MULTI-LAYERED CERAMIC CAPACITOR
    171.
    发明申请

    公开(公告)号:US20250149247A1

    公开(公告)日:2025-05-08

    申请号:US18750189

    申请日:2024-06-21

    Inventor: Junghwan PARK

    Abstract: Provided is a multi-layered ceramic capacitor in which a penetration-type electrode unit, which is manufactured in a cone shape, passes through a plurality of internal electrodes in a collective stack of the internal electrodes and the dielectric layers, so that the capacitor is miniaturized and the electrical reliability thereof is improved.

    METHOD AND APPARATUS WITH NEURAL NETWORK TO MEASURE PROCESS-SEQUENCES SIMILARITIES AND TRAINING THEREOF

    公开(公告)号:US20250149124A1

    公开(公告)日:2025-05-08

    申请号:US18890610

    申请日:2024-09-19

    Abstract: A training method for similarity measurement and a measuring apparatus for similarity measurement and operating method thereof are provided. A method, performed by a computing device, includes: based on a first training parameter, embedding vectors of first processes included in a first process-sequence and embedding vectors of second processes included in a second process-sequence, respectively; based on a second training parameter, mapping first embedding vectors respectively corresponding to the first processes to second embedding vectors respectively corresponding to the second processes; and based on a result of the mapping, determining a similarity score indicating a similarity between the first process-sequence and the second process-sequence.

    DISPLAY DEVICE FOR REDUCING POWER CONSUMPTION, AND METHOD FOR CONTROLLING SAME

    公开(公告)号:US20250148957A1

    公开(公告)日:2025-05-08

    申请号:US19005481

    申请日:2024-12-30

    Abstract: A display device including: a display; and at least one processor connected to the display and configured to control the display device, where the at least one processor is configured to: identify an input image as a plurality of areas, identify a type of each of the plurality of areas by identifying at least one of the plurality of areas as a focus area and by identifying other areas of the plurality of areas as a background area, obtain a global tone mapping curve (TMC) for the input image based on a pixel level reduction amount corresponding to a target power consumption reduction amount, allocate a power consumption reduction amount to each of the plurality of areas based on the type of each of the plurality of areas, obtain a local tone mapping curve for each of the plurality of areas.

    IMAGE PROCESSING DEVICE AND IMAGE PROCESSING SYSTEM USING LOOK-UP TABLE WITH EXCLUSIVE OR OPERATION APPLIED

    公开(公告)号:US20250148954A1

    公开(公告)日:2025-05-08

    申请号:US18786208

    申请日:2024-07-26

    Inventor: Kiwoong Eom

    Abstract: An example image processing device includes a first memory comprising a first look-up table, a second memory comprising a second look-up table, and a third memory comprising a third look-up table, a first processor and a second processor that are configured to output predetermined color values based on input color values using at least one of the first to third look-up tables, and a control logic circuit configured to control at least a portion of the first memory, the second memory, and the third memory. The control logic circuit is configured to, in response to receiving a read request for the first memory from each of the first and second processors, transmit data in the first look-up table to the first processor and to transmit data, obtained by performing an XOR operation on data in the third look-up table and data in the second look-up table, to the second processor.

    METHOD AND APPARATUS WITH MULTI-FEATURE OBJECT DETECTION

    公开(公告)号:US20250148800A1

    公开(公告)日:2025-05-08

    申请号:US18636331

    申请日:2024-04-16

    Abstract: An object detection method and an object detection apparatus for detecting an object based on multi-features are provided. The object detection method includes: obtaining first-sensor data from a first sensor and obtaining second-sensor data from a second sensor, wherein the first sensor is a different type of sensor than the second sensor; extracting a first feature from the first-sensor data and extracting a second feature from the second-sensor data; determining a target feature-type by inputting the first and second features to a feature-type selection model which, based thereon, predicts the target feature-type; determining a target feature to be used for object detection according to the determined target feature-type; and determining an object detection result based on the determined target feature.

    ELECTRONIC DEVICE AND IMAGE PROCESSING METHOD THEREFOR

    公开(公告)号:US20250148580A1

    公开(公告)日:2025-05-08

    申请号:US19016880

    申请日:2025-01-10

    Inventor: Daesung CHO

    Abstract: Disclosed is an electronic device that obtains a focus map based on importance information per region included in an input image, obtains reliability information per region of the focus map based on at least one of brightness information or contrast information of the input image and information included in the focus map, identifies sensitivity information of the focus map according to each of at least one type of image quality processing, and image-quality processes the input image according to the at least one type of image quality processing based on the focus map, the reliability information per region of the focus map, and the sensitivity information; and control the display to display the image-quality processed input image.

    ELECTRONIC DEVICE FOR TRAINING NEURAL NETWORK MODEL PERFORMING IMAGE ENHANCEMENT AND CONTROLLING METHOD THEREOF

    公开(公告)号:US20250148575A1

    公开(公告)日:2025-05-08

    申请号:US19018891

    申请日:2025-01-13

    Abstract: An electronic device includes memory storing instructions; and one or more processors configured to execute the instructions to obtain first loss values by inputting a first training image into neural network models; identify a smallest first loss value from among the first loss values; identify the first training image as being in a first training image group for a first neural network model corresponding to the smallest first loss value; obtain second loss values by inputting a second training image into the neural network models; identify a smallest second loss value from among the second loss values; identify the second training image as being in a second training image group for a second neural network model corresponding to the smallest second loss value; train the first neural network model based on the first training image group; and train the second neural network model based on the second training image group.

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