公开(公告)号：US11562224B2

公开(公告)日：2023-01-24

申请号：US16057828

申请日：2018-08-08

Applicant: University of Electronic Science and Technology of China

Inventor： Huijuan Wu ,  Jiping Chen ,  Xiangrong Liu ,  Yao Xiao ,  Mengjiao Wang ,  Bo Tang ,  Mingru Yang ,  Haoyu Qiu ,  Yunjiang Rao

IPC: G06N3/08 ,  G06F17/18 ,  G06N3/04 ,  G06N5/00 ,  G06N20/10 ,  G06N20/20

Abstract: A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features. The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.

公开(公告)号：US20180357542A1

公开(公告)日：2018-12-13

申请号：US16057828

申请日：2018-08-08

Applicant: University of Electronic Science and Technology of China

Inventor： Huijuan Wu ,  Jiping Chen ,  Xiangrong Liu ,  Yao Xiao ,  Mengjiao Wang ,  Bo Tang ,  Mingru Yang ,  Haoyu Qiu ,  Yunjiang Rao

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/18

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06N3/04

Abstract: A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features, The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.