キーワード: machine learning
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34 件のレコードが見つかりました。 1件目から10件目まで表示します。

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main.pdf
Automatic Identification and Normalisation of Physical Measurements in Scientific Literature
ジャーナル論文
著者
ORCID ;
ROMARY, Laurent (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID
キーワード
tdm, physical quantities, machine learning
刊行年月日
2019-09-23
更新時刻
2022-10-03 02:00:14 +0900
2604_supp_info.pdf
Non-negative matrix factorization analysis of spatially-resolved photoemission spectra for epitaxially grown graphene on SiC
ジャーナル論文
著者
Masaki Imamura (author) (この著者で検索)
Saga University Synchrotron Light Application Center
;
Kazutoshi Takahashi (author) (この著者で検索)
キーワード
Photoemission spectroscopy, non-negative matrix factorization, graphene, machine learning, data-driven analysis
刊行年月日
2026-06-19
更新時刻
2026-06-24 15:38:41 +0900
Predicting the surface roughness of an electrodeposited copper film using a machine learning technique.pdf
Predicting the surface roughness of an electrodeposited copper film using a machine learning technique
ジャーナル論文
著者
Ryo Tamura (author) (この著者で検索)
National Institute for Materials Science Center for Basic Research on Materials/Data-driven Materials Research Field/Data-driven Algorithm Team
ORCID SAMURAI ;
Ryuichi Inaba (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Mami Watanabe (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Yutaro Mori (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Makoto Urushihara (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Kenji Yamaguchi (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Shoichi Matsuda (author) (この著者で検索)
National Institute for Materials Science Research Center for Energy and Environmental Materials (GREEN)/Battery and Cell Materials Field/Automated Electrochemical Experiments Team
ORCID SAMURAI
キーワード
electrodeposited copper film, surface roughness, machine learning
刊行年月日
2024-12-31
更新時刻
2024-10-31 16:30:15 +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
Development of a method to evaluate strain in weld solidification using in-situ observations with high-brightness synchrotron X-rays.pdf
Development of a method to evaluate strain in weld solidification using in-situ observations with high-brightness synchrotron X-rays
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ;
Takayuki Yamashita (author) (この著者で検索)
;
Yasuhiro Aoki (author) (この著者で検索)
;
Hidetoshi Fujii (author) (この著者で検索)
キーワード
in-situ observation, synchrotron X-ray, welding technology, optical flow, machine learning, Strain evaluation method;
刊行年月日
2024-12-31
更新時刻
2024-11-13 12:30:30 +0900
Advanced Science - 2023 - Uryu - Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder.pdf
Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns
ジャーナル論文
著者
Hirotaka Uryu (author) (この著者で検索)
;
Tsunetomo Yamada (author) (この著者で検索)
;
Koichi Kitahara (author) (この著者で検索)
;
Alok Singh (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yutaka Iwasaki (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Kaoru Kimura (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Kanta Hiroki (author) (この著者で検索)
;
Naoki Miyao (author) (この著者で検索)
;
Asuka Ishikawa (author) (この著者で検索)
;
Ryuji Tamura (author) (この著者で検索)
;
Satoshi Ohhashi (author) (この著者で検索)
;
Chang Liu (author) (この著者で検索)
;
Ryo Yoshida (author) (この著者で検索)
キーワード
deep learning, x-ray powder diffraction, quasicrystal, phase identification, machine learning
刊行年月日
2023-11-14
更新時刻
2024-12-13 12:30:39 +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
Manuscript.pdf
Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers
ジャーナル論文
著者
Chiaki Yoshikawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Duc Anh Nguyen (author) (この著者で検索)
;
Tadashi Nakaji-Hirabayashi (author) (この著者で検索)
;
Ichigaku Takigawa (author) (この著者で検索)
;
Hiroshi Mamitsuka (author) (この著者で検索)
キーワード
cellulose nanofiber, concentrated polymer brush, hMSC, self-assembly, machine learning
刊行年月日
2024-04-08
更新時刻
2024-08-27 08:30:31 +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
ChemPlusChem25_90_e202500342.pdf
Machine Learning—Guided Design of Biomass‐Based Porous Carbon for Aqueous Symmetric Supercapacitors
ジャーナル論文
著者
Manickam Minakshi (author) (この著者で検索)
ORCID ;
Apsana Sharma (author) (この著者で検索)
;
Ferdous Sohel (author) (この著者で検索)
;
Almantas Pivrikas (author) (この著者で検索)
ORCID ;
Pragati A. Shinde (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
biomass, carbon, dopant, energy, machine learning, storage
刊行年月日
2025-10-09
更新時刻
2025-10-21 16:06:10 +0900
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