公开(公告)号：US20190065968A1

公开(公告)日：2019-02-28

申请号：US15892774

申请日：2018-02-09

Applicant: KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： Sang Soo HAN ,  Byung Chul YEO ,  Chansoo KIM ,  Donghun KIM

IPC: G06N5/04 ,  G06N99/00 ,  G06F17/16 ,  G06F17/30

Abstract: A method of predicting an electronic structure of a material by an electronic apparatus includes receiving user's input data about elements constituting the material; applying the received user's input data to a trained model for estimating a state density of the material; and outputting a first graph representing energy level-by-level state densities of the material output from the trained model, wherein the trained model is trained to generate the first graph based on a plurality of second graphs representing pre-calculated energy level-by-level state densities respectively corresponding to a plurality of pre-input data about elements of various materials and the plurality of pre-input data.

公开(公告)号：US20240426853A1

公开(公告)日：2024-12-26

申请号：US18475100

申请日：2023-09-26

Applicant: KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： Sang Soo HAN ,  Donghun KIM ,  HYUK JUN YOO ,  NAYEON KIM ,  Seung Yong LEE

IPC: G01N35/00 ,  G06Q10/0633

Abstract: Provided is a modular experiment automation system including a main computer, a material synthesis module, and a material analysis module. The main computer interacts with a material synthesis module and a material analysis module. Upon a start request, it provides synthesis instructions for a target material. Once synthesis is complete, it instructs the analysis module to analyze the material. Based on the analysis results, if the error exceeds a threshold, it generates a new synthesis condition and re-initiates synthesis.

公开(公告)号：US20240378881A1

公开(公告)日：2024-11-14

申请号：US18334030

申请日：2023-06-13

Applicant: KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： Donghun KIM ,  Sang Soo HAN ,  Leslie Tiong Ching OW ,  Hyukjun YOO ,  Nayeon KIM

IPC: G06V10/98 ,  G06V10/82

Abstract: There is provided a method and an apparatus for diagnosing an object placement error by using an artificial neural network by which feature data of the (N+1)-th stage among the feature data of a plurality of stages is generated by using any one feature data among a plurality of feature data generated in an operation process of a main network and feature data of the plurality of stages, and a placement error of at least one object is diagnosed by using an artificial neural network of a new structure, and thus, the diagnosis accuracy of a placement error of an object that is difficult to detect an edge, such as a transparent vial, may be greatly increased.