Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
EDBT 2026 Demo / reviewers in the wild / expert

Junghun Byun

dblp:264/1213 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Internet of things and sensor networks · 50% Wireless networking · 50%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless networking
link adaptation		 0.412020
Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract) · AAAI 2020
Internet of things and sensor networks › underwater sensor networks › underwater communication › acoustic communication
underwater acoustic communication		 0.412020
Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract) · AAAI 2020
Machine learning › Deep learning architectures and training
iterative learning		 0.112020
Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract) · AAAI 2020

Methods — techniques the papers use, named apart from their topics

iterative learning · 0.9OFDM · 0.9
YearPublicationVenuePosition
2020 Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)
abstract
This paper describes an iterative learning framework consisting of multi-layer prediction processes for underwater link adaptation. To obtain a dataset in real underwater environments, we implemented OFDM (Orthogonal Frequency Division Multiplexing)-based acoustic communications testbeds for the first time. Actual underwater data measured in Yellow Sea, South Korea, were used for training the iterative learning model. Remarkably, the iterative learning model achieves up to 25% performance improvement over the conventional benchmark model.
Junghun Byun, Yong-Ho Cho, Hak-Lim Ko, Kyung-Seop Shin, Ohyun Jo
 AAAI1