The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 03, 2022

Filed:

Sep. 15, 2020
Applicant:

Intelligent Fusion Technology, Inc., Germantown, MD (US);

Inventors:

Lun Li, Germantown, MD (US);

Yi Li, Germantown, MD (US);

Sixiao Wei, Germantown, MD (US);

Dan Shen, Germantown, MD (US);

Genshe Chen, Germantown, MD (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04B 10/077 (2013.01); H04B 10/11 (2013.01); G06N 3/04 (2006.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
H04B 10/0775 (2013.01); G06K 9/6261 (2013.01); G06K 9/6262 (2013.01); G06N 3/0481 (2013.01); G06N 3/08 (2013.01); H04B 10/11 (2013.01);
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.


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