# 機械学習高速自動スペクトル解析ソフト“EMPeaks”について

https://mdr.nims.go.jp/datasets/5a2c4a0d-7ba5-4336-bd42-0bb6fbca7a5b

## File

- [20241223Nagamura_著者最終稿.pdf](https://mdr.nims.go.jp/filesets/867f0319-ff05-4a1d-8b48-a9f577ba49fe/download) ([Detail](https://mdr.nims.go.jp/filesets/867f0319-ff05-4a1d-8b48-a9f577ba49fe.md))

## Id

5a2c4a0d-7ba5-4336-bd42-0bb6fbca7a5b

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-08T19:33:03.908277Z

## Updated at

2025-01-10T07:31:04.355818Z

## Published at

2025-01-10T07:31:04.514583Z

## Doi

https://doi.org/10.48505/nims.5257

## First published url

https://doi.org/10.1380/vss.67.500

## Date published

2024-10-10

## Recorded date published

2024

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 機械学習高速自動スペクトル解析ソフト“EMPeaks”について
  title_type: original
  lang: ja

## Description

- description: There are a wide variety of measurement techniques that produce spectra
    as output datasets. Existing data analysis software and some open-source macros
    are useful but not sufficient for non-experts to perform peak-fitting analysis
    and interpretation. Therefore, we have investigated analysis method using unsupervised
    machine learning for highthroughput and automated peak deconvolution analysis
    of spectra without prior knowledge of experimental techniques and material databases.
    In this paper, we will introduce the open-source package called “EMPeaks”, including
    development chronology, specific usage, current issues, and analysis examples.
  description_type: abstract
  lang: und

## Creator

- name: 永村 直佳
  role: author
  orcid: https://orcid.org/0000-0002-7697-8983
  organization: National Institute for Materials Science
- name: 安藤 康伸
  role: author

## Contact agent



## Publisher

organization: Surface Science Society Japan

## Managing organization



## Keyword

- subject: spectroscopy
  schema: not_defined
- subject: peak fitting
  schema: not_defined
- subject: data analysis
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

- description: "©公益社団法人 日本表面真空学会"
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 表面と真空
  issn: '24335835'
  volume: '67'
  issue: '10'
  start_page: 500
  end_page: 505

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## Specimen



## Chemical composition



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## Fileset

- id: 867f0319-ff05-4a1d-8b48-a9f577ba49fe
  filename: 20241223Nagamura_著者最終稿.pdf
  content_type: application/pdf
  size: 976298
  md5: f5792de6ebb6f2c2d45cc557d640c269

## Thumbnail

fileset_id: 867f0319-ff05-4a1d-8b48-a9f577ba49fe
filename: 20241223Nagamura_著者最終稿.pdf