48 citations · 48 across the 5 of their papers we have counts for
5 papers
Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems
Shun Muroga, Hideaki Nakajima, Taiyo Shimizu +2
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable…
Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers
Daisuke Kimura, Naoko Tajima, Toshiya Okazaki +1
In this study, we propose Explainable Multimodal Machine Learning (EMML), which integrates the analysis of diverse data types (multimodal data) using factor analysis for feature ex…
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming
Shun Muroga, Takashi Honda, Yasuaki Miki +3
To meet the demands for more adaptable and expedient approaches to augment both research and manufacturing, we report an autonomous system using real-time in-situ characterization…
Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data
Shun Muroga, Satoshi Yamazaki, Koji Michishio +5
We propose tabular two-dimensional correlation analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This metho…
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials
Shun Muroga, Yasuaki Miki, Kenji Hata
We present a multimodal deep learning (MDL) framework for predicting physical properties of a 10-dimensional acrylic polymer composite material by merging physical attributes and c…