公开(公告)号：US20230315851A1

公开(公告)日：2023-10-05

申请号：US17707277

申请日：2022-03-29

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Sixiao WEI ,  Genshe CHEN ,  Kuochu CHANG ,  Thomas M. CLEMONS, III

IPC: G06F21/56 ,  G06N7/00

CPC classification number: G06F21/566 ,  G06N7/005 ,  G06F2221/034

Abstract: A method for detecting false data injection attacks (FDIAs) on a condition-based predictive maintenance (CBPM) system includes: collecting sensor data from sensors monitoring components of a system maintained by the CBPM system to extract features for a cyberattack detection model and gathering historical data of the system to build a cyberattack knowledge base about the system; combining the sensor data and the historical data to train the cyberattack detection model; using a graphical Bayesian network model to capture domain knowledge and condition-symptom relationships between the sensor-monitored components and the sensors; and based on the cyberattack detection model and the Bayesian network model, detecting the FDIAs on the CBPM system.

公开(公告)号：US20230179265A1

公开(公告)日：2023-06-08

申请号：US16813250

申请日：2020-03-09

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Zhonghai WANG ,  Xingping LIN ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: G01S13/90 ,  G01S13/88 ,  G01S7/35

CPC classification number: G01S13/9017 ,  G01S7/354 ,  G01S13/888

Abstract: A hidden chamber detector includes a linear frequency modulated continuous wave (LFMCW) radar, a synthetic aperture radar (SAR) imaging processor, and a time division multiple access (TDMA) multiple input multiple output (MIMO) antenna array, including a plurality of transmitting and receiving (Tx-Rx) antenna pairs. A Tx-Rx antenna pair is selected, in a time division manner, as a Tx antenna and an Rx antenna for the LFMCW radar. The LFMCW radar is configured to transmit an illumination signal, receive an echo signal, convert the echo signal to a baseband signal, collect baseband samples, and send the collected samples to the SAR imaging processor. The SAR imaging processor is configured to receive the collected samples, collect structure/configuration of the antenna array and scanning information, and form an SAR image based on the collected samples, the structure/configuration of the antenna array, and the scanning information.

公开(公告)号：US20220375134A1

公开(公告)日：2022-11-24

申请号：US17734858

申请日：2022-05-02

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Yi LI ,  Xin TIAN ,  Genshe CHEN ,  Erik BLASCH ,  Khanh PHAM

IPC: G06T9/00 ,  G06T3/00 ,  G06V10/25 ,  G01S17/89

Abstract: A method for point cloud compression of an intelligent cooperative perception (iCOOPER) for autonomous air vehicles (AAVs) includes: receiving a sequence of consecutive point clouds; identifying a key point cloud (K-frame) from the sequence of consecutive point clouds; transforming each of the other consecutive point clouds (P-frames) to have the same coordinate system as the K-frame; converting each of the K-frame and P-frames into a corresponding range image; spatially encoding the range image of the K-frame by fitting planes; and temporally encoding each of the range images of the P-frames using the fitting planes.

公开(公告)号：US20220172122A1

公开(公告)日：2022-06-02

申请号：US17563014

申请日：2021-12-27

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Dan SHEN ,  Peter ZULCH ,  Marcello DISASIO ,  Erik BLASCH ,  Genshe CHEN

IPC: G06N20/00 ,  H04N5/33 ,  G06K9/62

Abstract: The present disclosure provide a system, a method, and a storage medium for distributed joint manifold learning (DJML) based heterogeneous sensor data fusion. The system includes a plurality of nodes; and each node includes at least one camera; one or more sensors; at least one memory configured to store program instructions; and at least one processor, when executing the program instructions, configured to obtain heterogeneous sensor data from the one or more sensors to form a joint manifold; determine one or more optimum manifold learning algorithms by evaluating a plurality of manifold learning algorithms based on the joint manifold; compute a contribution of the node based on the one or more optimum manifold learning algorithms; update a contribution table based on the contribution of the node and contributions received from one or more neighboring nodes; and broadcast the updated contribution table to the one or more neighboring nodes.

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：US20220085878A1

公开(公告)日：2022-03-17

申请号：US17021289

申请日：2020-09-15

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Lun LI ,  Yi LI ,  Sixiao WEI ,  Dan SHEN ,  Genshe CHEN

IPC: H04B10/077 ,  G06N3/04 ,  H04B10/11 ,  G06K9/62 ,  G06N3/08

Abstract: Various embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model.

公开(公告)号：US20190356053A1

公开(公告)日：2019-11-21

申请号：US15983266

申请日：2018-05-18

Applicant: Intelligent Fusion Technology, Inc

Inventor： Xingping LIN ,  Zhonghai WANG ,  Genshe CHEN ,  Erik BLASCH ,  Khanh PHAM

IPC: H01Q9/28 ,  H01Q5/378 ,  H01Q1/38

Abstract: A cone-based multi-layer wide band antenna is provided, including a cone-based member having a multi-layer structure. The multi-layer structure includes a first layer conical structure, and the first layer conical structure has a height and a base radius configured to provide a desired impedance of the antenna.

公开(公告)号：US20250028337A1

公开(公告)日：2025-01-23

申请号：US18494293

申请日：2023-10-25

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Dan SHEN ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH ,  Yajie BAO

IPC: G05D1/69 ,  G05D109/20 ,  G06N7/01

Abstract: The present disclosure provides a method, a system and a storage medium of resilient human-on-the-loop range-only cooperative positioning of a plurality of unmanned aerial vehicles (UAVs). The method includes computing an initial exploitability using an initial distribution and an initial policy; performing a forward updating of a distribution of a portion of the plurality of UAVs, and performing a backward updating of a Q function of each UAV of the plurality of UAVs; for each time step, calculating a dual variable at an (i+1)-th iteration and calculating a policy at an (i+1)-th iteration; computing a ratio of an exploitability at the (i+1)-th iteration over the initial exploitability; and if the ratio is less than or equal to a pre-defined tolerance value, maintaining a policy at the i-th iteration; and if the ratio is greater than the pre-defined tolerance value, using the policy at the (i+1)-th iteration.

公开(公告)号：US20240406269A1

公开(公告)日：2024-12-05

申请号：US18806352

申请日：2024-08-15

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: H04L67/131 ,  H04L67/10

Abstract: The present disclosure provides a system of distributed edge computing for cooperative augmented reality with mobile sensing capability. The system includes a plurality of nodes configured to generate a plurality of data streams; and a plurality of distributed edge servers configured to process one or more tasks using the plurality of data streams. An Apache Storm distributed stream processing platform is installed and properly configured on each distributed edge server; the plurality of distributed edge servers includes one or more service modules installed on each distributed edge server and configured to process the one or more tasks; and the plurality of distributed edge servers includes a master distributed edge server and a plurality of slave distributed edge servers; and a scheduler is installed on the master distributed edge server and configured to distribute the one or more tasks to the plurality of distributed edge servers.

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.