Industry-university Cooperation Foundation Hanyang University

Hanwha Precision Machinery Co., Ltd.

Deok-Soo Kim

Youngsong Cho

Donguk Kim

DongUk Kim

Joonghyun Ryu

Young Song Cho

Kyungtaek Kim

HyunJin Bang

Kichun Lee

Jeongyeon Seo

Je Hyun Cha

Joong Hyun Ryu

Cheol-Hyung Cho

Mok Won Lee

Cheolhyung Cho

Jae-Kwan Kim

Chan Young Song

Disclosed is a method for predicting collisions and conflicts between multiple moving bodies. A method for predicting and avoiding collisions and conflicts between multiple moving bodies comprises the steps of: creating objects by modeling the shape of each of multiple moving bodies; creating two-dimensional circles by modeling the objects by using size information of the objects; modeling the two-dimensional circles into moving disks by using at least one of the moving speeds of the moving bodies, the monitoring time window for the moving bodies, and the size information of the two-dimensional circles; computing a Voronoi diagram between the moving disks and calculating edges of the Voronoi diagram; and during the monitoring time window for the moving bodies, calculating a flipping event in which at least one of the edges of the Voronoi diagram is converted into a vertex and then converted into another edge, and a collision event by which a collision between a pair of moving disks defining an edge of the Voronoi diagram is predicted, and calculating whether actual collisions occur between moving disks triggering the flipping event and between moving disks triggering the collision event, in chronological order of the occurrence of the flipping event and the collision event.

Method for predicting collision and avoiding conflict between multiple moving bodies

A facility abnormality prediction model generation system includes: a data receiver receiving data of sensors of a facility previously obtained during an operation of the facility; an abnormality notification time predictor detecting a malfunction time of a malfunction of the facility based on the data of the sensors and determining an abnormality notification time for pre-notification of the malfunction of the facility based on the detected malfunction time; an optimal sensor combination calculator generating a chromosome based on the data of the sensors and performing a genetic algorithm using the generated chromosome to calculate an optimal sensor combination which is a combination of sensor data related to the determined abnormality notification time; and a facility abnormality prediction model generator generating a facility abnormality prediction model for the pre-notification of the malfunction of the facility, based on the optimal sensor combination.

System and method for generating facility abnormality prediction model, and computer-readable recording medium storing program for executing the method

There is provided a beta-shape, which is a compact structure for topology among spheres defining a blending surface of a sphere set. There is also provided a method of constructing the beta-shape, comprising: acquiring a Voronoi diagram of spheres; searching for partially accessible Voronoi edges; and obtaining faces of the beta-shape from the partially accessible Voronoi edges. Further, there is provided a method of utilizing the beta-shape to recognize pockets, which comprises the steps of acquiring the beta-shapes, and recognizing the pockets from the beta-shapes.

Beta-shape: compact structure for topology among spheres defining blending surface of sphere set and method of constructing same

Systems and methods for computing three-dimensional (3D) Euclidean Voronoi diagrams are disclosed. For some embodiments, a set of 3D objects is accessed, in which each 3D object is mathematically defined. Thereafter, a Voronoi region associated with each of the 3D objects is computed, thereby resulting in a complete Euclidean Voronoi diagram of the set of 3D objects. In some embodiments, the 3D objects are spheres, each of which is defined by a center and a radius. For other embodiments, the 3D objects are convex objects, each of which is mathematically-definable (e.g., cylinders, sphero-cylinders, etc.). Unlike prior approaches that suggested using a numerical approach to computing the Voronoi diagram, the present disclosure employs mathematical approaches for computing the Euclidean Voronoi diagram, thereby improving efficiency in the computation of the Euclidean Voronoi diagram.

Calculating three-dimensional (3D) Voronoi diagrams

In one embodiment, a system or method involves accessing a database having a macromolecular structure of a protein, the molecular structure being represented as a set of spheres, retrieving the molecular structure of the protein from the database, and mathematically computing a Euclidean Voronoi diagram representing atoms of the protein from the set of spheres.

Systems and methods for characterizing protein structures

Growing community of inventors

Seoul, South Korea

Deok-Soo Kim