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H. Kim

4 papers hereh-index 210 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cond-mat.soft2
  • cond-mat.mtrl-sci1
  • eess.IV1
same name
  • H. Kim — 19 papers
  • H. Kim — 8 papers, h 43
  • H. Kim — 7 papers, h 19
  • H. Kim — 7 papers, h 40
  • H. Kim — 5 papers, h 10
  • H. Kim — 4 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators

4 papers

cond-mat.mtrl-sci2026

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation

Vinícius Yu Okubo, Kotaro Shimizu, B. S. Shivaram +2

Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization challenging. We investigate the evolution o…

cond-mat.soft2026

Coarsening dynamics of fingerprint labyrinthine patterns: Machine learning assisted characterization

Supriyo Ghosh, Vinicius Yu Okubo, Kotaro Shimizu +3

Fingerprint labyrinthine patterns exhibit a level of structural complexity beyond simple stripe phases, combining local stripe order with a dense network of point-like defects. Unl…

eess.IV2025

Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

Daniel G. P. Petrini, Hae Yong Kim

Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intelligence have enabled increasingly sophist…

cond-mat.soft2024

Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions

Kotaro Shimizu, Vinicius Yu Okubo, Rose Knight +5

We present a comprehensive approach to characterizing labyrinthine structures that often emerge as a final steady state in pattern forming systems. We employ advanced machine learn…

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