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：US09679478B2

公开(公告)日：2017-06-13

申请号：US14694984

申请日：2015-04-23

Applicant: University of Electronic Science and Technology of China

Inventor： Huijuan Wu ,  Yunjiang Rao ,  Ya Qian ,  Hanyu Li

IPC: G08G1/04 ,  H04L12/26 ,  G08G1/02

CPC classification number: G08G1/04 ,  G08G1/02

Abstract: An online traffic volume monitoring system based on a phase-sensitive optical time domain reflectometry and its monitoring method are related to a field of intelligent transportation and an application of distributed fiber sensing. A vehicle moving temporal-spatial response graph is generated by accumulating differentiated Optical Time-Domain Reflectometry tracks at different moments in one unit monitoring period for traffic volume statistics, and then converted into a vehicle moving trajectory image through binarization and image pre-processing. Parameters of the moving vehicles are detected by utilizing a search-match method. A traffic volume, moving speeds, moving directions and locations are obtained respectively from detected trajectory number, and a tilt angle and pixel positions. The monitoring method is helpful to solve traffic congestion problem and informing drivers of real-time traffic volume, and contributes to realize an intelligent city traffic regulation.

公开(公告)号：US20160275788A1

公开(公告)日：2016-09-22

申请号：US14694984

申请日：2015-04-23

Applicant: University of Electronic Science and Technology of China

Inventor： Huijuan Wu ,  Yunjiang Rao ,  Ya Qian ,  Hanyu Li

IPC: G08G1/04

CPC classification number: G08G1/04 ,  G08G1/02

Abstract: An online traffic volume monitoring system based on a phase-sensitive optical time domain reflectometry and its monitoring method are related to a field of intelligent transportation and an application of distributed fiber sensing. A vehicle moving temporal-spatial response graph is generated by accumulating differentiated Optical Time-Domain Reflectometry tracks at different moments in one unit monitoring period for traffic volume statistics, and then converted into a vehicle moving trajectory image through binarization and image pre-processing. Parameters of the moving vehicles are detected by utilizing a search-match method. A traffic volume, moving speeds, moving directions and locations are obtained respectively from detected trajectory number, and a tilt angle and pixel positions. The monitoring method is helpful to solve traffic congestion problem and informing drivers of real-time traffic volume, and contributes to realize an intelligent city traffic regulation.

Abstract translation: 基于相敏光时域反射测量及其监测方法的在线流量监测系统与智能交通领域和分布式光纤传感应用有关。 通过在一个单位监测周期内在不同时刻累积差分光时域反射测量轨迹来产生车辆移动时空响应图，用于交通量统计，然后通过二值化和图像预​​处理转换成车辆移动轨迹图像。 通过利用搜索匹配方法来检测移动车辆的参数。 分别从检测到的轨迹数以及倾斜角度和像素位置获得交通量，移动速度，移动方向和位置。 监测方法有助于解决交通拥堵问题，通知驾驶员实时交通量，有助于实现智能城市交通管制。

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.

公开(公告)号：US20180080812A1

公开(公告)日：2018-03-22

申请号：US15812996

申请日：2017-11-14

Applicant: University of Electronic Science and Technology of China

Inventor： Huijuan Wu ,  Wei Zhang ,  Xiangrong Liu ,  Yunjiang Rao

IPC: G01H9/00 ,  G01V1/18 ,  G01V1/28

CPC classification number: G01H9/004 ,  G01V1/18 ,  G01V1/208 ,  G01V1/226 ,  G01V1/288 ,  G06F17/12 ,  G06N5/04 ,  G06N7/005 ,  H04B10/25

Abstract: A distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network, which belongs to infrastructure safety monitoring field, which is aimed to improve the intelligent ability of detection and identification of the existing distributed optical fiber sound/vibration sensing system under complex application conditions. The present invention utilizes the distributed optical fiber sound/vibration sensing system to pick up the sound or vibration signal of the whole line along the detection cable; and the customized short time feature and long time feature are respectively extracted from the relative quantity of the sound or the vibration signal at each spatial point in the whole monitoring range. The Bayesian identification and classification network at each spatial point is constructed and trained based on the prior knowledge of the collected signal features and their different background noises.