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    DEVICE AND METHOD FOR PROCESSING IMAGE
    21.
    发明申请
    DEVICE AND METHOD FOR PROCESSING IMAGE 审中-公开
    用于处理图像的设备和方法

    公开(公告)号：US20160029027A1

    公开(公告)日：2016-01-28

    申请号：US14807443

    申请日：2015-07-23

    Applicant: Samsung Electronics Co., Ltd. ,  Korea University Of Technology And Education Industry-University Cooperation Foundation

    Inventor： Sriharsha KATAMANENI ,  Jae-Won CHOI ,  Kang-Sun CHOI ,  Jae-Hun CHO ,  Trang VU ,  Yong-Man LEE ,  Dong-Kyoon HAN

    IPC: H04N19/176 ,  H04N19/196

    CPC classification number: H04N19/176 ,  H04N19/103 ,  H04N19/11 ,  H04N19/12 ,  H04N19/134 ,  H04N19/196 ,  H04N19/59 ,  H04N19/65 ,  H04N19/94

    Abstract: An apparatus and a method for applying an independent compression mode for encoding on each data block constituting an image frame in an encoder provided in an image processing device are provided. To that end, at least one data block is encoded based on each of a plurality of specified compression modes, and at least one data block corresponding to each of the plurality of specified compression modes is reconfigured based on at least in part on each of the plurality of specified compression modes. An inter-data difference corresponding to each of the plurality of specified compression modes is determined based on the at least one data block and a data block obtained by reconfiguring the at least one data block, and at least one compression mode is selected from the plurality of specified compression modes based on at least in part on each of the inter-data difference.

    Abstract translation: 提供了一种用于在设置在图像处理装置中的编码器中对构成图像帧的每个数据块应用用于编码的独立压缩模式的装置和方法。 为此，基于多个指定压缩模式中的每一个对至少一个数据块进行编码，并且至少部分地基于多个指定压缩模式中的每一个来重新配置与多个指定压缩模式中的每一个对应的至少一个数据块 多个指定的压缩模式。 基于至少一个数据块和通过重新配置至少一个数据块获得的数据块来确定与多个指定压缩模式中的每一个相对应的数据间差异，并且从多个数据块中选择至少一个压缩模式 至少部分地基于每个数据间差异来指定压缩模式。

    JOINT APPARATUS FOR ROBOT
    24.
    发明申请
    JOINT APPARATUS FOR ROBOT 有权

    公开(公告)号：US20250058458A1

    公开(公告)日：2025-02-20

    申请号：US18935096

    申请日：2024-11-01

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

    Inventor： Yong Jae KIM ,  Seong Ho YUN ,  Jae Hyun PARK

    IPC: B25J9/10

    Abstract: A robot joint device having an upper arm unit including a first rotating pulley, a second rotating pulley and a third rotating pulley sequentially arranged in one direction, a forearm unit including a fixing pulley configured to rolling-contact the third rotating pulley, a connection unit including a first pivot shaft passing through the third rotating pulley and being connected to the upper arm unit, and a second pivot shaft passing through the fixing pulley and being connected to the forearm unit, a first cable unit wound around the first rotating pulley in a first direction, bent between the second rotating pulley and the third rotating pulley, and fixed to the fixing pulley, and a second cable unit wound around the first rotating pulley in a second direction opposite to the first direction, bent between the second rotating pulley and the third rotating pulley, and fixed to the fixing pulley.

    METHOD AND APPARATUS FOR OPERATING MULTI-TASK LEARNING MODEL
    25.
    发明申请
    METHOD AND APPARATUS FOR OPERATING MULTI-TASK LEARNING MODEL 有权

    公开(公告)号：US20240378497A1

    公开(公告)日：2024-11-14

    申请号：US18447675

    申请日：2023-08-10

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

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

    IPC: G06N20/00

    Abstract: The operation method of a multi-task learning model according to an embodiment of the present disclosure may include: obtaining a common feature vector of a previous step; receiving input data corresponding to a current task executed in a current step from among a plurality of tasks; extracting a common feature vector of the current step based on the common feature vector of the previous step and the input data corresponding to the current task; extracting an output feature vector corresponding to the current task based on the common feature vector of the current step; and outputting output data corresponding to the current task based on the output feature vector corresponding to the current task.

    Gravity compensation assembly and robot waist structure including same
    27.
    发明授权
    Gravity compensation assembly and robot waist structure including same 有权

    公开(公告)号：US12083672B2

    公开(公告)日：2024-09-10

    申请号：US17404396

    申请日：2021-08-17

    Applicant: Korea University of Technology and Education Industry-University Cooperation Foundation ,  NAVER LABS CORPORATION

    Inventor： Yong Jae Kim ,  Seong Ho Yun

    IPC: B25J19/00 ,  B25J17/00

    CPC classification number: B25J19/0016 ,  B25J17/00 ,  B25J19/002

    Abstract: A gravity compensation assembly including a main frame having one side connected to a weight body to be supported, and the other side where an inner rotation portion and an outer rotation portion spaced apart from each other and having rotation axis in an x-axis direction are coupled, an auxiliary frame having one side rotatably connected to the inner rotation portion of the main frame, and the other side where an auxiliary rotation portion having a rotation axis in the x-axis direction is coupled, and an elastic force providing having one side rotatably connected to the outer rotation portion of the main frame, and the other side coupled to the auxiliary rotation portion of the auxiliary frame, and configured to perform load compensation by an elastic force when a center of gravity changes as a relative angle of the main frame and the auxiliary frame changes may be provided.

    DEEP LEARNING-BASED MLCC STACKED ALIGNMENT INSPECTION SYSTEM AND METHOD
    30.
    发明公开
    DEEP LEARNING-BASED MLCC STACKED ALIGNMENT INSPECTION SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20240103076A1

    公开(公告)日：2024-03-28

    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

    CPC classification number: G01R31/318307 ,  G01R31/318342

    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.

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