Factors affecting the voxel-based analysis of diffusion tensor imaging

Diffusion tensor imaging （DTI） provides a unique method to reveal the integrity of white matter microstructure noninvasively. Voxel-based analysis （VBA）, which is a highly reproducible and user-independent tech- nique, has been used to analyze DTI data in a number of studies. Fractional anisotropy （FA）, which is derived from DTI, is the most frequently used parameter. The parameter setting during the DTI data preprocessing might affect the FA analysis results. However, there is no reliable evidence on how the parameters affect the results of FA analysis. This study sought to quantitatively investigate the factors that might affect the voxel-based analysis of FA; these include the interpolation during spatial normalization, smoothing kernel and statistical threshold. Because it is difficult to obtain the true information of the lesion in the patients, we simulated lesions on the healthy FA maps. The DTI data were obtained from 20 healthy subjects. The FA maps were calculated using DTIStudio. We randomly divided these FA maps into two groups. One was used as a model patient group, and the other was used as a normal control group. Simulated lesions were added to the model patient group by decreasing the FA intensities in a specified region by 5 %- 50 %. The model patient group and the normal control groupwere compared by two-sample t test statistic analysis voxel- by-voxel to detect the simulated lesions. We evaluated these factors by comparing the difference between the detected lesion through VBA and the simulated lesion. The result showed that the space normalization of FA image should use the trilinear interpolation, and the smoothing kernel should be 2-3 times the voxel size of spatially normalized FA image. For lesions with small intensity change, FWE correction must be cautiously used. This study provided an important reference to the analysis of FA with VBA method.     

Multi-criteria assessment of offshore wind turbine support structures based on dynamic property optimization

Increasing size of wind turbine and deep water deployment have raised the issue of appropriate selection of the most suitable support structure to make offshore wind energy cost competitive.The paper presents an optimization methodology for decision making process of bottom mounted supports of offshore wind turbines （OWTs） through reasonable engineering attributes derivation.Mathematic models of support structures are reduced by the generalized single-degree-of-freedom theory with relatively fewer structural parameters.Soft-stiff design optimization based on dynamic properties of OWTs is performed for monopile and lattice supports with different wind turbines,water depth and hub height.Attributes of support structures,wind turbines and environment conditions are applied in the multi-criteria decision making method——TOPSIS for benchmarking of those options.The results illustrate the effectiveness of the proposed optimazation methodology combined with economical and environmental attributes together.     

相似语言文本横向处理的研究

针对电子资源短缺少数民族语言自然语言处理问题,提出了借助于大语言大数据,着重语言的相似性交叉处理的方法.该文在本次试验中利用维哈柯语及蒙古语多文种文本7 854条平行语料,分词单位和短句子单位考查分析了同语族语言以及不同族语言之间、同语系不同语族语言之间的相似程度.引用动态规划(DP)算法定量地获取了语言之间的相似性.实验结果显示:在句子阶段,维哈柯语言其相似度为84.3％,蒙族类语言为81.3％;在词阶段,维哈柯语言相似度可达到91.6％,而蒙族类语言相似度可达到87.8％.另外,同语系而不同语族多语言的相似性较低,在词级单位考查其相似程度不超过10％.     

快速路网实时OD预测的时间颗粒度选择

常见的基于实测数据的origin-destination(OD)预测方法分为两类:一类基于历史信息,即根据上一天或上一周同一日相同时段的数据进行预测,简称同比预测法;另一类则是根据同一天相邻时间段的数据预测本时段的OD,简称环比预测法.预测所用基础数据的时段长度称为时间颗粒度.时间颗粒度的大小对OD预测结果的稳定性、准确性具有重要影响.针对上海快速路网,采用ADF单位根检验和KMeans聚类分析方法,研究时间颗粒度对预测结果的影响,提出了时间颗粒度选择的建议,同比预测方法相比环比预测法更容易得出稳定、合理的预测结果,30~60min的时间颗粒度预测效果较好.     

融合标签相似度的 k 近邻 Slope One 算法

Slope One 协同过滤算法被广泛应用于个性化推荐系统中。标签是一种描述项目特性的重要形式,针对Slope One 算法推荐精度不足的问题,将标签信息融合到 Slope One 算法当中。同时参考 k 近邻算法思想,选取阈值过滤后的 k 近邻项目参与平均评分偏差计算,提高计算效率的同时增加预测精度。使用评分相似度和标签相似度作为权重修正线性回归模型。通过线性加权融合预测结果,进一步提升推荐质量。将算法应用于 MovieLens 数据集,与传统加权 Slope One 算法相比,平均绝对偏差下降4．8％,召回率和准确率分别提高32．1％和26．3％。     

圆抛物面薄壳在冲击波载荷下变形的实验研究

目的　研究中心固定支撑的圆抛物面薄壳体在爆炸冲击波载荷作用下的变形效应; 方法　将圆抛物面薄壳体置于裸装药柱产生的爆炸场中,测定在不同冲击波载荷作用下壳体表面的变形量,分析了壳体的变形特点和变形规律;通过相似理论将实验研究结果加以推广; 结果　得到了壳体变形量的有关曲线和关系式;结论　该研究结果可以为雷达天线在爆炸冲击波作用下的变形程度分析提供参数;     

H/H′≤G/G′≤A/A′与H≤G≤A的类似性与相异性

对于Ｋｙ Ｆａｎ 型不等式与调和 几何 算术平均不等式的类似性和相异性作了一些讨论．其目的是,从美学与数学的角度认识这两类不等式,为了进一步研究它们．最后,借助于这些类似性的启示与Ｊ．Ｅ．Ｐｅｃａｒｉｃ 和Ｄ．Ｓｖｔａｎ 的结果给出几个新的不等式     

一类非线性不确定相似组合系统的鲁棒输出跟踪

针对一类具有不确定性的非线性相似组合系统,讨论了鲁棒输出跟踪问题·这类相似组合系统的一个显著特点是其标称子系统具有相同的相对度·充分利用相似结构信息,采用坐标变换和变结构控制方法,将其不确定项分为匹配和不匹配两部分进行处理,得到了一类鲁棒控制器以确保系统输出与参考输出间的误差收敛到原点的一个小邻域内且系统状态保持有界·最后的仿真表明了所得结果的有效性·     

一类具有不确定性的相似组合系统的鲁棒镇定

研究非匹配条件下不确定非线性相似组合大系统,给出其可用结构相似非光滑控制器进行镇定的鲁棒分散控制器设计方案及镇定域的估计方法·这种控制器由线性和非线性部分组成,其中的非线性部分类似于“砰砰”控制,相对于现有结果,新设计的控制器计算量小,比较容易实现·     

基于MATLAB的相似度-遗传神经网络在储层物性预测中的应用

由于储层的非均匀性,传统的方法很难得到真实反映储层特性的结果。采用了遗传算法与BP神经网络相结合,利用遗传算法的全局寻优特点,优化神经网络的连接权值和阀值,从而提高网络的训练精度和预测精度。将相似度的概念引入到测井中,定义了相似度在测井中的计算公式,提出了相似度与遗传神经网络相结合的方法。根据取心井段储层物性与测井信息的关系,选取相应的测井曲线,运用MATLAB中神经网络工具箱建立神经网络模型并训练。实例研究表明,预测准确性较高,且可以有效地控制预测精度,避免了因储层差别大而造成的预测精度降低的现象。