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    Direct inverse reinforcement learning with density ratio estimation
    61.
    发明授权
    Direct inverse reinforcement learning with density ratio estimation 有权

    公开(公告)号：US10896383B2

    公开(公告)日：2021-01-19

    申请号：US15425924

    申请日：2017-02-06

    Applicant: Okinawa Institute of Science and Technology School Corporation

    Inventor： Eiji Uchibe ,  Kenji Doya

    IPC: G06N20/00 ,  G06N7/00 ,  G06K9/62

    Abstract: A method of inverse reinforcement learning for estimating reward and value functions of behaviors of a subject includes: acquiring data representing changes in state variables that define the behaviors of the subject; applying a modified Bellman equation given by Eq. (1) to the acquired data: r ⁡ ( x ) + γ ⁢ ⁢ V ⁡ ( y ) - V ⁡ ( x ) = ⁢ ln ⁢ ⁢ π ⁡ ( y | x ) b ⁡ ( y | x ) , ⁢ ( 1 ) = ⁢ ln ⁢ ⁢ π ⁡ ( x , y ) b ⁡ ( x , y ) - ln ⁢ ⁢ π ⁡ ( x ) b ⁡ ( x ) ,                                                ⁢ ( 2 ) where r(x) and V(x) denote a reward function and a value function, respectively, at state x, and γ represents a discount factor, and b(y|x) and π(y|x) denote state transition probabilities before and after learning, respectively; estimating a logarithm of the density ratio π(x)/b(x) in Eq. (2); estimating r(x) and V(x) in Eq. (2) from the result of estimating a log of the density ratio π(x,y)/b(x,y); and outputting the estimated r(x) and V(x).

    2D discrete fourier transform with simultaneous edge artifact removal for real-time applications
    63.
    发明授权
    2D discrete fourier transform with simultaneous edge artifact removal for real-time applications 有权

    公开(公告)号：US10121233B2

    公开(公告)日：2018-11-06

    申请号：US15746407

    申请日：2016-07-20

    Applicant: OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL CORPORATION

    Inventor： Faisal Mahmood ,  Märt Toots ,  Lars-Göran Wallentin Öfverstedt ,  Bo Ulf Skoglund

    IPC: G06K9/38 ,  G06T5/10 ,  G06T5/00

    Abstract: A method for performing 2-dimensional discrete Fourier transform of a subject image data to be performed in one or more digital processors includes performing 1-dimensional fast Fourier transform on each row of the subject image data and 1-dimensional fast Fourier transform on each column of the subject image, and performing a simplified fast Fourier transform processing on the extracted boundary image without performing column-by-column 1-dimensional fast Fourier transform by: performing 1-dimensional fast Fourier transform only on a first column vector in the extracted boundary image data, using scaled column vectors to derive fast Fourier transform of remaining columns of the extracted boundary image data, and performing 1-dimensional fast Fourier transform on each row of the extracted boundary image data. Then, fast Fourier transform of a periodic component of the subject image data with edge-artifacts removed and fast Fourier transform of a smooth component of the subject image data are derived from results of steps (b) and (c).

    MAGNETIC VECTOR POTENTIAL-BASED LENS
    69.
    发明申请
    MAGNETIC VECTOR POTENTIAL-BASED LENS 有权

    公开(公告)号：US20240429016A1

    公开(公告)日：2024-12-26

    申请号：US18697006

    申请日：2022-09-27

    Applicant: OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL CORPORATION

    Inventor： Makoto Tokoro SCHREIBER ,  Matthias WOLF ,  Cathal CASSIDY

    IPC: H01J37/141 ,  H01J37/26

    Abstract: Techniques are described for a charged particle optical apparatus that includes a loop of solid material that encloses a bore and a wire winding poloidally wrapped around the loop surrounding the bore. A current is applied to the toroidal winding generating a magnetic field inside the loop along a toroidal direction of the loop and generating magnetic vector potential within the bore. When charged particle(s) pass through the bore of the loop, the magnetic vector potential focuses the charged particles based on the focal point of the charged particle optical apparatus.

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