DEVICE FOR ANALYZING MATERIAL FROM UNKNOWN SAMPLE BASED ON ARTIFICIAL INTELLIGENCE AND METHOD OF USING THE SAME

    公开(公告)号:US20240047016A1

    公开(公告)日:2024-02-08

    申请号:US17760503

    申请日:2021-12-24

    发明人: Gwan Yeong KIM

    IPC分类号: G16C20/30 G16C20/90 G16C20/70

    CPC分类号: G16C20/30 G16C20/90 G16C20/70

    摘要: The present invention relates to an artificial intelligence-based device for analyzing a material from an unknown sample, which includes a material analysis unit configured to acquire at least three pieces of characteristic analysis data from an unknown sample; a sample classification unit configured to compare the acquired characteristic analysis data with data included in a property database to determine a similarity score and a confidence score, and to store the scores in a sample database; a comprehensive analysis unit configured to learn and verify the characteristic analysis data on the basis of the similarity score and the confidence score according to a predetermined condition to analyze a material; and a data output unit configured to store a material analysis result in an output database, and a method employing the device.

    DEVICE FOR ARTIFICIAL INTELLIGENCE-BASED COMPLEX MATERIALS COMPOSITION-PROCESS AND METHOD OF USING THE SAME

    公开(公告)号:US20240046123A1

    公开(公告)日:2024-02-08

    申请号:US17643000

    申请日:2021-12-24

    发明人: Gwan Yeong KIM

    IPC分类号: G06N5/04 G06N5/022

    CPC分类号: G06N5/04 G06N5/022

    摘要: The present invention relates to an artificial intelligence-based device comprising a data collection unit configured to collect composition-process condition data for a target property input by a user and store the collected condition data in a collection database; an input grade classification unit configured to classify the collected condition data into different input grades according to an input grade determination factor; a training data supply unit configured to store the condition data classified into the input grades in a training database and input condition data of a predetermined high grade in the training database; a model generation unit configured to learn and verify the data input from the training data supply unit and generate a composition-process model; and a data output unit configured to derive one or more composition-process conditions for the target property and store the derived composition-process conditions in an output database.