盐16井全封闭边界油藏的确定及评价

充分运用试井解释成果，以尽量少的井确定一个油藏的范围及储量面积，加快勘探速度，提高勘探效果，近年来在胜利油田得到了较充分的应用。根据盐16井的电子压力计探边恢复试井资料解释成果，确定了该井沙四段砂砾岩体为全封闭边界油藏，以及具体的形状、储量面积及边界大小，并对此进行了评价，为决策部门提供了准确的勘探依据。通过在盐16井南部钻的一口空井（盐161井），证实了本次试井解释结果的可靠性。     

An Efficient Code-Based Voxel-Traversing Algorithm

The paper considers an efficient approach to traversing a uniformly-subdivided space pierced by a line segment. A voxel, as the basic constituent element of the uniformly subdivided space, is restricted to having the form of a cube. The algorithm works in two steps. In the first step, the so-called Bresenham voxels are identified and, by comparing their position codes, their type of connectivity is determined. To achieve the required connectivity between neighbouring voxels, the second step of the algorithm is applied to find the missing voxels. In this way, the algorithm efficiently switches between face-, edge- and vertex-connectivity. Although the algorithm works with oating-point precision, it is extremely computationally efficient, and tests of speed compared with the Müller, Cleary & Wyvill, Amanatides & Woo, and Zemčik algorithms are described.     

多频段脑功能网络融合的阿尔茨海默病分类

将图论与机器学习方法相结合的阿尔茨海默病计算机辅助中,脑网络的构建大多是基于滤波去噪后的全频段BOLD信号匹配,忽略了不同脑活动信息的差异.因此,本文提出了一种多频段脑功能网络融合模型.首先将离散小波变换应用于BOLD信号中,得到不同频域下的体素信号,而后计算同频信号的相关性,获取不同频段下相关矩阵.而后计算所有矩阵的网络特征,在特征选择后基于SVM对患者进行分类.从实验结果可以看出,分频下的脑功能网络特征与未分频网络相比能在一定程度上提高分类的准确性;体素级网络由于可以更加详细的表达脑网络的变化,其分类效果要优于脑区级.     

真三维成像系统结构设计及GR空间模型分析

本文提出了一种基于下投影的真三维立体成像显示系统。讨论了一种基于空间和时间的三维模型体素构造的新方法——体素铸模法,并推导了四维局部状态空间模型Givone-Roessor,此模型利用状态矢量和输入矢量计算和表征体素变量,模型在某时刻计算的各变量只与相邻的体素或前一时刻有关,减少了计算量。系统机械结构简单轻便,成本低,功耗小;视场角大,除天顶点外,在整个x-y平面以上的半球空间均可成立体像;Givone-Roessor模型计算量小,便于微控制器实现,为真三维立体成像显示提供了一种新方法。     

一种基于数据差分的频域体绘制算法

针对频域体绘制的图像质量问题,提出一种基于数据差分的且能动态提升图像质量的频域体绘制算法。用数据场差分替代原数据场,计算三维数据场的动态调节因子,通过模拟补偿图像的深度感和真实感,动态地调节图像质量。设计了采样点合并等加速技术绘制图像。实验结果表明该算法能提升图像质量,加快了绘制速度。     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Automated online measurement of limestone particle size distributions using 3D range data

Fully automated online measurement of the size distribution of limestone particles on conveyor belt is presented based on 3D range data collected every minute during 13 h of production. The research establishes the necessary analysis and measurement capabilities to facilitate automatic control of rock crushing or particle agglomeration processes to improve both energy efficiency and product quality. 3D data from laser triangulation is used to provide high resolution data of the surface of the piled limestone particles. The 3D data is unaffected by color variation in the material and is not susceptible to scale or perspective distortion common in 2D imaging. Techniques are presented covering; sizing of particles, determination of non-overlapped and overlapped particles, and mapping of sizing results to distributions comparable to sieving. Detailed variations in the product sieve-size are shown with abrupt changes when the size range of the limestone particles is changed.     

基于多尺度空间体元的地学三维建模

提出基于多尺度空间体元(M-S SVV)的真三维可视化实体建模技术,对基于M-S SVV模型的地质体与地质场建模方法及三维可视化技术进行研究,开发三维建模与可视化软件。通过东营坳陷及花沟地区的构造和物性三维建模与可视化应用,证明M-S SVV模型对地学数据有良好的适应性,能够较好地满足地学空间多尺度表达及分析的需求,辅助地质、地球物理建模及分析。     

高分辨真三维显示中的体素化及均匀化方法

为了使基于投影的真三维显示获得径向均匀亮度,并提高有效体素数量,提出一种体素化和均匀化方法.针对体素中轴亮度高和外围亮度低的问题,分析了现有体素采样方法导致体素数量损失过多的原因;然后以二维平面填充算法替代三维体素采样,提出基于径向范围填充的体素化方法,以及基于抖动技术的均匀化方法,以修正了柱空间内体素亮度失真.实验结果证明,文中方法保留了约60％的体显示分辨率,有效体素数量达到2亿个,真三维显示样机能够在室内光环境下显示清晰且均匀的全角度立体影像.     

基于改进空体素跳跃法的光线投射算法

提出一种针对计算统一设备架构(CUDA)存储器的访存优化策略。在此基础上,给出适用于CUDA存储器的改进空体素跳跃法,以减少对空体素采样点的处理,加快基于Phong光照模型的光线投射算法的运行速度。实验结果表明,该算法能在保证图像质量的前提 下,提高图像绘制速度。