基于Bootstrap方法的密封寿命可靠性评估

针对高可靠、长寿命密封件在小样本试验下的可靠性寿命评估需求,提出一种基于虚拟增广样本和Bootstrap方法的密封寿命小样本数据可靠性评估方法。并以轴用阶梯圈为例,进行寿命可靠性评估。首先通过虚拟增广法将轴用阶梯圈台架寿命试验数据样本数增广至10个,使得样本数满足Bootstrap方法的适用条件;然后再利用Bootstrap方法得到轴用阶梯圈的寿命可靠性评估结果。     

煤矿主提升机平衡尾绳更换工艺的改进

更换煤矿主提升机平衡尾绳是煤矿重大检修项目之一,原施工方案为两条平衡尾绳同时更换,存在较多不合理之处,改进为以新绳带旧绳、单绳依次更换的方案,实现了安全、快捷地更换平衡尾绳,同时减少施工对矿井生产的影响,提高煤矿经济效益。     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization

Large-scale strongly nonlinear and nonconvex mixed-integer nonlinear programming (MINLP) models frequently appear in optimization-based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to much lower computational effort required for the same locally optimal solution.     

饱和尾粉砂边界面模型在FLAC3D中的开发

由于岩土材料的复杂性，FLAC3D软件自带的本构模型不能满足实际数值分析的全部要求。利用FLAC3D预留的UMD接口，在C++的编译环境下实现了一种基于临界状态模型CASM边界面模型的开发。介绍了边界面模型的基本组成，给出了开发的关键步骤。并基于三轴试验结果，利用该模型对饱和尾粉砂静/动力学特性进行了分析，论证了该模型对饱和尾粉砂的适用性。     

M-estimator based Chinese Remainder Theorem with few remainders using a Kroenecker product based mapping vector

The Chinese Remainder Theorem (CRT) explains how to estimate an integer-valued number from the knowledge of the remainders obtained by dividing such unknown integer by co-prime integers. As an algebraic theorem, CRT is the basis for several techniques concerning data processing. For instance, considering a single-tone signal whose frequency value is above the sampling rate, the respective peak in the DFT informs the impinging frequency value modulo the sampling rate. CRT is nevertheless sensitive to errors in the remainders, and many efforts have been developed in order to improve its robustness. In this paper, we propose a technique to estimate real-valued numbers by means of CRT, employing for this goal a Kroenecker based M-Estimation (ME), specially suitable for CRT systems with low number of remainders. Since ME schemes are in general computationally expensive, we propose a mapping vector obtained via Kroenecker products which considerably reduces the computational complexity. Furthermore, our proposed technique enhances the probability of estimating an unknown number accurately even when the errors in the remainders surpass 1/4 of the greatest common divisor of all moduli. We also provide a version of the mapping vectors based on tensorial n-mode products, delivering in the end the same information of the original method. Our approach outperforms the state-of-the-art CRT methods not only in terms of percentage of successful estimations but also in terms of smaller average error.     

引水式水电站变顶高尾水洞水力特性和体型优化

考虑引水式水电站常用的有压尾水洞和变顶高尾水洞两类布置型式,采用基于特征线法的有压管道瞬变流分析模型和适用于明满流过渡过程分析的改进狭缝法模型,分别分析系统的过渡过程特性和尾水洞体型参数的敏感性,研究不同的尾水洞型式下尾水系统的水力特性,进一步揭示了变顶高尾水洞的底宽、洞高和顶坡对尾水管进口最小内水压力的影响规律。结果表明,与增大面积的有压尾水洞比较,变顶高尾水洞是一种可行的布置型式,可有效提高尾水管进口的最小内水压力且水锤过程衰减较快;合理增大变顶高尾水洞底宽、洞高或顶坡均可不同程度地改善尾水管进口最小内水压力,其中顶坡的影响相对明显,为体型优化设计提供了参考依据。     

可调偏迈步式胶带自移机尾的研究

随着掘进工作面自动化程度的提高,掘进巷道速度也在日益加快,而现有的顺槽皮带机机尾移动缓慢且移动时工作量大,需要两三人配合操作,存在一定的安全隐患。针对以上问题,研发可调偏迈步式胶带自移机尾,具有与悬挂二运皮带转载机搭接行程长,可以迈步式前进,自动调偏等功能,同时,可以降低工人的劳动强度,加快巷道掘进速度。     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.