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Found 11 papers, 0 papers with code
A Large-Scale Study of a Sleep Tracking and Improving Device with Closed-loop and Personalized Real-time Acoustic Stimulation
Various intervention therapies ranging from pharmaceutical to hi-tech tailored solutions have been available to treat difficulty in falling asleep commonly caused by insomnia in modern life.
Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images
Hence in this work, we bring more objectivity, scalability, and repeatability in our damage inspection process, using deep learning, to miss fewer cracks.
An Automated System for Detecting Visual Damages of Wind Turbine Blades
We discuss the performance of our damage's suggestion model in production and how this system works in coordination with humans as part of a commercialized product and how it can contribute towards lowering wind energy's operational costs.
Sparse aNETT for Solving Inverse Problems with Deep Learning
We propose a sparse reconstruction framework (aNETT) for solving inverse problems.
Augmented NETT Regularization of Inverse Problems
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems.
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation
Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines.
Can a Robot Hear the Shape and Dimensions of a Room?
Knowing the geometry of a space is desirable for many applications, e. g. sound source localization, sound field reproduction or auralization.
Sound Robotics Audio and Speech Processing Signal Processing
Adaptive neural network based dynamic surface control for uncertain dual arm robots
The paper discusses an adaptive strategy to effectively control nonlinear manipulation motions of a dual arm robot (DAR) under system uncertainties including parameter variations, actuator nonlinearities and external disturbances.
Domain-to-Domain Translation Model for Recommender System
To handle the two problems, we propose a model that can extract both homogeneous and divergent features among domains and extract data in a domain can support for other domain equally: a so-called Domain-to-Domain Translation Model (D2D-TM).
A Learning and Masking Approach to Secure Learning
Finally, we show that a classifier masking method achieved by adding noise to the a neural network's logit output protects against low distortion attacks such as the CW attack.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
In this analysis, we find that lead service lines are not the only factor that is predictive of the risk of lead contamination of water.
