对自举式功率放大器共射工作的分析研究

对有些教材认为互补式功率放大器引入自举电路后输出级变为“共射联接”模式提出不同看法，认为该电路输出级仍为“共集联接”模式。     

A multinomial logistic regression modeling approach for anomaly intrusion detection

Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.     

随机加数U-统计量

本文基于刀切虚拟值提出了对U-统计量的随机加权法。证明了一些渐近性质并讨论了Bootstrap与随机加权法之间的关系。     

Reducing Variance of Committee Prediction with Resampling Techniques

Algorithms for reducing variance in neural network prediction using committee and resampling techniques bootstrap and cross-validation are presented. Their effectiveness is tested on data sets with different levels of noise and on medical diagnosis data sets. The methods are most effective when the noise level in the data is high or the size of the learning set is small, which consequently produces high variance. The algorithms will not be of much help in cases where the error of prediction is mainly due to bias. An increase in bias is observed due to smaller effective learning size in the bootstrap and crossvalidation committee. The impact of increased bias on the performance ranges from negligible to completely undermining the benefit of reducing the variance.     

Developing a multi-access system online

COTAN is a multi-acess system designed to operate on linked KDF9–PDP8 computers. It supports 20 remote teletypes and provides the online user with both remote job entry and interactive capabilities The development of COTAN relied heavily on the continuing availability of online access from an early stage in the project. Access was first obtained through the monitor console on the KDF9 and this was then used to ‘bootstrap’ the implementation of the multi-access facilities. The new software was checked out in a real time, online test bed called an ‘environmental simulator’. This provided a close approximation to the ultimate operating conditions and enabled the system to be brought to a high degree of reliability before being placed in service.     

Evaluating chi-squared automatic interaction detection

We propose a method to compute confidence bounds around the predictions from chi-squared automatic interaction detection (CHAID). These bounds are useful for ex ante target selection as some clusters may or may not overlap. They are also useful for ex post evaluation of the predictive quality of CHAID. The method is illustrated using simulated data.     

基于中位数分布密度的武器系统精度评定方法

根据武器系统精度是在一定空间上弹着点与瞄准点或平均弹着点之间距离的中位数的统计意义,在不对总体分布进行任何假设情况下,采用Bootstrap方法获得再生样本的中位数;利用非参数核密度估计方法确定中位数的分布密度函数;从而获得武器系统精度的分布特性。确定了考虑Bootstrap方法估计稳定性时所需的子样数。     

Estimation of metabolite <Emphasis Type="Italic">T</Emphasis><Subscript>1</Subscript> relaxation times using tissue specific analysis,signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data

Object A novel method of estimating metabolite T ₁ relaxation times using MR spectroscopic imaging (MRSI) is proposed. As opposed to conventional single-voxel metabolite T ₁ estimation methods, this method investigates regional and gray matter (GM)/white matter (WM) differences in metabolite T ₁ by taking advantage of the spatial distribution information provided by MRSI. Material and methods The method, validated by Monte Carlo studies, involves a voxel averaging to preserve the GM/WM distribution, a non-linear least squares fit of the metabolite T ₁ and an estimation of its standard error by bootstrapping. It was applied in vivo to estimate the T ₁ of N-acetyl compounds (NAA), choline, creatine and myo-inositol in eight normal volunteers, at 1.5 T, using a short echo time 2D-MRSI slice located above the ventricles. Results WM-T _1,NAA was significantly (P < 0.05) longer in anterior regions compared to posterior regions of the brain. The anterior region showed a trend of a longer WM T ₁ compared to GM for NAA, creatine and myo-Inositol. Lastly, accounting for the bootstrapped standard error estimate in a group mean T ₁ calculation yielded a more accurate T ₁ estimation. Conclusion The method successfully measured in vivo metabolite T ₁ using MRSI and can now be applied to diseased brain.     

Inference for the Fourth‐Order Innovation Cumulant in Linear Time Series

The rescaled fourth‐order cumulant of the unobserved innovations of linear time series is an important parameter in statistical inference. This article deals with the problem of estimating this parameter. An existing nonparametric estimator is first discussed, and its asymptotic properties are derived. It is shown how the autocorrelation structure of the underlying process affects the behaviour of the estimator. Based on our findings and on an important invariance property of the parameter of interest with respect to linear filtering, a pre‐whitening‐based nonparametric estimator of the same parameter is proposed. The estimator is obtained using the filtered time series only; that is, an inversion of the pre‐whitening procedure is not required. The asymptotic properties of the new estimator are investigated, and its superiority is established for large classes of stochastic processes. It is shown that for the particular estimation problem considered, pre‐whitening can reduce the variance and the bias of the estimator. The finite sample performance of both estimators is investigated by means of simulations. The new estimator allows for a simple modification of the multiplicative frequency domain bootstrap, which extends its considerable range of validity. Furthermore, the problem of testing hypotheses about the rescaled fourth‐order cumulant of the unobserved innovations is also considered. In this context, a simple test for Gaussianity is proposed. Some real‐life data applications are presented.