公开(公告)号：US20220092420A1

公开(公告)日：2022-03-24

申请号：US17480999

申请日：2021-09-21

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Jingyang LU ,  Erik BLASCH ,  Roman ILIN ,  Hua-mei CHEN ,  Dan SHEN ,  Nichole SULLIVAN ,  Genshe CHEN

IPC: G06N3/08 ,  G06K9/62 ,  G06K9/00

Abstract: Embodiments of the present disclosure provide a method, a device, and a storage medium for domain adaptation for efficient learning fusion (DAELF). The method includes acquiring data from a plurality of data sources of a plurality of sensors; for each of the plurality of sensors, training an auxiliary classifier generative adversarial network (AC-GAN) by a hardware processor with data from each data source of the plurality of data sources, thereby obtaining a trained feature extraction network and a trained label prediction network for each data source; forming a decision-level fusion network or a feature-level fusion network; and training the decision-level fusion network or the feature-level fusion network with a source-only mode or a generate to adapt (GTA) mode; and applying the trained decision-level fusion network or the trained feature-level fusion network to detect a target of interest.

公开(公告)号：US20210134046A1

公开(公告)日：2021-05-06

申请号：US16674929

申请日：2019-11-05

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Jingyang LU ,  Yiran XU ,  Dan SHEN ,  Nichole SULLIVAN ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: G06T15/06 ,  G06N20/00 ,  G06N5/04

Abstract: The present disclosure provides a method for wave propagation prediction based on a 3D ray tracing engine and machine-learning based dominant ray selection. The method includes receiving, integrating, and processing input data. Integrating and processing the input data includes dividing a cone of the original millimeter wave (mmWave) into a plurality of sub cones; determining a contribution weight of rays coming from each sub cone to the received signal strength (RSS) at a receiving end of interest; and determining rays coming from one or more sub cones that have a total contribution weight to the RSS larger than a preset threshold value as dominant rays using a neural network obtained through a machine learning approach. The method further includes performing ray tracing based on the input data and the dominant rays to predict wave propagation.

公开(公告)号：US20240391608A1

公开(公告)日：2024-11-28

申请号：US18324986

申请日：2023-05-28

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Genshe CHEN ,  Hui HUANG ,  Jiaoyue LIU ,  Nichole SULLIVAN ,  Kuochu CHANG

IPC: B64F5/60 ,  G01M15/14 ,  G06N3/08

Abstract: The present disclosure provides a method, a system and a storage medium for remaining useful life prediction of an aircraft engine based on gaussian process regression integrated deep learning. The method includes partitioning observation data into training data, validation data, and testing data; training a generative GPR model using training data to obtain a trained GPR model; using trained GPR model as a synthetic data generator to generate synthetic data; performing an averaging process to integrate the synthetic data and the training data to obtain integrated data; generating a plurality of data minibatches from the integrated data; feeding the plurality of data minibatches into a deep leaning model to train the deep leaning model; obtaining RUL prediction from trained deep learning model based on the validation data; and using the RUL prediction for further parameter training of the generative GPR model and the deep learning model.

公开(公告)号：US20240402298A1

公开(公告)日：2024-12-05

申请号：US17382931

申请日：2021-07-22

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Hui HUANG ,  Yi LI ,  Erik BLASCH ,  Khanh PHAM ,  Jiaoyue LIU ,  Nichole SULLIVAN ,  Dan SHEN ,  Genshe CHEN

IPC: G01S7/41 ,  G01S7/40

Abstract: A method for recognizing a low-probability-of-interception (LPI) radar signal waveform includes: obtaining, by a radar signal receiver, an LPI radar signal s(t), s(t) varying with time t; extracting, by a radar signal processor, an adaptive feature and a pre-defined analytical feature from the LPI radar signal s(t); combining, by the radar signal processor, the adaptive feature with the pre-defined analytical feature to generate a constructed adaptive feature; and applying, by the radar signal processor, a convolutional neural network (CNN) model to classify the constructed adaptive feature to recognize the LPI radar signal waveform.

公开(公告)号：US20230186620A1

公开(公告)日：2023-06-15

申请号：US17551436

申请日：2021-12-15

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Huong Ngoc DANG ,  Yi LI ,  Xin TIAN ,  Nichole SULLIVAN ,  Genshe CHEN ,  Khanh PHAM

IPC: G06V10/94 ,  G06V10/764 ,  G06V10/82 ,  G06V10/96 ,  G06V10/77

CPC classification number: G06V10/95 ,  G06V10/764 ,  G06V10/82 ,  G06V10/96 ,  G06V10/7715

Abstract: A system includes: a named data networking (NDN) based Spark distributed computing network including a Spark distributed computing network including a master computer node and a plurality of slave computer nodes, and a named data networking (NDN) protocol installed on the Spark distributed computing network, and a coded distributed computing (CDC) target recognition model deployed on the NDN-based Spark distributed computing network. The NDN-based Spark distributed computing network is configured to: receive one or more batches of input images; generate a parity image from each batch of the input images; predict a label for each image of the batch of the input images; process the generated parity image; upon a label prediction of one image of the batch of the input images being unavailable, reconstruct the unavailable label prediction; and classify labels for the input images.

公开(公告)号：US20230186120A1

公开(公告)日：2023-06-15

申请号：US17534754

申请日：2021-11-24

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qingliang ZHAO ,  Jiaoyue LIU ,  Nichole SULLIVAN ,  Kuochu CHANG ,  Erik BLASCH ,  Genshe CHEN

IPC: G06N5/04 ,  G06N5/02 ,  G06F40/295 ,  G06F40/30 ,  G06F16/25 ,  G06F16/26

CPC classification number: G06N5/04 ,  G06F16/26 ,  G06F16/258 ,  G06F40/30 ,  G06F40/295 ,  G06N5/022

Abstract: A computing system includes: a memory, containing instructions for a method for anomaly and pattern detection of unstructured big data via semantic analysis and dynamic knowledge graph construction; a processor, coupled with the memory and, when the instructions being executed, configured to: receive unstructured big data associated with social network interactions, events, or activities; parse and structure the unstructured big data to generate structured big data; form a dynamic knowledge base based on the structured big data; and perform sematic reasoning on the dynamic knowledge base to discover patterns and anomalies among the social network interactions, events, or activities; and a display, comprising an interactive graphical user interface (GUI), configured to receive the anomalies and patterns to display real-time actionable alerts, provide recommendations, and support decisions.