Mr. Lei Liang Profile

Lei Liang

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 August 2023 Paper

Denoising method of explosion vibration signal by deep learning

Ao Liu, Lei Liang, Ke Jiang

Proceedings Volume 12748, 127480G (2023) https://doi.org/10.1117/12.2689579

KEYWORDS: Vibration, Background noise, Denoising, Signal processing, Deep learning

Read Abstract +

In the explosion experiment, the explosion vibration signal is the important information to evaluate the explosion equivalent or to locate the explosion point. However, the environment at the scene of the explosion is very complicated. The explosion vibration signal collected by the Distributed fiber Acoustic Sensor (DAS) system not only contains optical noise, but also contains a lot of non-stationary and non-Gaussian environmental background noise. In order to solve this issue, feed-forward denoising convolutional neural network (DnCNN) are used to denoise the signals during the noise suppression of explosion vibration signals. The initial application of this network was to purge images of additive Gaussian white noise. In order to make DnCNN adapt to the de-noising of explosion vibration data, we have performed numerous optimization tasks. Firstly, the input data is processed by an reversible downsampling factor in this paper, expanding the network’s perceptual field while also making training easier. Secondly, rather than using Gaussian white noise, the training set of DnCNN is rebuilt using background noise collected in the actual environment. Finally, the DnCNN parameters that impact network performance are modified and improved. From the experimental results in this paper, the DnCNN can effectively suppress the noise in the explosion vibration signal and preserve the effective signal.

Proceedings Article | 8 June 2023 Paper

Research on underwater pipeline leakage monitoring based on distributed fiber optic acoustic sensing technology

Bing-Shi Luo, Lei Liang, Yu-Xing Duan

Proceedings Volume 12707, 1270735 (2023) https://doi.org/10.1117/12.2681354

KEYWORDS: Pipes, Vibration, Optical sensing, Acoustics, Optical fibers, Fiber optics sensors, Environmental monitoring, Fiber optics, Modal decomposition, Transportation

Read Abstract +

Underwater pipeline transportation, as an important means of natural gas transportation, will cause great economic loss and environmental pollution in case of leakage damage. Aiming at improving the accuracy of pipeline leakage monitoring research, a method was proposed to monitor the leakage process of underwater natural gas pipelines using distributed optical fiber acoustic sensing technology. In this paper, a processing algorithm combining empirical modal decomposition method and wavelet decomposition reconstruction is used to extract the signal frequency domain features. The experimental results show that the frequency domain amplitude of the vibration signal gradually grows as the leakage orifice diameter and internal pipe pressure increase, and the standard deviation of the vibration signal of the pipeline exhibits a quadratic fitting relationship with the size of the leak aperture. The method proposed in this paper has a minimum leak aperture identification error of 7.2%, which can effectively improve the pipeline leak monitoring accuracy.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years