VISUAL GUIDANCE METHOD FOR USER PLACEMENT IN AVATAR-MEDIATED TELEPRESENCE ENVIRONMENT AND THE SYSTEM THEREOF

    公开(公告)号:US20250104372A1

    公开(公告)日:2025-03-27

    申请号:US18607585

    申请日:2024-03-18

    Abstract: Provided is a visual guidance method and system for user placement in an avatar-mediated telepresence environment that may enhance the quality of avatar placement by recommending a placement for preserving a user's interaction context and the visual guidance method includes specifying an interaction target in a space in a telepresence environment for an interaction between a local space and a remote space; computing a recommendation score for a placement of a local avatar as a maximum interaction feature similarity obtainable with the remote space based on the interaction target; visualizing the recommendation score; and placing the local avatar at an optimal placement in the remote space according to a position of a local user.

    METHOD AND DEVICE FOR MULTI-MATERIAL TOPOLOGY OPTIMIZATION OF ELECTRIC MOTORS FOR DETERMINING OPTIMAL ARRANGEMENT OF PERMANENT MAGNETS

    公开(公告)号:US20250103772A1

    公开(公告)日:2025-03-27

    申请号:US18626563

    申请日:2024-04-04

    Abstract: The present disclosure proposes a method and device for multi-material topology optimization for optimal arrangement of a permanent magnet in an electric motor. The present disclosure relates to multi-material topology optimization for optimal arrangement of a permanent magnet in an electric motor including a stator, a rotor, and at least one permanent magnet provided to the rotor, and may be configured to express multi-materials of each of a plurality of finite elements divided from at least a partial area of the rotor, as a plurality of design variables defined to express multi-material states; and to derive topology optimization for a structure of the permanent magnet in the rotor using the design variables, for at least one of usage minimization of the permanent magnet and torque density maximization for the electric motor.

    DATA PROCESSING ESTIMATING METHOD FOR PRIVACY PROTECTION AND SYSTEM FOR PERFORMING THE SAME

    公开(公告)号:US20250086311A1

    公开(公告)日:2025-03-13

    申请号:US18649182

    申请日:2024-04-29

    Abstract: A data processing estimating method for privacy protection using a statistical estimation block design and a system for performing the method is disclosed. A data processing estimating system for privacy protection includes a block design unit for designing block designs for statistical estimation shared between data providers and data users; a modification data generating unit for generating modification data in a random manner along a conditional distribution for the original data of the above statistical estimation block design; and a data distribution estimating unit for estimating the distribution of the original data using an estimation function based on the statistical estimation block design. Accordingly, data processing techniques and estimation functions are provided by utilizing statistical estimation block designs shared between data providers and data users, thereby preventing leakages of sensitive personal information such as personal photos, purchase records, and locations included in the collected data, it is possible to increase statistical accuracy and communication efficiency while satisfying the goal of privacy protection by preventing leakage of sensitive personal information.

    FEDERATED LEARNING SYSTEM, FEDERATED LEARNING METHOD, AND RECORDING MEDIUM STORING INSTRUCTIONS TO PERFORM FEDERATED LEARNING METHOD

    公开(公告)号:US20250061376A1

    公开(公告)日:2025-02-20

    申请号:US18489087

    申请日:2023-10-18

    Abstract: There is provided a federated learning system. The federated learning system comprises: a central server including a central learning model; and a plurality of client devices, each including a local learning model trained by performing federated learning with the central learning model, wherein the central server is configured to transmit status information of the central learning model to each client device, receive status information of the trained local learning model from each client device, and update the central learning model based on the status information of the trained local learning model, wherein each client device is configured to update the status information of the central learning model to the local learning model, train the local learning model by using individual training data, determine the status information of the trained local learning model, and transmit status information of the trained local learning model to the central server.

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