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    5G-TSN RESOURCE JOINT SCHEDULING APPARATUS AND METHOD BASED ON DDPG
    11.
    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.

    AI engine-supporting downlink radio resource scheduling method and apparatus
    17.
    发明授权
    AI engine-supporting downlink radio resource scheduling method and apparatus 有权

    公开(公告)号：US11943793B2

    公开(公告)日：2024-03-26

    申请号：US17537542

    申请日：2021-11-30

    Applicant: University of Science and Technology Beijing

    Inventor： Haijun Zhang ,  Wanqing Guan ,  Dong Wang ,  Tongwei Lu ,  Xiangming Wen ,  Keping Long

    IPC: H04W72/50 ,  G06N20/00 ,  H04L47/62 ,  H04W72/23

    CPC classification number: H04W72/535 ,  G06N20/00 ,  H04L47/6225 ,  H04W72/23

    Abstract: An Artificial Intelligence (AI) engine-supporting downlink radio resource scheduling method and apparatus are provided. The AI engine-supporting downlink radio resource scheduling method includes: constructing an AI engine, establishing a Socket connection between an AI engine and an Open Air Interface (OAI) system, and configuring the AI engine into an OAI running environment to utilize the AI engine to replace a Round-Robin scheduling algorithm and a fair Round-Robin scheduling algorithm adopted by a Long Term Evolution (LTE) at a Media Access Control (MAC) layer in the OAI system for resource scheduling to take over a downlink radio resource scheduling process; sending scheduling information to the AI engine through Socket during the downlink radio resource scheduling process of the OAI system; and utilizing the AI engine to carry out resource allocation according to the scheduling information, and returning a resource allocation result to the OAI system.

    MULTI-MODE SMELTING METHOD BASED ON THE CLASSIFICATION SYSTEM OF MOLTEN IRON
    19.
    发明公开
    MULTI-MODE SMELTING METHOD BASED ON THE CLASSIFICATION SYSTEM OF MOLTEN IRON 审中-公开

    公开(公告)号：US20230368021A1

    公开(公告)日：2023-11-16

    申请号：US18183402

    申请日：2023-03-14

    Applicant: UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING

    Inventor： Yanping BAO ,  Ruixuan ZHENG

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: The invention is in the field of iron and steel metallurgy, specifically a method and system for determining the amount of alloy added during the converter tapping process. Given that the LSTM neural network has a strong ability to capture nonlinear relationships, the invention builds an alloy element yield prediction model based on the LSTM neural network. Because different alloy elements have different factors that affect their yield, that is, different model input variables, different LSTM models are established for training. Furthermore, the invention uses integer linear programming to combine the yield prediction results to determine the alloy addition amount. This method not only finds the optimal alloy proportioning scheme quickly, but it also improves the component hit rate and the stability of steel products in the converter steelmaking process, obtains the lowest total cost, effectively reduces alloying costs, and has a good application prospect.

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