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：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.

公开(公告)号：US20210103256A1

公开(公告)日：2021-04-08

申请号：US16562657

申请日：2019-09-06

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Bin JIA ,  Jiaoyue LIU ,  Huamei CHEN ,  Genshe CHEN ,  Kuo-Chu CHANG ,  Thomas M. CLEMONS, III

IPC: G05B13/02 ,  G06N5/02 ,  G06N7/00 ,  G06K9/62 ,  G06F40/10

Abstract: A decision support method for machinery control includes extracting entities and relations from information sources, and creating subject-predicate-object (SPO) triples. Each SPO triple includes a subject entity and an object entity, and a relation between the subject entity and the object entity. The method further includes constructing a knowledge graph (KG) based on the SPO triples. The KG includes a plurality of nodes corresponding to the entities, and a plurality of links corresponding to the relations between the entities. The method also includes predicting missing links between the nodes and adding the predicted links to the KG, and performing diagnostic and prognostic analysis using the KG, including analyzing plain text description of MCS situations to obtain relevant information concerning key components from the KG, recognizing sensor observations and component conditions to diagnose situations of other related components, and providing prognostics by analyzing the present trending/symptom in the MCS operating process.