基于有限障碍的全牛顿步内点算法

针对线性规划问题,文中引入了一种与传统障碍函数不同的新的障碍函数,其在可行域边界上的取值是有限的.沿此有限障碍函数所确定的新的搜索方向,给出了短步长全牛顿步内点算法,结果证明该算法具有目前求解线性规划问题最好的复杂性界.     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

用于高速列车移动网络的资源分配实时算法

误码率高、频繁中断的高速列车移动场景宜采用多宿主容迟网络。但其资源分配计算复杂,实时性较差。基于效用差分法设计初始化过程资源分配算法,以保证过程中每一步的效用单调不减,算法时间复杂度降至O(n)。     

A weighted H1 seminorm regularization method for Fredholm integral equations of the first kind

Many problems in mathematics and engineering lead to Fredholm integral equations of the first kind, e.g. signal and image processing. These kinds of equations are difficult to solve numerically since they are ill-posed. Therefore, regularization is required to obtain a reasonable approximate solution. This paper presents a new regularization method based on a weighted H¹ seminorm. Details of numerical implementation are given. Numerical examples, including one-dimensional and two-dimensional integral equations, are presented to illustrate the efficiency of the proposed approach. Numerical results show that the proposed regularization method can restore edges as well as details.     

Lossless asymmetric single instruction multiple data codec

This report introduces a new lossless asymmetric single instruction multiple data codec designed for extremely efficient decompression of large satellite images. A throughput in excess of 3GB/s allows decompression to proceed in parallel with asynchronous transfers from fast block devices such as disk arrays. This is made possible by a simple and fast single instruction multiple data entropy coder that removes leading null bits. Our main contribution is a new approach for vectorized prediction and encoding. Unlike previous approaches that treat the entropy coder as a black box, we account for its properties in the design of the predictor. The resulting compressed stream is 1.2 to 1.5 times as large as JPEG‐2000, but can be decompressed 100 times as quickly – even faster than copying uncompressed data in memory. Applications include streaming decompression for out of core visualization. To the best of our knowledge, this is the first entirely vectorized algorithm for lossless compression. Copyright © 2011 John Wiley & Sons, Ltd.     

关于《求解LP问题的部分基变量算法》的勘误及补正

文章针对周康等人的《求解LP问题的部分基变量算法》一文进行了分析,指出其文中算法原理及算法步骤存在的一些错误,补正了算法的不完善之处,并通过数值试验验证算法的计算性能。     

基于结构继承的贝叶斯网结构学习优化设计

采用遗传算法建立贝叶斯网络的优化学习结构,一直是贝叶斯网络研究倍受关注的课题.传统遗传算法的个体设计存在需要反复进行无环性检验的问题,降低了进化效率.针对这个问题,提出一种新的个体编码方式.考虑到进化过程中家族得分的可继承性,提出基于家族继承的结构评分改进算法,进而设计相应的改进遗传算法.实验结果表明,改进算法在BN建网精度与效率上都得到明显提升.     

Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel

A novel approach to characterise the model prediction errors using a Gaussian mixture model is proposed. The motivation for this work lies behind many data models that are developed through prediction error minimisation with the assumption of a normal noise distribution. When the noise is non-normal, which may often be the case in complicated data modelling scenarios, the model prediction errors may contain rich information, which can be further exploited for model refinement and improvement. The key contents presented in this paper include: choosing the relevant variables to form the error data, optimising the number of Gaussian components required for the error data modelling, and fitting the Gaussian mixture parameters using an expectation-maximisation algorithm. Application of the proposed method for further model improvement, within the framework of hybrid deterministic/stochastic modelling, is also discussed. Preliminary results on the real industrial Charpy impact energy data for heat-treated steels show its effectiveness for model error characterisation, and the potential for model performance improvement in terms of prediction accuracy as well as providing accurate prediction confidence intervals.