ライセンス: Creative Commons BY Attribution 4.0 International キーワード: machine learning
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6 件のレコードが見つかりました。 1件目から6件目まで表示します。

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d2ma00881e.pdf
Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach
ジャーナル論文
著者
Yukinori Koyama (author) (この著者で検索)
National Institute for Materials Science Research and Services Division of Materials Data and Integrated System
ORCID SAMURAI ;
Hidekazu Ikeno (author) (この著者で検索)
Osaka Metropolitan University
;
Masamichi Harada (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
;
Shiro Funahashi (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
;
Takashi Takeda (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
ORCID SAMURAI ;
Naoto Hirosaki (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
キーワード
phosphor, materials design, machine learning, luminescence, emission spectrum, Eu2+
刊行年月日
2022-11-29
更新時刻
2024-01-05 22:11:23 +0900
An interpretable linear model bridging data-driven analysis and chemical intuition for Eu2 -phosphor emissions.pdf
An interpretable linear model bridging data-driven analysis and chemical intuition for Eu 2+ -phosphor emissions
ジャーナル論文
著者
ORCID SAMURAI ;
Ryusei Hayasaka (author) (この著者で検索)
;
Yuta Matsushima (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI ;
Naoto Hirosaki (author) (この著者で検索)
キーワード
materials informatics, phosphor, europium, machine learning, interpretability, linear model, composition-based feature
刊行年月日
2026-12-31
更新時刻
2026-07-07 14:31:04 +0900
Development of an AI-based acoustic disturbance-detection method for robotic arc welding processes.pdf
Development of an AI-based acoustic disturbance-detection method for robotic arc welding processes
ジャーナル論文
著者
Houichi Kitano (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Masaki Kobayashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Masahiko Demura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Acoustic sensing, in-process monitoring, gas metal arc welding, disturbance detection, machine learning, explainable AI, smart manufacturing
刊行年月日
2026-12-31
更新時刻
2026-06-25 09:51:40 +0900
materials-17-02866.pdf
Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ;
Chao Kang (author) (この著者で検索)
ORCID ;
Chiaki Sato (author) (この著者で検索)
キーワード
experimental testing, multi-objective optimization, epoxy, adhesive, elastic modulus, machine learning, active learning
刊行年月日
2024-06-12
更新時刻
2025-01-07 08:30:26 +0900
Manuscript_clean.pdf
Machine learning prediction of Young's modulus in multi component titanium based biomedical alloys using extended thermodynamic descriptors
ジャーナル論文
著者
Hassan Ahmad (author) (この著者で検索)
Pakistan Institute of Engineering and Applied Sciences (PIEAS) Department of Metallurgy and Materials Engineering
;
Muhammad Haider (author) (この著者で検索)
;
Zafar Iqbal (author) (この著者で検索)
;
Muhammad Zarif (author) (この著者で検索)
;
Syed Mujtaba Ul Hassan (author) (この著者で検索)
キーワード
Titanium alloys, machine learning, biomedical alloys, Young’s modulus prediction
刊行年月日
2026-12-31
更新時刻
2026-07-08 16:58:38 +0900
Advanced Science - 2025 - Si - Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy.pdf
Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning
ジャーナル論文
著者
Qianli Si (author) (この著者で検索)
National Institute for Materials Science
;
Shoichi Matsuda (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yasunobu Ando (author) (この著者で検索)
;
Toshiyuki Momma (author) (この著者で検索)
;
Yoshitaka Tateyama (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
lithium metal battery, machine learning
刊行年月日
2025-05-05
更新時刻
2025-09-09 12:30:20 +0900
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