中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

2291 records (took 0.037 seconds)

    INTERDEPENDENT CAUSAL NETWORKS FOR ROOT CAUSE LOCALIZATION
    91.
    发明申请
    INTERDEPENDENT CAUSAL NETWORKS FOR ROOT CAUSE LOCALIZATION 有权

    公开(公告)号：US20230069074A1

    公开(公告)日：2023-03-02

    申请号：US17888819

    申请日：2022-08-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Jingchao Ni ,  Zheng Wang ,  Liang Tong

    IPC: G06N3/08 ,  G06F11/34

    Abstract: A method is provided for training a hierarchical graph neural network. The method includes using a time series generated by each of a plurality of nodes to train a graph neural network to generate a causal graph, and identifying interdependent causal networks that depict hierarchical causal links from low-level nodes to high-level nodes to the system key performance indicator (KPI). The method further includes simulating causal relations between entities by aggregating embeddings from neighbors in each layer, and generating output embeddings for entity metrics prediction and between-level aggregation.

    Tracking RFID groups with spatio-temporal proximity
    93.
    发明授权
    Tracking RFID groups with spatio-temporal proximity 有权

    公开(公告)号：US11580317B2

    公开(公告)日：2023-02-14

    申请号：US16817115

    申请日：2020-03-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Mustafa Arslan ,  Mohammad Khojastepour ,  Shasha Li ,  Sampath Rangarajan

    IPC: G06K7/10 ,  G06N3/04

    Abstract: Systems and methods for determining radio-frequency identification (RFID) tag proximity groups are provided. The method includes receiving RFID tag readings from multiple RFID tags. The method includes determining signal strengths of the RFID tag readings. The method includes determining pairs of RFID tags based on the RFID tag readings. The method also includes implementing a twin recurrent neural network (RNN) to determine proximity groups of RFID tags based on distance similarity over time between each of the pairs of the RFID tags.

    CODEBOOK DESIGN FOR BEAMFORMING IN 5G AND BEYOND MMWAVE SYSTEMS
    94.
    发明申请
    CODEBOOK DESIGN FOR BEAMFORMING IN 5G AND BEYOND MMWAVE SYSTEMS 有权

    公开(公告)号：US20230027513A1

    公开(公告)日：2023-01-26

    申请号：US17856305

    申请日：2022-07-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Mohammad Khojastepour ,  Nariman Torkzaban

    IPC: H04B7/0456 ,  H04B7/06

    Abstract: A communications system for hybrid beamforming is provided. The communications system includes a base station encoding data into a plurality of streams, each transmitted through a radio frequency chain. The communication system further includes a beamforming codebook including a set of beamforming codewords. The communications system also includes a beamformer transmitting a given one of the plurality of streams through multiple antennas by adjusting a phase and a gain of symbols of the given one of the plurality of streams for each of the multiple antennas by using a corresponding beamforming coefficient of a given beamforming codeword chosen from the beamforming codebook. A beam and its corresponding beamforming codeword is designed such that the mean squared error between the beam pattern generated with the given beamforming codeword and a given beam pattern is minimized.

    IDENTIFICATION OF FALSE TRANSFORMER HUMMING USING MACHINE LEARNING
    96.
    发明申请
    IDENTIFICATION OF FALSE TRANSFORMER HUMMING USING MACHINE LEARNING 有权

    公开(公告)号：US20230024104A1

    公开(公告)日：2023-01-26

    申请号：US17871862

    申请日：2022-07-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yangmin DING ,  Sarper OZHARAR ,  Yue TIAN ,  Ting WANG

    IPC: G01R31/62 ,  G01H9/00

    Abstract: Systems, and methods for automatically determining false transformer humming when using DFOS systems and methods to determine such humming along with machine learning approach(es) to identify the false transformer humming signal(s) that are transferred to a utility pole without a transformer from a working transformer on another utility pole. Advantageously, our inventive systems and methods employ a customized signal processing workflow to process raw data collected from the DFOS. Our employs a binary classifier that can automatically identify a transformer humming signal from a utility pole with a transformer and simultaneously identify the false humming signal from a utility pole without a transformer.

    REINFORCEMENT-LEARNING BASED SYSTEM FOR CAMERA PARAMETER TUNING TO IMPROVE ANALYTICS
    98.
    发明申请
    REINFORCEMENT-LEARNING BASED SYSTEM FOR CAMERA PARAMETER TUNING TO IMPROVE ANALYTICS 有权

    公开(公告)号：US20220414935A1

    公开(公告)日：2022-12-29

    申请号：US17825519

    申请日：2022-05-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kunal Rao ,  Giuseppe Coviello ,  Murugan Sankaradas ,  Oliver Po ,  Srimat Chakradhar ,  Sibendu Paul

    IPC: G06T7/80 ,  G06T7/00

    Abstract: A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.

    Structural graph neural networks for suspicious event detection
    99.
    发明授权
    Structural graph neural networks for suspicious event detection 有权

    公开(公告)号：US11522881B2

    公开(公告)日：2022-12-06

    申请号：US16992395

    申请日：2020-08-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  Jiaping Gui ,  Haifeng Chen ,  Lei Cai

    IPC: H04L9/40 ,  G06N3/08

    Abstract: A computer-implemented method for graph structure based anomaly detection on a dynamic graph is provided. The method includes detecting anomalous edges in the dynamic graph by learning graph structure changes in the dynamic graph with respect to target edges to be evaluated in a given time window repeatedly applied to the dynamic graph. The target edges correspond to particular different timestamps. The method further includes predicting a category of each of the target edges as being one of anomalous and non-anomalous based on the graph structure changes. The method also includes controlling a hardware based device to avoid an impending failure responsive to the category of at least one of the target edges.

    SELF-LEARNING FRAMEWORK OF ZERO-SHOT CROSS-LINGUAL TRANSFER WITH UNCERTAINTY ESTIMATION
    100.
    发明申请
    SELF-LEARNING FRAMEWORK OF ZERO-SHOT CROSS-LINGUAL TRANSFER WITH UNCERTAINTY ESTIMATION 有权

    公开(公告)号：US20220366143A1

    公开(公告)日：2022-11-17

    申请号：US17723942

    申请日：2022-04-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xuchao Zhang ,  Haifeng Chen

    IPC: G06F40/295 ,  G06N5/04

    Abstract: A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.

Patent Agency Ranking