fluidization-ae
Projeto de pesquisa que utiliza modelagem sequencial para detecção de anomalias em leitos fluidizados. licença: CC-BY-4.0
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (0.5%) to scientific vocabulary
Keywords
fluidized-bed
seq2seq
Last synced: 6 months ago
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JSON representation
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Repository
Projeto de pesquisa que utiliza modelagem sequencial para detecção de anomalias em leitos fluidizados. licença: CC-BY-4.0
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 2
Topics
fluidized-bed
seq2seq
Created over 1 year ago
· Last pushed 10 months ago
Metadata Files
Readme
License
Citation
Owner
- Name: Vinicius Mello e Muller
- Login: ViniMelloeMuller
- Kind: user
- Company: UNICAMP
- Repositories: 1
- Profile: https://github.com/ViniMelloeMuller
Estudante de Eng Química
Citation (CITATION.cff)
cff-version: 1.2.0 message: "Se usar os dados ou o código desse repositório, por favor cite como abaixo." authors: - family-names: "Muller" given-names: "Vinicius" orcid: "https://orcid.org/0000-0002-6884-255X" title: "Detecção de Anomalias em Leitos Fluidizados utilizando Redes LSTM" version: 2.0.0 doi: 10.5281/zenodo.15288363 date-released: 2025-04-26 url: "https://github.com/ViniMelloeMuller/fluidization-AE"
GitHub Events
Total
- Release event: 1
- Delete event: 2
- Push event: 16
- Public event: 1
- Pull request event: 1
- Create event: 3
Last Year
- Release event: 1
- Delete event: 2
- Push event: 16
- Public event: 1
- Pull request event: 1
- Create event: 3
Dependencies
GUI/requirements.txt
pypi
- matplotlib ==3.7.5
- nidaqmx ==1.0.0
- numpy ==1.24.4
- pandas ==2.0.3
pyproject.toml
pypi
- ipykernel >=6.29.5
- jinja2 >=3.1.4
- matplotlib >=3.9.2
- matplotlib-inline >=0.1.7
- neptune >=1.12.0
- neptune-tensorflow-keras >=2.2.2
- nidaqmx >=1.0.1
- numpy <2
- openpyxl >=3.1.5
- pandas >=2.2.3
- scienceplots >=2.1.1
- scikit-learn >=1.5.2
- scipy >=1.14.1
- seaborn >=0.13.2
- setuptools >=75.1.0
- tensorflow >=2.17.0
- tqdm >=4.66.5