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    THIN METAL STRIP CONTINUOUS CASTING METHOD USING MOMENTUM FLOW DISTRIBUTION
    44.
    发明申请
    THIN METAL STRIP CONTINUOUS CASTING METHOD USING MOMENTUM FLOW DISTRIBUTION 有权

    公开(公告)号：US20240367221A1

    公开(公告)日：2024-11-07

    申请号：US18556167

    申请日：2022-07-21

    Applicant: University of Science and Technology Beijing

    Inventor： Cheng Zhou ,  Dongpo Xuan ,  You Zhou ,  Tianliang Jiang ,  Biji Zhu ,  Wenhao Fan ,  Zhihao Zhang ,  Jianxin Xie

    IPC: B22D11/103 ,  B22D11/06

    Abstract: A thin metal strip continuous casting method using momentum flow distribution, comprising the steps of: adjusting the position of a flow distribution device (2), and starting a double-roller thin strip continuous casting apparatus; molten metal (3) forming a uniform sheet-shaped molten metal flow (4) having an initial momentum after the molten metal (3) passes through the flow distribution device; the sheet-shaped molten metal flow entering a molten pool (5) at a superheat degree of 50-100° C. and an initial velocity of 0.5-2 m/s, wherein the flow distribution device is spaced apart from the molten pool; under the action of the initial velocity of the molten metal and in the molten pool, forming a whirlpool, which is adjacent to surfaces of two cooling rollers and has a momentum stirring action; and completing the solidification of the molten metal under the momentum stirring action of the whirlpool along with the rotation of the two cooling rollers. In the method, a whirlpool, which is adjacent to surfaces of cooling rollers and has a momentum stirring action, is formed in a molten pool by means of the kinetic energy of molten metal, such that equiaxed crystals can be prepared when a superheat degree is as high as 50-100° C., and the proportion of equiaxed crystals can be increased to 100%, thereby refining crystal grains and alleviating segregation.

    WIND POWER GENERATION QUANTILE PREDICTION METHOD BASED ON MACHINE MENTAL MODEL AND SELF-ATTENTION
    46.
    发明公开
    WIND POWER GENERATION QUANTILE PREDICTION METHOD BASED ON MACHINE MENTAL MODEL AND SELF-ATTENTION 审中-公开

    公开(公告)号：US20240256829A1

    公开(公告)日：2024-08-01

    申请号：US18243107

    申请日：2023-09-07

    Applicant: University of Science and Technology Beijing

    Inventor： Tianyu HU ,  Huimin MA ,  Xiao ZHANG ,  Hao LIU ,  Kangsheng WANG

    IPC: G06N3/042 ,  G06N3/084

    CPC classification number: G06N3/042 ,  G06N3/084

    Abstract: A wind power generation quantile prediction method based on machine mental model and self-attention includes: using human cognitive decision-making mechanism for reference to construct the machine mental model as the basic framework of WQPMMSA, and then the seasonal power generation rules and intraday power generation trend are encoded into WQPMMSA as the input information of the prediction method, using the self-attention layer to replace the recurrent neural network in the original machine mental model, and establishing the statistical relationship between the seasonal power generation rules and the intraday power generation trend effectively, reducing the long-range forgetting of the original machine mental model-convert the continuous rank probability score in the integral form into a summation form, and using it as a loss function to train WQPMMSA, so that WQPMMSA approaches the optimal quantile prediction result with the highest efficiency. Therefore, accurate quantile prediction of wind power generation is realized.

    5G-TSN RESOURCE JOINT SCHEDULING APPARATUS AND METHOD BASED ON DDPG
    47.
    发明公开
    5G-TSN RESOURCE JOINT SCHEDULING APPARATUS AND METHOD BASED ON DDPG 审中-公开

    公开(公告)号：US20240251399A1

    公开(公告)日：2024-07-25

    申请号：US18395771

    申请日：2023-12-26

    Applicant: University of Science and Technology Beijing

    Inventor： Lei SUN ,  Yuan ZHU ,  Jianquan WANG ,  Wei LI ,  Sha LI ,  Yang ZHANG

    IPC: H04W72/1263 ,  H04W72/0446 ,  H04W72/542 ,  H04W72/543

    CPC classification number: H04W72/1263 ,  H04W72/0446 ,  H04W72/542 ,  H04W72/543

    Abstract: A 5G-TSN resource joint scheduling apparatus includes: a state information acquisition module, a scheduling decision making module, and a configuration module. The state information acquisition module is configured to acquire bottom-layer network information, and process the acquired bottom-layer network information to obtain state information, the bottom-layer network information includes channel information, gate control list information of a TSN domain, and queue information in a base station. The scheduling decision making module is configured to obtain a result of decision making based on the state information output by the state information acquisition module using a DDPG-based reinforcement learning model, the result of decision making includes whether to allocate resources for a current queue and a number of resources actually allocated to the current queue. The configuration module is configured to convert the result of decision making to one or more instructions understandable by the base station to configure the base station.

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