The growth of a two-phase cloud of a liquid fuel in a stagnant atmosphere is studied using computational fluid dynamic techniques. In order to predict the danger and hazard of such cloud in open atmosphere it is very important to determine the fuel concentration in the cloud, especially in the far field region from the fuel reservoir. The results show that with omission of droplets break up...

We present here the Fast Sampling Plane Filtering (FSPF) algorithm, which reduces the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (called the “plane filtered” points) or points that do not correspond to planes (the “outlier” points). We present a localization algorithm based on an observation mod...

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The consist of those located complex boundary, with similar local textures but different categories, and isolate small hard areas, which largely harm performance segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization...

Motivated by nonlinear elasticity theory, we study deformations that are weakly approximately differentiable, orientation-preserving and one-to-one almost everywhere, and in addition have finite surface energy. This surface energy is a functional E introduced by the authors in a previous paper, and has connections with the theory of currents. In the present paper we prove that E measures exactl...

Cloud computing has increasingly been drawing attention these days. Each big company in IT hurries to get a chunk of meat that promises to be a whopping market in the future. At the same time, information is always associated with security and risk problems. Nowadays, the handling of these risks is no longer just a technology problem, with a good deal of literature focusing on risk or security ...

We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result i...

A new feature point extraction algorithm which is used to match the satellite cloud image feature point is proposed. The new algorithm is proposed by combining corner detection with curvature scale space. The new algorithm can accurately extract the satellite cloud image corner points in different positions and directions. In order to accurately match the corner points of two source images, an ...