no code implementations • 20 Oct 2023 • Xuechun Li, Paula M. Burgi, Wei Ma, Hae Young Noh, David J. Wald, Susu Xu
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses.
1 code implementation • 4 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.
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 10 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.
1 code implementation • 23 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.
no code implementations • 5 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.
no code implementations • 6 Apr 2020 • Madhumitha Harishankar, Jun Han, Sai Vineeth Kalluru Srinivas, Faisal Alqarni, Shi Su, Shijia Pan, Hae Young Noh, Pei Zhang, Marco Gruteser, Patrick Tague
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
no code implementations • 21 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.
1 code implementation • 28 Aug 2019 • Asim Smailagic, Pedro Costa, Alex Gaudio, Kartik Khandelwal, Mostafa Mirshekari, Jonathon Fagert, Devesh Walawalkar, Susu Xu, Adrian Galdran, Pei Zhang, Aurélio Campilho, Hae Young Noh
Our online method enhances performance of its underlying baseline deep network.
no code implementations • 25 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.