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    Deep learning-based MLCC stacked alignment inspection system and method
    61.
    发明授权
    Deep learning-based MLCC stacked alignment inspection system and method 有权

    公开(公告)号：US12210061B2

    公开(公告)日：2025-01-28

    申请号：US18276450

    申请日：2022-11-01

    Applicant: KOREA UNIVERSITY OF TECHNOLOGY AND EDUCATION INDUSTRY-UNIVERSITY COOPERATION FOUNDATION

    Inventor： Heung-Seon Oh ,  Sung Bin Son ,  Jun Uk Jung ,  Hyun Jae Kim

    IPC: G01R31/3183 ,  G06N3/00 ,  G06N3/08

    Abstract: A deep learning-based MLCC stacked alignment inspection system includes an integrated defect detection unit configured to detect core areas requiring inspection of image data in which a stacked structure is photographed from a semiconductor MLCC chip by using at least one deep learning-based core area detection model, perform segmentation in the detected core areas, determine whether a defect exists according to a standard margin percentage range, and enable defect detection by generating normal and/or defective data based on the determination result, a result analysis unit configured to perform visualization for respective results of the core area detection, segmentation, and defect detection of the integrated defect detection unit, and provide stepwise analysis data for the visualized respective results so as to determine whether to modify corresponding data, and a data storage configured to store the normal and/or defective data, and stepwise analysis data.

    METHOD OF ANALYZING WIRELESS SIGNALS USING MULTI-TASK LEARNING-BASED SPECTRAL ANALYSIS LEARNING MODEL
    65.
    发明公开
    METHOD OF ANALYZING WIRELESS SIGNALS USING MULTI-TASK LEARNING-BASED SPECTRAL ANALYSIS LEARNING MODEL 审中-公开

    公开(公告)号：US20240104428A1

    公开(公告)日：2024-03-28

    申请号：US18077206

    申请日：2022-12-07

    Applicant: Korea University of Technology and Education Industry-University Cooperation Foundation

    Inventor： Won Tae KIM ,  Han Jin KIM ,  Young Jin KIM

    IPC: G06N20/00 ,  H04B17/30

    CPC classification number: G06N20/00 ,  H04B17/30

    Abstract: A wireless signal spectral analysis method using a multi-task learning-based spectral analysis learning model, the wireless signal spectral analysis method may be provided. The analysis method according to an embodiment of the present disclosure may include: receiving a target signal of a target band, obtaining a training dataset through pre-processing of the target signal, performing wireless signal spectral analysis learning using the training dataset, and analyzing the target signal using a trained spectral analysis learning model, wherein the performing of wireless signal spectral analysis learning comprises: configuring task specific layers for respectively performing individual learning for a plurality of tasks to be analyzed and a shared layer for performing shared learning; learning, in the shared layer, correlation data that meets a predefined criterion in the training dataset; and individually learning, in each of the plurality of task specific layers, using an individual dataset required for each task in the training dataset and a result of learning the correlation data.

    Data classification method and device for hierarchical multi-label classification
    67.
    发明授权
    Data classification method and device for hierarchical multi-label classification 有权

    公开(公告)号：US11874856B2

    公开(公告)日：2024-01-16

    申请号：US17927549

    申请日：2021-10-07

    Applicant: KOREA UNIVERSITY OF TECHNOLOGY AND EDUCATION INDUSTRY-UNIVERSITY COOPERATION FOUNDATION

    Inventor： Heung-Seon Oh ,  Sang Hun Im ,  Seong-Ung Jo

    IPC: G06F16/00 ,  G06F16/28 ,  G06F18/241

    CPC classification number: G06F16/285 ,  G06F18/241

    Abstract: A data classification method and a device thereof for hierarchical multi-label classification are proposed. The data classification device includes an encoder configured to vectorize data, a decoder configured to receive the vectorized data from the encoder, and classification data stored in the decoder, wherein the decoder starts classification of the vectorized data on the basis of a first node in the classification data, allocates a plurality of nodes included in the classification data to the vectorized data, and forms partial layers based on the allocated nodes with respect to the vectorized data, and when terminal nodes or temporary nodes are allocated among the nodes, the decoder ends the allocation to the partial layers to which the terminal nodes or the temporary nodes are allocated and allocates final partial layers formed on the basis of the partial layers to the data.

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