Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation
Sign Up to like & getrecommendations! Published in 2021 at "ChemCatChem"
DOI: 10.1002/cctc.202101481
Abstract: A machine learning (ML) approach implementing the gradient boosting regressor (GBR) algorithm is applied to predict the binding energies of oxygen (EO) and carbon (EC) atoms on single atom alloys (SAAs) of Cu, Ag and… read more here.
Keywords: learning enabled; dehydrogenation; atom alloys; machine learning ... See more keywords
Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation.
Sign Up to like & getrecommendations! Published in 2022 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.2c19207
Abstract: Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks… read more here.
Keywords: learning enabled; enabled framework; machine learning; framework ... See more keywords
Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing.
Sign Up to like & getrecommendations! Published in 2020 at "ACS nano"
DOI: 10.1021/acsnano.0c05267
Abstract: Engineered point defects in two-dimensional (2D) materials offer an attractive platform for solid-state devices that exploit tailored opto-electronic, quantum emission, and resistive properties. Naturally occurring defects are also unavoidably important contributors to material properties and… read more here.
Keywords: point defects; machine learning; learning enabled; defects materials ... See more keywords
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials.
Sign Up to like & getrecommendations! Published in 2018 at "ACS nano"
DOI: 10.1021/acsnano.8b03569
Abstract: Deep-learning framework has significantly impelled the development of modern machine learning technology by continuously pushing the limit of traditional recognition and processing of images, speech, and videos. In the meantime, it starts to penetrate other… read more here.
Keywords: demand design; chiral metamaterials; deep learning; learning enabled ... See more keywords
Graph deep learning enabled spatial domains identification for spatial transcriptomics.
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbad146
Abstract: Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph… read more here.
Keywords: learning enabled; enabled spatial; deep learning; graph deep ... See more keywords
Barrier-Certified Learning-Enabled Safe Control Design for Systems Operating in Uncertain Environments
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/CAA Journal of Automatica Sinica"
DOI: 10.1109/jas.2021.1004347
Abstract: This paper presents learning-enabled barrier-certified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact. That is, safety constraints are imposed by not only the… read more here.
Keywords: learning enabled; safety; barrier certified; control ... See more keywords
Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2022.3221991
Abstract: Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the… read more here.
Keywords: network; learning enabled; semantic communication; task ... See more keywords
Reinforcement Learning Enabled Autonomous Manufacturing Using Transfer Learning and Probabilistic Reward Modeling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2022.3188014
Abstract: Here we propose a reinforcement learning enabled physical autonomous manufacturing system (AMS) that is capable of learning the manufacturing process parameters to autonomously fabricate a complex-geometry artifact with desired performance characteristics. The poor sample efficiency… read more here.
Keywords: learning enabled; reinforcement learning; manufacturing; reward ... See more keywords
Deep Reinforcement Learning Enabled Covert Transmission With UAV
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2023.3251357
Abstract: This letter considers covert communications in the context of unmanned aerial vehicle (UAV) networks, where a UAV is employed as a base station to transmit covert data to a legitimate ground user, while ensuring that… read more here.
Keywords: learning enabled; reinforcement learning; covert transmission; deep reinforcement ... See more keywords
Toward Proactive Human–Robot Collaborative Assembly: A Multimodal Transfer-Learning-Enabled Action Prediction Approach
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2021.3105977
Abstract: Human–robot collaborative assembly (HRCA) is vital for achieving high-level flexible automation for mass personalization in today’s smart factories. However, existing works in both industry and academia mainly focus on the adaptive robot planning, while seldom… read more here.
Keywords: learning enabled; transfer; transfer learning; action ... See more keywords
Learning-Enabled Output-Feedback-MPC-Based Synchronization Tracking Control of Multiaxis Motion Systems
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2022.3203753
Abstract: The unknown and time-varying model parameters and measurement noises impose great influence on the high-precision synchronization tracking control performance of multiaxis motion systems (MAMSs). To deal with the influence of the factors on the high-precision… read more here.
Keywords: enabled output; learning enabled; synchronization tracking; tracking control ... See more keywords