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    REGRESSION-BASED PLANT LIGHT ENVIRONMENT-CARBON SEQUESTRATION BENEFIT CURVE DETERMINATION METHOD AND SYSTEM, AND MEDIUM
    3.
    发明申请
    REGRESSION-BASED PLANT LIGHT ENVIRONMENT-CARBON SEQUESTRATION BENEFIT CURVE DETERMINATION METHOD AND SYSTEM, AND MEDIUM 有权

    公开(公告)号：US20250036710A1

    公开(公告)日：2025-01-30

    申请号：US18346800

    申请日：2023-07-04

    Applicant: Beijing Forestry University

    Inventor： Fei Li ,  Zhandong Li ,  Xiaonan Yu ,  Weiru Yang

    IPC: G06F17/11 ,  G06F17/18

    Abstract: The present disclosure belongs to the technical field of plant photoresponse and discloses a regression-based plant light environment-carbon sequestration benefit (a light response curve) determination method and system, and a medium. The method includes: performing data regression analysis by using various models; verifying data regression equations corresponding to the respective models; selecting a double hyperbolic curve regression model as an optimal one of the models; measuring red-blue light source by using a Li6400XT photosynthesis system; obtaining a light response curve through regression of the double hyperbolic curve; and obtaining corresponding formulas of light compensation (LCP) and a light saturation point (LSP) through regression. According to the present disclosure, a new double hyperbolic curve regression model is first adopted to fit the light response curve, so as to construct a more accurate photoresponse model.

    GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM AND METHOD OF OPERATING THE SAME
    4.
    发明申请
    GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM AND METHOD OF OPERATING THE SAME 有权

    公开(公告)号：US20250021723A1

    公开(公告)日：2025-01-16

    申请号：US18773402

    申请日：2024-07-15

    Applicant: Incucomm, Inc.

    Inventor： Randal Allen ,  Eric Haney ,  Russell Shayne Wenzel

    IPC: G06F30/20 ,  G06F17/11

    Abstract: A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for a commercial operations system to provide aggregated system requirements and measures of merit, and receiving design parameters of the commercial operations system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the commercial operations system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.

    UTILIZING A DEPTH PREDICTION MACHINE LEARNING MODEL TO GENERATE COMPRESSED LOG DEPTH MAPS AND MODIFIED DIGITAL IMAGES
    6.
    发明申请
    UTILIZING A DEPTH PREDICTION MACHINE LEARNING MODEL TO GENERATE COMPRESSED LOG DEPTH MAPS AND MODIFIED DIGITAL IMAGES 有权

    公开(公告)号：US20250014201A1

    公开(公告)日：2025-01-09

    申请号：US18887334

    申请日：2024-09-17

    Applicant: Adobe Inc.

    Inventor： Jianming Zhang

    IPC: G06T7/593 ,  G06F17/11 ,  G06N20/00

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and/or implementing machine learning models utilizing compressed log scene measurement maps. For example, the disclosed system generates compressed log scene measurement maps by converting scene measurement maps to compressed log scene measurement maps by applying a logarithmic function. In particular, the disclosed system uses scene measurement distribution metrics from a digital image to determine a base for the logarithmic function. In this way, the compressed log scene measurement maps normalize ranges within a digital image and accurately differentiates between scene elements objects at a variety of depths. Moreover, for training, the disclosed system generates a predicted scene measurement map via a machine learning model and compares the predicted scene measurement map with a compressed log ground truth map. By doing so, the disclosed system trains the machine learning model to generate accurate compressed log depth maps.

    Lattice Boltzmann Based Solver for High Speed Flows
    8.
    发明申请
    Lattice Boltzmann Based Solver for High Speed Flows 有权

    公开(公告)号：US20250005234A1

    公开(公告)日：2025-01-02

    申请号：US18826827

    申请日：2024-09-06

    Applicant: Dassault Systemes Americas Corp.

    Inventor： Pradeep Gopalakrishnan ,  Raoyang Zhang ,  Hudong Chen

    IPC: G06F30/25 ,  G06F17/11 ,  G06F30/23 ,  G06F30/28 ,  G06F30/367 ,  G06F111/08 ,  G06F111/10 ,  G06F113/08

    Abstract: Techniques for simulating fluid flow on a computer that involve a stable entropy solver are described. The techniques include simulating activity of a fluid across a mesh, the activity of the fluid being simulated so as to model movement of particles across the mesh, storing, in a computer accessible memory, a set of state vectors for each mesh location in the mesh, each of the state vectors comprising a plurality of entries that correspond to particular momentum states of possible momentum states at a corresponding mesh location, simulating a time evolution of entropy of the flow by collecting incoming set of distributions from neighboring mesh locations for the collision operation, calculating by the computer scalar values in each location, determining outgoing distributions as a product of the collision operation and addition of a heat source, and modifying the flow by the computer performing for a time interval, an advection of the particles to subsequent mesh locations.

    DATA PROCESSING
    10.
    发明申请
    DATA PROCESSING 有权

    公开(公告)号：US20240427836A1

    公开(公告)日：2024-12-26

    申请号：US18824400

    申请日：2024-09-04

    Applicant: Alipay (Hangzhou) Information Technology Co., Ltd.

    Inventor： Tianyi Li ,  Wuqiong Pan ,  Tingting Li ,  Tao Wei ,  Zhongtian Qian

    IPC: G06F17/11

    Abstract: In an example data processing method, a data processing task is received, which includes a to-be-processed non-polynomial function and to-be-processed data corresponding to an independent variable of the non-polynomial function. A first linear transformation is performed on the to-be-processed data, so that an independent variable value corresponding to data obtained after the first linear transformation falls within a fitting domain of definition. The fitting domain of definition is an interval selected from a domain of definition of the independent variable of the non-polynomial function. A fitting polynomial function value is obtained based on the data obtained after the first linear transformation. The fitting polynomial is obtained by performing Chebyshev series fitting on the non-polynomial function in the fitting domain of definition. A second linear transformation is performed on the fitting polynomial function value based on the first linear transformation, and a value of the non-polynomial function is obtained.

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