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210 条记录 (耗时 0.025 秒)

    PETROLEUM DRILL-STRING SHOCK ABSORBERS
    1.
    发明公开
    PETROLEUM DRILL-STRING SHOCK ABSORBERS 审中-公开

    公开(公告)号：US20230304361A1

    公开(公告)日：2023-09-28

    申请号：US17700555

    申请日：2022-03-22

    申请人： University of Science and Technology Beijing ,  Shunde Graduate School, University of Science and Technology Beijing

    发明人： Haiping LIU ,  Qi LV ,  Dashan SHEN ,  Donghang HAN ,  Dongmei ZHU

    IPC分类号： E21B17/07

    CPC分类号： E21B17/076

    摘要： A petroleum drill-string shock absorber includes a drill collar nipple, a driving unit, an outer shell, a helical guideway, a drill bit base, a woven metal ring and a vibration-absorbing composite beam, the vibration-absorbing composite beam consists of a sheet metal and a flexible material gasket bonded by an adhesive; the drill collar nipple is connected with the helical guideway; the helical guideway is connected with the drill bit base; the driving unit rotates on the helical guideway; the outer shell is connected with the drill bit base; a metal seal ring is configured to seal a space formed between the outer shell and the drill collar nipple, a sealed space is formed among the drill collar nipple, the outer shell and the drill bit base; the woven metal ring is placed between the helical guideway and the drill bit base configured to absorb an axial impact vibration.

    WIND POWER GENERATION QUANTILE PREDICTION METHOD BASED ON MACHINE MENTAL MODEL AND SELF-ATTENTION
    3.
    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

    申请人： University of Science and Technology Beijing

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

    IPC分类号： G06N3/042 ,  G06N3/084

    CPC分类号： G06N3/042 ,  G06N3/084

    摘要： 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
    4.
    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

    申请人： University of Science and Technology Beijing

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

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

    CPC分类号： H04W72/1263 ,  H04W72/0446 ,  H04W72/542 ,  H04W72/543

    摘要： 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
    10.
    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

    申请人： University of Science and Technology Beijing

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

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

    CPC分类号： H04W72/535 ,  G06N20/00 ,  H04L47/6225 ,  H04W72/23

    摘要： 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.