公开(公告)号：US20240214414A1

公开(公告)日：2024-06-27

申请号：US18359389

申请日：2023-07-26

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Dongjie Wang

IPC: H04L9/40 ,  H04L41/0631 ,  H04L41/16

CPC classification number: H04L63/145 ,  H04L41/0631 ,  H04L41/16

Abstract: A computer-implemented method for identifying attack origins is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, backtracking from an attack detection point, via an incident backtrack and system recovery component, by using the learned causal graph to identify an attack origin when an intrusion or attack occurs, and displaying data relating to the attack origin on a visualization display for user analysis.

公开(公告)号：US12001513B2

公开(公告)日：2024-06-04

申请号：US17522226

申请日：2021-11-09

Applicant: NEC Laboratories America, Inc.

Inventor： Giuseppe Coviello ,  Yi Yang ,  Srimat Chakradhar

IPC: G06V10/00 ,  G06F9/50 ,  G06F18/21 ,  G06N3/08 ,  G06V10/94 ,  G06V20/40

CPC classification number: G06F18/217 ,  G06F9/5027 ,  G06N3/08 ,  G06V10/94 ,  G06V20/46

Abstract: A method for implementing a self-optimized video analytics pipeline is presented. The method includes decoding video files into a sequence of frames, extracting features of objects from one or more frames of the sequence of frames of the video files, employing an adaptive resource allocation component based on reinforcement learning (RL) to dynamically balance resource usage of different microservices included in the video analytics pipeline, employing an adaptive microservice parameter tuning component to balance accuracy and performance of a microservice of the different microservices, applying a graph-based filter to minimize redundant computations across the one or more frames of the sequence of frames, and applying a deep-learning-based filter to remove unnecessary computations resulting from mismatches between the different microservices in the video analytics pipeline.

公开(公告)号：US20240137086A1

公开(公告)日：2024-04-25

申请号：US18483931

申请日：2023-10-10

Applicant: NEC Laboratories America, Inc.

Inventor： Mohammad Khojastepour ,  Nariman Torkzaban

IPC: H04B7/06 ,  H04B7/08

CPC classification number: H04B7/0617 ,  H04B7/086

Abstract: Transmission methods and systems include calibrating a mismatch between uplink and downlink digital elements of hybrid beamforming transceivers. calibrating a mismatch between uplink and downlink analog elements of the plurality of hybrid beamforming transceivers. Respective downlink channels are estimated between a user equipment and the hybrid beamforming transceivers using respective mismatch calibrations for the digital and analog elements of each of the hybrid beamforming transceivers. Data is transmitted to the user equipment from the hybrid beamforming transceivers using a distributed beamforming pattern based on the estimated downlink channels.

公开(公告)号：US20240136063A1

公开(公告)日：2024-04-25

申请号：US18481383

申请日：2023-10-05

Applicant: NEC Laboratories America, Inc.

Inventor： Xujiang Zhao ,  Haifeng Chen

IPC: G16H50/20 ,  G16H50/70

CPC classification number: G16H50/20 ,  G16H50/70

Abstract: Systems and methods for out-of-distribution detection of nodes in a graph includes collecting evidence to quantify predictive uncertainty of diverse labels of nodes in a graph of nodes and edges using positive evidence from labels of training nodes of a multi-label evidential graph neural network. Multi-label opinions are generated including belief and disbelief for the diverse labels. The opinions are combined into a joint belief by employing a comultiplication operation of binomial opinions. The joint belief is classified to detect out-of-distribution nodes of the graph. A corrective action is performed responsive to a detection of an out-of-distribution node. The systems and methods can employ evidential deep learning.

公开(公告)号：US20240135188A1

公开(公告)日：2024-04-25

申请号：US18545055

申请日：2023-12-19

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k-1 binary classifiers on top of the semi-supervised representations to obtain k-1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k-1 binary predictions by matching the inconsistent ones to consistent ones of the k-1 binary predictions. The method further includes aggregating the k-1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20240134736A1

公开(公告)日：2024-04-25

申请号：US18493374

申请日：2023-10-23

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  LuAn Tang ,  Yanchi Liu ,  Zhengzhang Chen ,  Haifeng Chen

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0709 ,  G06F11/0793 ,  G16H50/20

Abstract: Methods and systems for anomaly detection include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Anomaly detection is performed using the feature vector to identify an anomaly within a system. A corrective action is performed responsive to the anomaly to correct or mitigate an effect of the anomaly. The detected anomaly can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

公开(公告)号：US20240133937A1

公开(公告)日：2024-04-25

申请号：US18485235

申请日：2023-10-11

Applicant: NEC Laboratories America, Inc.

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

IPC: G01R31/08

CPC classification number: G01R31/085 ,  G01R31/088

Abstract: Systems, methods, and structures providing dynamic line rating (DLR) for overhead transmission lines based on distributed fiber optic sensing (DFOS)/distributed temperature sensing (DTS) to determine temperature of the electrical conductors. Environmental conditions such as wind speed, wind direction, and solar radiation data, are collected from environmental sensors and an acoustic modem that convert the digital data collected from the environmental sensors into generated vibration patterns that are subsequently used to vibrationally excite a DFOS optical sensor fiber. The DFOS system monitors the optical sensor fiber and detects, measures, and decodes the vibrational excitations. An Artificial Neural Network (ANN) determines a heat transfer correlation between the temperature of the optical sensor fiber and electrical conductor(s) (core temperature).

公开(公告)号：US20240127072A1

公开(公告)日：2024-04-18

申请号：US18545025

申请日：2023-12-19

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k−1 binary classifiers on top of the semi-supervised representations to obtain k−1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k−1 binary predictions by matching the inconsistent ones to consistent ones of the k−1 binary predictions. The method further includes aggregating the k−1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20240087196A1

公开(公告)日：2024-03-14

申请号：US18463784

申请日：2023-09-08

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Kai Li ,  Shaobo Han ,  Hans Peter Graf ,  Changhao Shi

IPC: G06T11/60 ,  G06T9/00 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06T11/60 ,  G06T9/002 ,  G06V10/764 ,  G06V10/774

Abstract: Methods and systems for image generation include generating a latent representation of an image, modifying the latent representation of the image based on a trained attribute classifier and a specified attribute input, and decoding the modified latent representation to generate an output image that matches the specified attribute input.

公开(公告)号：US20240085238A1

公开(公告)日：2024-03-14

申请号：US18501203

申请日：2023-11-03

Applicant: NEC Laboratories America, Inc.

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

IPC: G01H9/00 ,  G01D5/353

CPC classification number: G01H9/004 ,  G01D5/35361

Abstract: In sharp contrast to the prior art, a fallen tree detection and localization method based on distributed fiber optical sensing (DFOS) technique and physics informed machine learning is described in which DFOS leverages existing fiber cables that are conventionally installed on the bottom layer of distribution lines and used to provide high-speed communications. The DFOS collects and transmits fallen tree induced vibration data along the length of the entire overhead lines, including distribution lines and transmission lines, where there is a fiber cable deployed. The developed physics-informed neural network model processes the data and localizes the fallen tree location along the lines. The location is interpreted in at least two aspects: the fallen tree location in terms of the fiber cable length; and the exact cable location (power cable or fiber cable) that the fallen tree mechanically impacts.