Search Results for author: Jingxiao Liu

Found 8 papers, 3 papers with code

TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors

1 code implementation4 May 2023 Jingxiao Liu, Siyuan Yuan, Yiwen Dong, Biondo Biondi, Hae Young Noh

Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking.

Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing

no code implementations7 Dec 2022 Siyuan Yuan, Martijn van den Ende, Jingxiao Liu, Hae Young Noh, Robert Clapp, Cédric Richard, Biondo Biondi

In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution.

GaitVibe+: Enhancing Structural Vibration-based Footstep Localization Using Temporary Cameras for In-home Gait Analysis

no code implementations7 Dec 2022 Yiwen Dong, Jingxiao Liu, Hae Young Noh

In the fusion stage, both cameras and vibration sensors are installed to record only a few trials of the subject's footstep data, through which we characterize the uncertainty in wave arrival time and model the wave velocity profiles for the given structure.

Event Extraction

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

1 code implementation18 Jul 2022 Jingxiao Liu, Yujie Wei, Bingqing Chen

However, existing methods perform poorly when detecting small damages (e. g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced.

Image Segmentation Semantic Segmentation

Vibration-Based Bridge Health Monitoring using Telecommunication Cables

no code implementations10 May 2022 Jingxiao Liu, Siyuan Yuan, Bin Luo, Biondo Biondi, Hae Young Noh

Bridge Health Monitoring (BHM) enables early damage detection of bridges and is thus critical for avoiding more severe damages that might result in major financial and human losses.

HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis

1 code implementation23 Jul 2021 Jingxiao Liu, Susu Xu, Mario Bergés, Hae Young Noh

Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge.

Unsupervised Domain Adaptation

Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning

no code implementations5 Jun 2020 Jingxiao Liu, Mario Bergés, Jacobo Bielak, Hae Young Noh

Specifically, we train a deep network in an adversarial way to learn features that are 1) sensitive to damage and 2) invariant to different bridges.

Multi-Task Learning

Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring

no code implementations6 Feb 2020 Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.

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