Uncertainty-Aware Flood Inundation Mapping With a Bayesian Deep Learning Framework Using SAR Imagery
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2025.3610403
Abstract: Climate change-induced extreme rainfall events are driving a rise in flood frequency, posing significant challenges that need to be addressed by decision-makers. To aid in this challenge, Reliable, near-real-time flood extent maps are essential to… read more here.
Keywords: deep learning; bayesian deep; learning framework; uncertainty ... See more keywords
Bayesian Deep Neural Networks for Supervised Learning of Single-View Depth
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3142915
Abstract: Uncertainty quantification is essential for robotic perception, as overconfident or point estimators can lead to collisions and damages to the environment and the robot. In this letter, we evaluate scalable approaches to uncertainty quantification in… read more here.
Keywords: single view; deep neural; neural networks; depth ... See more keywords
Simultaneous Inverse Design and Uncertainty Quantification for Frequency-Selective Rasorber With Tunable and Switchable Abilities by Bayesian Deep Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Antennas and Propagation"
DOI: 10.1109/tap.2024.3384064
Abstract: A Bayesian deep learning (DL) scheme is proposed for simultaneous inverse design and uncertainty qualification (UQ) for frequency-selective rasorber (FSR) with switchable and tunable (S/T) abilities. The inversely designed FSR could work in single/two passband… read more here.
Keywords: deep learning; bayesian deep; inverse design; simultaneous inverse ... See more keywords
Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains With Mobile Edge Computing
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2020.2994366
Abstract: We present a novel game-theoretic, Bayesian reinforce-ment learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a… read more here.
Keywords: bayesian deep; blockchains mobile; learning; edge computing ... See more keywords
Bayesian Deep Learning for Fault Diagnosis of Induction Motors With Reduced Data Reliance and Improved Interpretability
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Energy Conversion"
DOI: 10.1109/tec.2025.3546347
Abstract: Fault diagnosis holds significant practical importance for high performance and reliable control of induction motors. However, existing deep learning-based fault diagnosis methods demand a large amount of training data and lack interpretability, which can limit… read more here.
Keywords: deep learning; diagnosis; bayesian deep; fault diagnosis ... See more keywords
A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2020.3009593
Abstract: Recent years have witnessed the prominent advancements of deep learning (DL) in the arsenal of prognostics and health management. However, the prognostic uncertainty problem extensively existed in industrial devices is not addressed by most DL… read more here.
Keywords: bayesian deep; uncertainty; learning rul; framework ... See more keywords
Multi-Target Detection in Underwater Sensor Networks Based on Bayesian Deep Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2025.3535572
Abstract: Underwater target detection and its development have an important role in advancing marine science and technology. However, the complex and dynamic underwater environment poses challenges for detecting non-cooperative targets. This paper focuses on the problem… read more here.
Keywords: detection; deep learning; bayesian deep; target detection ... See more keywords
Predicting Flatfish Growth in Aquaculture Using Bayesian Deep Kernel Machines
Sign Up to like & getrecommendations! Published in 2025 at "Applied Sciences"
DOI: 10.3390/app15179487
Abstract: Olive flounder (Paralichthys olivaceus) is a key aquaculture species in South Korea, but its production has been challenged by rising mortality under environmental stress from key environmental factors such as water temperature, dissolved oxygen, and… read more here.
Keywords: kernel; bayesian deep; regression; deep kernel ... See more keywords