Search Results for author: Hae Young Noh

Found 10 papers, 2 papers with code

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

Our results show that our method can enhance the spatial-temporal resolution and better resolve closely traveling cars.

PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration

no code implementations7 Dec 2022 Yiwen Dong, Jesse R Codling, Gary Rohrer, Jeremy Miles, Sudhendu Sharma, Tami Brown-Brandl, Pei Zhang, Hae Young Noh

In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations.

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

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

Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning

no code implementations21 Feb 2020 Susu Xu, Hae Young Noh

The supervised learning requires historical structural response data and corresponding damage states (i. e., labels) for each building to learn the building-specific damage diagnosis model.

Disaster Response Domain Adaptation

MedAL: Deep Active Learning Sampling Method for Medical Image Analysis

no code implementations25 Sep 2018 Asim Smailagic, Hae Young Noh, Pedro Costa, Devesh Walawalkar, Kartik Khandelwal, Mostafa Mirshekari, Jonathon Fagert, Adrián Galdrán, Susu Xu

Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.

Active Learning Diabetic Retinopathy Detection

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