for constructing the modified chi-squared test is proposed.As an application,the proposed test is used to test whether vectorial data come from an uniformity distribution defined on the hypersphere.Some simulation studies show that the modified chi-squared test against different alternative is robust.