基于EMD的非线性信号自适应分析

根据EMD分解原理,提出通过选择平均包络中的突变点确定自适应滤波器截止分量阶,以此构造时空滤波器组进行非线性信号自适应分析的方法,成功应用于分析波音737飞机涡轮轴振动信号。从噪声中自适应地提取随转速变化的信号分量和低频振动分量,与小波分析方法相比,在轴向位移检测中取得了更为满意的效果。该方法不仅改进了涡轮轴振动信号分析方法,也适用于解决其他非线性信号的分析问题。     

汽轮发电机组轴系磁阻尼器抑振方案优选计算分析

采用有限元与轴承及磁阻尼器动特性线性简化模型建立大型汽轮机转子系统不平衡振动响应计算模型;选择典型不平衡状态,采用多种磁阻尼器布置方案对BBC公司汽轮发电机组轴系振动的抑振效果进行分析对比优选,结果表明在中压转子段采用大阻尼参数磁阻尼器抑振效果最佳。     

横向塞曼稳频He－Ne激光器的研究

稳频横向塞曼Ｈｅ－Ｎｅ激光器作为一种新型光源，具有功率大、频差低、易于相位细分的特点，因而能广泛应用于各种精密测量中．本文介绍了我们研制的稳频横向塞曼激光器，实验结果表明，其激光频率稳定度达４．７×１０（－１０），再现性达１．８×１０（－８）．     

化学反应中的非线性现象

本文以“水中花园”和“化学振荡”等实验事实,论述了化学反应中的自然分形和自组织过程等非线性现象.     

动力减振器最优参数的非线性回归和若干问题

本文采用最优化方法求得考虑生系统阻尼的动力减振器最优参数集，并对此参数集进行非线性回归，得到了便于动力减振器最优设计的回归曲线族。本文还对现有的主系统无阻尼时动力减振器最优参数公式进行了讨论，指出了这些公式的局限性，并根据本文的结果给出了应用范围更广泛的改进公式。     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

Teachers’ Knowledge and Beliefs About Child Sexual Abuse

Child sexual abuse is one of the main types of abuse still to be addressed within the field of education, yet the education system itself can serve as a primary tool for its prevention. A better understanding of teachers’ knowledge and beliefs about child sexual abuse will allow us to establish key starting points from which to utilize the system for prevention. Four hundred and fifty teachers participated in this study, completing a questionnaire regarding their knowledge and beliefs about child sexual abuse. The study revealed that over half the teachers, 65.3% (n = 294), had never received any type of training in child sexual abuse education and that the majority were not familiar with methods of identifying child sexual abuse, 90.7% (n = 279). Various mistaken beliefs were identified among the participating teachers, such as pathological profiles of abusers, that the vast majority of child sexual abuse implies violent behavior, and that there cannot be abusers the same age as the victim. These results indicate that knowledge deficiencies do exist about child sexual abuse among teachers and highlight the need for training in this field.     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

互联网环境下学术期刊防范学术不端行为技术研究

利用互联网环境，从技术的角度出发，对一稿多投、抄袭行为、剽窃行为以及捏造行为等学术不端行为的检测与防范进行研究，提出了各种操作上可行的方法。实践证明，运用这些方法，可有效地防范有学术不端行为的文章在学术期刊上发表。     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.