This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since t...This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since the programmer of the machining operator can easily find some faults inthe NC program visually. But it is difficult to visually detect collisions arnong moving objects such ascutting tools , workpieces and fixtures, a data structure to represent moving objects and an algorithmof collision detection between moving objects are proposed. A moving object can be represented by ahierarchical sphere octree and its motion can be described by a quadratic function of time. A collisionoccurs in the case that the distance between any two sphere centers in the respective two moving ob-jects is equal to the sum of the radii of these two spheres, and the radii of these two spheres are lessthan a given precision. By solving the equations that satisfy the conditions of collision between thespheres recursively , we obtain the time and the position of the collision between these two moving ob-Jects.展开更多
作为多年冻土退化的重要标志,热融湖对其热状态、水文过程、生态环境及冻土工程稳定性具有重要影响。然而,现有的热融湖识别研究受青藏高原复杂地形和下垫面、广泛存在的云雾与冰雪,以及识别方法和影像分辨率本身的制约,导致青藏高原小...作为多年冻土退化的重要标志,热融湖对其热状态、水文过程、生态环境及冻土工程稳定性具有重要影响。然而,现有的热融湖识别研究受青藏高原复杂地形和下垫面、广泛存在的云雾与冰雪,以及识别方法和影像分辨率本身的制约,导致青藏高原小型热融湖存在严重漏提问题。本文基于Sentinel-2影像,提取细小水体边界区分度高的植被红边水体指数(vegetation red edge based water index,RWI),辅以人工目视剔除河流与积雪,实现了青藏高原热融湖的准确识别。在此基础上,应用频率比法评估了环境因子与热融湖发育的相关性。通过叠加各环境因子频率比值的权重积,建立了热融湖的易发性分区。结果表明,RWI在热融湖提取中总体精度为98.97%、制图精度为85.52%、用户精度为83.12%、Kappa系数为0.8118,体现出较高的识别精度。在青藏高原共识别出182668个热融湖,总面积为2456.13 km^(2),总体呈现出“小型热融湖数量多面积占比小,大型热融湖数量少面积占比大”的特征。各环境因子对热融湖影响程度依次排序为坡度>高程>活动层厚度>归一化植被指数>地下冰含量>降水量>年平均地温。热融湖易发性分区显示,低易发区面积占比最大(31.01%),但仅包含4.04%的热融湖,密度为2.01个⋅(100km^(2))^(-1);高与极高易发区面积占比之和仅为25.19%,却集中了84.30%的热融湖,极高易发区热融湖点密度达77.92个⋅(100km^(2))^(-1),是低易发区的38.76倍。展开更多
文摘This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since the programmer of the machining operator can easily find some faults inthe NC program visually. But it is difficult to visually detect collisions arnong moving objects such ascutting tools , workpieces and fixtures, a data structure to represent moving objects and an algorithmof collision detection between moving objects are proposed. A moving object can be represented by ahierarchical sphere octree and its motion can be described by a quadratic function of time. A collisionoccurs in the case that the distance between any two sphere centers in the respective two moving ob-jects is equal to the sum of the radii of these two spheres, and the radii of these two spheres are lessthan a given precision. By solving the equations that satisfy the conditions of collision between thespheres recursively , we obtain the time and the position of the collision between these two moving ob-Jects.
文摘作为多年冻土退化的重要标志,热融湖对其热状态、水文过程、生态环境及冻土工程稳定性具有重要影响。然而,现有的热融湖识别研究受青藏高原复杂地形和下垫面、广泛存在的云雾与冰雪,以及识别方法和影像分辨率本身的制约,导致青藏高原小型热融湖存在严重漏提问题。本文基于Sentinel-2影像,提取细小水体边界区分度高的植被红边水体指数(vegetation red edge based water index,RWI),辅以人工目视剔除河流与积雪,实现了青藏高原热融湖的准确识别。在此基础上,应用频率比法评估了环境因子与热融湖发育的相关性。通过叠加各环境因子频率比值的权重积,建立了热融湖的易发性分区。结果表明,RWI在热融湖提取中总体精度为98.97%、制图精度为85.52%、用户精度为83.12%、Kappa系数为0.8118,体现出较高的识别精度。在青藏高原共识别出182668个热融湖,总面积为2456.13 km^(2),总体呈现出“小型热融湖数量多面积占比小,大型热融湖数量少面积占比大”的特征。各环境因子对热融湖影响程度依次排序为坡度>高程>活动层厚度>归一化植被指数>地下冰含量>降水量>年平均地温。热融湖易发性分区显示,低易发区面积占比最大(31.01%),但仅包含4.04%的热融湖,密度为2.01个⋅(100km^(2))^(-1);高与极高易发区面积占比之和仅为25.19%,却集中了84.30%的热融湖,极高易发区热融湖点密度达77.92个⋅(100km^(2))^(-1),是低易发区的38.76倍。
