公开(公告)号：US11991531B2

公开(公告)日：2024-05-21

申请号：US17570767

申请日：2022-01-07

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Chengming Hu ,  Xi Chen ,  Ju Wang ,  Hang Li ,  Jikun Kang ,  Yi Tian Xu ,  Xue Liu ,  Di Wu ,  Seowoo Jang ,  Intaik Park ,  Gregory Lewis Dudek

IPC: H04W16/22 ,  G06N3/047 ,  H04L41/14 ,  H04L41/147 ,  H04L41/16 ,  H04W24/02 ,  H04W24/08

CPC classification number: H04W16/22 ,  G06N3/047 ,  H04L41/145 ,  H04L41/147 ,  H04L41/16 ,  H04W24/02 ,  H04W24/08

Abstract: A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

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：US20210241173A1

公开(公告)日：2021-08-05

申请号：US17008218

申请日：2020-08-31

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Hang Li ,  Xi Chen ,  Di Wu ,  Xue Liu

IPC: G06N20/00 ,  H04W4/02 ,  G01P13/00 ,  G01S5/02 ,  G06N5/04

Abstract: A method of location determination with a WiFi transceiver and an AI model includes jointly training, based on various losses: a feature extractor, a location classifier, and a domain classifier. The domain classifier may include a first domain classifier and a second domain classifier. The losses used for training tend to cause feature data from the feature extractor to cluster even if a physical object in an environment has moved after training is completed. Then, the location classifier is able to accurately estimate the position of, for example, a person in a room, even if a door or window has changed from open to close or close to open between the time of training and the time of estimating the person's position.

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.