基于区域权函数理论的多电极电磁流量计电极设计

基于Shercliff权函数提出区域权函数概念,设计多电极电磁流量计,通过测量管道截面不同位置的弦端电压,计算各区域的轴向平均速度,实现速度分布与体积流量的测量。着重研究了流量计电极设计,选取不锈钢平头方案的多电极电磁流量计,对于阀门下游的非轴对称单相流体积流量测量精度高于±1.0%,倾斜管固—液两相流实验证明:该设计对于两相流非轴对称的速度分布测量具备可靠性。     

动态受限区域内的单纯型连续近邻链查询方法

受限区域内的单纯型连续近邻链查询在空间数据挖掘、数据的相似分析和推理、空间数据库等方面具有重要的作用。为了弥补已有方法的不足,详细研究了动态受限区域内的单纯型连续近邻链查询方法。基于计算几何中的Voronoi图给出了VOR_IN_CRSCNNC算法、VOR_EX_CRSCNNC算法和VOR_DE_CRSCNNC算法。进一步进行了实验比较和分析。理论研究和实验分析表明,所提出的算法在查询过程中减少了数据逐一筛选和判断的冗余计算,在处理空间数据量较大、初始受限区域数据量较多、受限区域形状较为复杂的单纯型连续近邻链查询方面具有较大的优势。     

代数约简的条件信息熵表示及其高效约简算法

给出如何保持正区域不变的语义分析,提出一种修正条件信息熵计算公式,证明保持修正条件信息熵不变与保持正区域不变相互等价。在此基础上,给出代数约简概念的修正条件信息熵表示。给出反例说明修正条件信息熵不具有单调性,导致没法给出自底向上的启发式约简算法,证明了代数协调集中不可删除属性的不可逆性质,提出一种自顶向下直接删除属性的高效约简算法。它从所有条件属性集出发,逐步删除不必要的属性,只需遍历各属性一次,即可保证得到原始决策表的一个代数约简。数值算例和实验验证了该算法的正确性和高效性。     

单幅图像多尺度小波深度提取算法

针对浅景深图像中平滑前景区域深度提取误差大的问题,基于像素点分类思想对深度值进行修正,提出一种基于多尺度小波线索的、可同时面向单幅浅景深图像和广角图像的深度图提取算法.首先使用小波分析法在多个尺度下提取图像深度信息;然后提出自适应分类法并根据尺度与深度变化规律对像素点做深度修正,得到深度图;最后结合区域生长与边缘分割算法对深度图进行区域优化.为了加快深度计算,还提出了快速zerocount法以及多尺度加速法来满足标清视频实时处理要求.实验结果证明,采用文中算法获得的深度图相对深度正确,前景和背景区域深度一致性好.     

京津唐地区城市化对植被物候的影响研究

城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。     

基于视觉注意力模型的红外人体图像兴趣区域提取*

由于红外人体图像所处背景的复杂性,使用阈值分割定位红外人体图像往往把大量的非人体图像一起分离出来,这给后续工作带来了巨大的计算量.研究了红外人体图像的典型特点和人眼视觉判断人体轮廓图像的敏感特征,使用该特征建立了简单的人眼视觉对人体的注意力模型,并构建特征判别概率函数,对待定目标进行概率的估算,最终使用该模型快速地剔除非人体红外图像的干扰,并准确地定位人体兴趣区域.     

螺旋CT的三维空间面皮参数平面化方法*

将三维空间面皮转换为二维平面可以使获取面皮特征、颅骨与面皮的关联信息更方便。通过改进的轮廓线提取方法剔除原始切片冗余数据,对重构得到的面皮数据进行空间坐标系的规范,并以最近邻区域(NNR)划分方法将空间面皮的点云数据点集化,最后结合新划分的平面点数据集的特点提出了线性插补的参数平面化方法和基于链表的参数平面化方法。实验证明两种方法的效果良好,验证了其正确性和可行性。     

Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the med- ical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics （LARFS） was proposed. By combining segmentation algorithm principles for traditional region growing （RG） and robust feature statistics （RFS）, the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And be- cause the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.     

A Thermo-Mechanical Study of Some Epoxy Adhesives Subjected to Combined Dynamic and Static Stresses

The object of the present study was to investigate the effect of superimposed dynamic and static stresses on mechanical and thermal properties of some epoxy adhesives. It was found that combinations of shear creep and torsional oscillations, applied simultaneously to adhesive joints at temperatures within the glassy range of the adhesive, led to strengthening of the joints in shear and to an increase in the glass transition temperature of the adhesive. Similar loading stresses applied at temperatures close to the T_g of the adhesive, led to opposite effects on the above mentioned properties of the joints. The width of the glassy-rubbery transition of the adhesives increased, in the whole range of temperatures used in this study and for all epoxy compositions, as a result of subjecting the joints to superimposed dynamic and static stresses. The broadening of the glass transition was interpreted as a result of the stiffening of polymer network during the combined stressing experiments. A linear relationship was found between the area of endothermal peaks in the first DSC scan of specimens subjected prior to test to superimposed dynamic and static stresses at temperatures below T_g, and the shear strength of the joints. In agreement with this observation and with literature data, a linear relationship was revealed also between the glass transition temperature of the resins (measured in the first DSC scan) and the shear strength of the joints. Based on experimental observations and on some literature information, it was suggested that the strengthening of the joint, as well as the changes in thermal properties of the adhesives, are mainly due to physical processes, such as short-range orientation of network chains and an increase in intermolecular interaction between highly polar sites of the network. The possibility that superimposed stressing led to changes in chemical crosslinking was discussed but it seems that no such reactions occurred.     

认知任务驱动的fMRI脑功能网络构建方法

针对脑功能网络的构建受到特定大脑图谱对兴趣点描述准确度及覆盖度的限制,提出了基于认知任务信息和神经影像数据的脑功能网络构建方法。首先计算fMRI体素对于认知任务的敏感度,然后在此基础上选取兼顾分布均衡和去中心化的大脑兴趣点,从而构建任务驱动的脑功能网络。实验通过在人脸情绪识别认知任务相关的梭状回构造任务驱动的脑功能网络,其度中心性、聚类系数、全局效率、局部效率这四个复杂网络指标均优于典型大脑图谱梭状回中兴趣点构成的网络。结果表明,计算得到的大脑兴趣点具有更强的整合性,更适合用于表征特定认知任务下的脑功能网络。