Estimation of Life with Piecewise Linear-Quadratic Hazard Rate

Bladder is an important accessory in the curing operation for tire manufacturing. Failure of bladder, during its use, results in scrap of a large amount of tire under curing. This leads to heavy financial loss to the company. In this article, we deal with estimation of life and determination of replacement plan for bladder. The life of bladder has been modeled using a piecewise linear-quadratic hazard function. A computational procedure is proposed for estimation of life. Finally, a replacement plan for bladder is derived.     

A LIS compiler for GCOS-7

This paper describes the implementation of a LIS compiler for GCOS-7. LIS is a high level system implementation language developed at CII-Honeywell Bull during the middle 1970s, and experience with the language and its implementation have largely influenced the design of Ada. The design of the compiler was particularly aimed at efficient code generation. Design decisions concerning the run-time organization in relation to procedure call and separate compilation are discussed. The structure of the compiler is described. The articulation between the different phases of the code generator is emphasized. Experience with the bootstrap is related.     

Bootstrap learning for accurate onset detection

Supervised learning models have been applied to create good onset detection systems for musical audio signals. However, this always requires a large set of labeled training examples, and hand-labeling is quite tedious and time consuming. In this paper, we present a bootstrap learning approach to train an accurate note onset detection model. Audio alignment techniques are first used to find the correspondence between a symbolic music representation (such as MIDI data) and an acoustic recording. This alignment provides an initial estimate of note boundaries which can be used to train an onset detector. Once trained, the detector can be used to refine the initial set of note boundaries and training can be repeated. This iterative training process eliminates the need for hand-labeled audio. Tests show that this training method can improve an onset detector initially trained on synthetic data. Major part of work was done while the first author was at Carnegie Mellon University. Editor: Gerhard Widmer     

Testing linear independence in linear models with interval-valued data

Testing methods are introduced in order to determine whether there is some ‘linear’ relationship between imprecise predictor and response variables in a regression analysis. The variables are assumed to be interval-valued. Within this context, the variables are formalized as compact convex random sets, and an interval arithmetic-based linear model is considered. Then, a suitable equivalence for the hypothesis of linear independence in this model is obtained in terms of the mid-spread representations of the interval-valued variables. That is, in terms of some moments of random variables. Methods are constructed to test this equivalent hypothesis; in particular, the one based on bootstrap techniques will be applicable in a wide setting. The methodology is illustrated by means of a real-life example, and some simulation studies are considered to compare techniques in this framework.     

Bootstrap hypothesis testing for some common statistical problems: A critical evaluation of size and power properties

The construction of bootstrap hypothesis tests can differ from that of bootstrap confidence intervals because of the need to generate the bootstrap distribution of test statistics under a specific null hypothesis. Similarly, bootstrap power calculations rely on resampling being carried out under specific alternatives. We describe and develop null and alternative resampling schemes for common scenarios, constructing bootstrap tests for the correlation coefficient, variance, and regression/ANOVA models. Bootstrap power calculations for these scenarios are described. In some cases, null-resampling bootstrap tests are equivalent to tests based on appropriately constructed bootstrap confidence intervals. In other cases, particularly those for which simple percentile-method bootstrap intervals are in routine use such as the correlation coefficient, null-resampling tests differ from interval-based tests. We critically assess the performance of bootstrap tests, examining size and power properties of the tests numerically using both real and simulated data. Where they differ from tests based on bootstrap confidence intervals, null-resampling tests have reasonable size properties, outperforming tests based on bootstrapping without regard to the null hypothesis. The bootstrap tests also have reasonable power properties.     

Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model

An accuracy measure (mean squared error, MSE) is necessary when small area estimators of linear parameters are provided. Even in the case when such estimators arise from the assumption of relatively simple models for the variable of interest, as linear mixed models, the analytic form of the MSE is not suitable to be calculated explicitly. Some good and widely used approximations are available for those models. For generalized linear mixed models, a rough approximation can be obtained by a linearization of the model and application of Prasad-Rao approximation for linear mixed models. Resampling methods, although computationally demanding, represent a conceptually simple alternative. Under a logistic mixed linear model for the characteristic of interest, the Prasad-Rao-type formula is compared with a bootstrap estimator obtained by a wild bootstrap designed for estimating under finite populations. A simulation study is developed in order to study the performance of both methods for estimating a small area proportion.     

基于嵌入式Bootstrap的主动学习示例选择方法

在Bootstrap示例选择算法的基础上提出一种新的嵌入式Bootstrap算法．该算法适用于一大类主动机器学习中训练示例的选择问题．新算法在保持和原Bootstrap算法相当的训练时间的前提下可得到更典型的训练示例集，从而解决了计算条件对训练集规模的限制，使训练所得预测器具有更高的性能．从理论上分析了新算法的有效性，然后将其与原Bootstrap算法分别应用到基于AdaBoost的正面人脸检测任务中进行对比实验，实验结果与理论分析一致．     

Quantifying reliability uncertainty from catastrophic and margin defects: A proof of concept

We aim to analyze the effects of component level reliability data, including both catastrophic failures and margin failures, on system level reliability. While much work has been done to analyze margins and uncertainties at the component level, a gap exists in relating this component level analysis to the system level. We apply methodologies for aggregating uncertainty from component level data to quantify overall system uncertainty. We explore three approaches towards this goal, the classical Method of Moments (MOM), Bayesian, and Bootstrap methods. These three approaches are used to quantify the uncertainty in reliability for a system of mixed series and parallel components for which both pass/fail and continuous margin data are available. This paper provides proof of concept that uncertainty quantification methods can be constructed and applied to system reliability problems. In addition, application of these methods demonstrates that the results from the three fundamentally different approaches can be quite comparable.     

C8051F410单片机BootLoader的实现

针对目前基于单片机的产品和C8051F410单片机特色,提出了一种使用UART接口进行程序升级的实现方法,并对实现此功能的关键技术点做了相应分析,使程序更加实用、可靠,所有代码使用C语言进行实现.此方法具有一定的普遍适用性,在基本思想不变情况下稍加改变程序代码即可用于其他型号的单片机.