公开(公告)号：US20220103609A1

公开(公告)日：2022-03-31

申请号：US17549283

申请日：2021-12-13

Applicant: Samsung Electronics Co., Ltd.

Inventor： Di WU ,  Ye SUN ,  Siyuan HUANG ,  Chunbo ZHU ,  Lie ZOU

IPC: H04L65/612 ,  H04L65/60 ,  H04N21/239 ,  H04N21/2187 ,  H04N21/236 ,  H04N21/262 ,  H04N21/231 ,  H04N21/6587

Abstract: A transmission control method and an apparatus of multimedia streaming data are provided. The method includes, when an edge server is to transmit a data packet of a video requested by user equipment to the user equipment, and it is a first time that the edge server transmits the data packet according to the request, filtering, by the edge server, the data packet, and transmitting the filtered data packet to the user equipment, otherwise, directly transmitting the data packet to the user equipment, wherein the video includes an on-demand video and a real-time video, and playing, by the user equipment, the video directly according to the received data packet.

公开(公告)号：US20220207357A1

公开(公告)日：2022-06-30

申请号：US17359919

申请日：2021-06-28

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di WU ,  Michael JENKIN ,  Xue LIU ,  Gregory Lewis DUDEK

IPC: G06N3/08 ,  G06N3/04

Abstract: A method of load forecasting using multi-task deep learning includes obtaining references data corresponding to commodity consuming objects, clustering the commodity consuming objects into clusters based on the obtained reference commodity consumption data; obtaining cluster models based on: reference commodity consumption data, reference environmental data, and reference calendar data; inputting, into the cluster models, present data corresponding to the commodity consuming objects; and predicting, based on an output of the cluster models, a future commodity consumption for the commodity consuming objects. The cluster models include multi-task learning processes having joint loss functions.

公开(公告)号：US20220150786A1

公开(公告)日：2022-05-12

申请号：US17363918

申请日：2021-06-30

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jikun KANG ,  Xi CHEN ,  Di WU ,  Yi Tian XU ,  Xue LIU ,  Gregory Lewis DUDEK ,  Taeseop LEE ,  Intaik PARK

IPC: H04W36/22 ,  H04W28/08 ,  H04W24/02 ,  G06N3/08 ,  G06N3/04

Abstract: Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

公开(公告)号：US20220004941A1

公开(公告)日：2022-01-06

申请号：US16953586

申请日：2020-11-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Di WU ,  Yi Tian Xu ,  Xi Chen ,  Ju Wang ,  Michael Jenkin ,  Hang Li ,  Gregory Lewis Dudek ,  Xue Liu

IPC: G06Q10/04 ,  G06Q50/06 ,  G06N20/20

Abstract: A method, computer program, and computer system are provided for load forecasting. Datasets corresponding to source machine learning models and a target domain base model are identified. A set of forecasting models corresponding to the identified datasets are learned. An ensemble model is determined from the learned set of forecasting models based on gradient boosting. An available resource is allocated based on the ensemble model.

公开(公告)号：US20210105578A1

公开(公告)日：2021-04-08

申请号：US16913493

申请日：2020-06-26

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Xi CHEN ,  Hang Li ,  Chenyi ZHOU ,  Xue LIU ,  Di WU ,  Gregory L. Dudek

IPC: H04W4/029 ,  H04R1/32 ,  G06Q30/02 ,  G06N3/04 ,  G06N3/08

Abstract: A location-aware electronic device is provided. The electronic device trains feature extraction layers, reconstruction layers, and classification layers. The training may be based on a reconstruction loss and/or a clustering loss. The electronic device processes a fingerprint to obtain an augmented fingerprint using randomization based on statistics of the fingerprint. The feature extraction layers provide feature data to both the reconstruction layers and the classification layers. The classification layers operate on the codes to obtain an estimated location label. An application processor operates on the estimated location label to provide a location-aware application result to a person.