# 鉄鋼試料中水素拡散評価を目指したマルチモーダルデータ解析

https://mdr.nims.go.jp/datasets/9f6c2e42-5fea-4e0b-9845-473ae21a6cbe

## File

- [2021_Aoyagi_表面と真空64_20180785.pdf](https://mdr.nims.go.jp/filesets/b25795eb-387e-4993-8a4a-5483263681c7/download) ([Detail](https://mdr.nims.go.jp/filesets/b25795eb-387e-4993-8a4a-5483263681c7.md))

## Id

9f6c2e42-5fea-4e0b-9845-473ae21a6cbe

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-10-20T14:35:28.146617Z

## Updated at

2024-01-05T13:12:57.599920Z

## Published at

2023-12-26T07:30:49.762740Z

## Doi



## First published url

https://doi.org/10.1380/vss.64.472

## Date published

2021-10-10

## Recorded date published

2021

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 鉄鋼試料中水素拡散評価を目指したマルチモーダルデータ解析
  title_type: original
  lang: ja
- title: Multimodal Data Analysis for Evaluating Hydrogen Diffusion in Steel
  title_type: alternative
  lang: en

## Description

- description: Multimodal data analysis provides useful information that is not generally
    obtained from one of the analysis methods. In this study, time-course images of
    hydrogen distribution on a steel sample measured using electron stimulated desorption
    (ESD), scanning electron microscopy (SEM) images and electron backscatter diffraction
    (EBSD) images were fused to create a multimodal image data set. The fused multimodal
    images were analyzed by principal component analysis, least absolute shrinkage
    and selection operator (LASSO) and autoencoder. Each method is one of the most
    popular methods in each field, multivariate analysis, sparse modeling, and unsupervised
    learning based on artificial neural networks, respectively. The results of PCA,
    LASSO and autoencoder were consistent, and each method provides different aspects
    of the sample data information.
  description_type: abstract
  lang: jpn

## Creator

- name: Satoka AOYAGI
  role: author
- name: Tomomi AKIYAMA
  role: author
- name: Natsumi SUZUKI
  role: author
- name: Naoya MIYAUCHI
  role: author
  orcid: https://orcid.org/0000-0002-7716-3049
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Akiko N. ITAKURA
  role: author
  orcid: https://orcid.org/0000-0001-5783-141X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Surface Science Society Japan

## Managing organization



## Keyword

- subject: hydrogen permeation
  schema: not_defined
- subject: multimodal data analysis
  schema: not_defined
- subject: hydrogen visualization
  schema: not_defined
- subject: EBSD
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Vacuum and Surface Science
  issn: '24335835'
  volume: '64'
  issue: '10'
  start_page: 472
  end_page: 475

## Conference



## Related item



## Funding

- identifier: 18H03849
  funder_name: JSPS

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: b25795eb-387e-4993-8a4a-5483263681c7
  filename: 2021_Aoyagi_表面と真空64_20180785.pdf
  content_type: application/pdf
  size: 2651211
  md5: e823ddce889d427aeffbb2af227f1206

## Thumbnail

fileset_id: b25795eb-387e-4993-8a4a-5483263681c7
filename: 2021_Aoyagi_表面と真空64_20180785.pdf