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Found 12 papers, 3 papers with code
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
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.
TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors
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.
GaitVibe+: Enhancing Structural Vibration-based Footstep Localization Using Temporary Cameras for In-home Gait Analysis
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.
PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration
In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations.
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
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.
Vibration-Based Bridge Health Monitoring using Telecommunication Cables
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
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.
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning
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.
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
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).
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning
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.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
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.
