基于属性约简的MPI运行时参数优化

目前MPI实现提供上百种可供调整的运行时参数,但默认的运行时参数不能发挥程序应有的性能。为了给应用程序提供接近最优的运行时参数,本文提出基于粗糙集理论中的属性约简建立优化模型。首先通过属性约简方法为不同基准程序产生参数组合值,再根据未知应用程序的程序特征通过所建立的优化模型为其预测参数值。实验表明,该方法可以有效地为未知应用程序预测参数值,并且比使用默认参数程序性能提升约20%。     

基于HEVC的帧内快速模式选择算法

为了解决高效率视频编码(High Efficiency Video Coding,HEVC)复杂度过高的问题,基于粗粒度模式选择(Rough Mode Decision,RMD)过程提出一种降低编码复杂度的方法。该算法充分利用了RMD候选队列的相似程度和预测模式在方向上的相关性这两个特性,进一步减少了进入率失真优化过程(Rate Distortion Optimization,RDO)的预测模式数量,达到了降低编码复杂度的目的。实验结果表明,该算法在编码质量损失0.5%的情况下节省了近22%的编码时间。     

2006年国际原油价格变化趋势分析

近年来,国际石油价格一直保持高位运行状态。2006年国际石油市场的发展趋势将表现出需求强劲、OPEC影响力进一步减弱、各种不确定性政治和自然事件依然存在、基金积极参与以及美国主宰石油政治的特点,国际油价也因之将保持较高水平。以2002年年末至2006年年初的WTI原油价格数据为基础,构建ARIMA模型并对2006年度的油价走势进行分析和预测。预测结果显示,2006年WTI月平均油价运行区间为(60￣70)美元b/bl。     

多粒度时序特征在离网预测中的应用

电信运营商为了发现可能离网的客户,针对不同的场景研究开发了多种离网预测模型。目前的离网预测模型首先选择一种时间粒度抽取特征,之后使用机器学习算法对抽取的数据建模。这类方法只考虑了模型对分类性能的影响,没有充分考虑数据的作用。针对上述问题,提出一种使用多种时间粒度抽取特征的方法,并尝试在模型训练的不同阶段对不同粒度的特征进行融合。实验结果表明,使用多种粒度抽取特征训练出来的模型性能会明显优于使用单一粒度抽取特征的模型。     

基于排列熵和支持向量机的癫痫发作预测研究

针对癫痫发作给病人带来的巨大伤害,为临床治疗留下足够空余时间,提出一个可以预测癫痫发作的系统模型。对21名癫痫病人进行研究,提取具有较低算法复杂度的排列熵构成特征向量,将其输入支持向量机（support vector machine,SVM）训练出学习模型,用来识别发作期样本,利用投票机制充分考虑病人差异来判断所处状态,最终实现癫痫的实时预测。结果表明,其中81%的发作可以提前平均50多分钟预测到,且具有较低的误报率。为癫痫发作预测系统的理论研究打下坚实基础。     

基于经验统计的弃渣场失稳致灾距预测模型研究

弃渣场失稳滑坡是一种严重的山地灾害,而对其致灾距离的预测对于灾害的防治具有重要意义。通过收集多组山地灾害数据,并经过处理后提出了滑坡碎屑流致灾距的经验预测模型,并基于此,进一步提出了弃渣场失稳滑坡致灾距的经验预测模型,模型考虑了弃渣体内部因素可能造成的影响,并通过数据拟合对模型的适用性进行了验证。其研究成果具有一定的推广价值。     

The prediction of crack growth in bonded joints under cyclic-fatigue loading I. Experimental studies

The performance of adhesively-bonded joints under monotonic and cyclic-fatigue loading has been investigated using a fracture-mechanics approach. The joints consisted of an epoxy film adhesive which was employed to bond aluminium-alloy substrates. The effects of undertaking cyclic-fatigue tests in (a) a ‘dry’ environment of 55% relative humidity at 23°C, and (b) a ‘wet’ environment of immersion in distilled water at 28°C were investigated. In particular, the influence of employing different surface pretreatments for the aluminium-alloy substrates was examined. In addition, single-lap joints were tested under cyclic fatigue loading in the two test environments, and a back-face strain technique has been used which revealed that crack propagation, rather than crack initiation, occupied the dominant proportion of the fatigue lifetime of the single-lap joints. In Part II, the data obtained in the present Part I paper will be employed to predict theoretically the lifetime of the adhesively-bonded single-lap joint specimens.     

山西省孝义市近40年气候变化特征分析

为了深入研究山西省孝义市气候变化特征,采用Mann-Kendall检验、滑动T检验及Morlet小波变换等方法对孝义市1975-2015年气候要素进行趋势估计、突变检验、周期分析及发展趋势预测。结果表明:近40年来,孝义市年平均气温呈显著上升趋势,突变年份为1996年,存在32、9 a长周期;年降雨量呈不显著下降趋势,突变年份为1990年,存在32、6 a长周期;年蒸发量呈显著性上升趋势,突变年份为1997年,存在32、10 a长周期;年平均相对湿度呈显著下降趋势,突变年份为1982、2003年,存在15、32 a长周期。孝义市1975-2015年年代际气候经历了"冷湿-暖湿-暖干"的变化过程,预测全境2017-2030年年代际气候将经历"冷湿-暖干"的变化过程。     

Relationship between chemical degradation and thickness loss of an amine-cured epoxy coating exposed to different UV environments

The relationship between chemical degradation and thickness loss of an unpigmented, non UV-stabilized, crosslinked amine-cured epoxy coating exposed to three UV conditions was investigated. Spin-coated samples having a thickness of approximately 7 μm on an Si substrate were prepared from a stochiometric mixture of a bisphenol A epoxy resin and a tetra-functional amine curing agent. Samples were exposed outdoors and to two accelerated laboratory UV environments. Chemical degradation and thickness loss were measured by transmission Fourier transform infrared spectroscopy (FTIRS) and laser scanning confocal microscopy (LSCM), respectively. In addition, surface roughness and morphological changes were measured by atomic forcemicrosocopy (AFM) and LSCM. Substantial chemical degradation, thickness loss, and morpholocal changes occurred in the exposed films, and the rate of chemical degradation was greater than that due to the thickness loss. This additional chemical loss was attributed to an inhomogeneous degradation process in which nanoscale localized depressions initiate at certain sites on the surface, which then enlarge and deepen with exposure time. The results of this study provide a better understanding of the degradation mechanism and should lead to the development of scientific-based models for predicting the service life of crosslinked amine-cured epoxy coatings. Presented at the 82nd Annual Meeting of the Federation of Societies for Coatings Technology, October 27–29, 2004, in Chicago, IL     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.