Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista de Engenharia e Pesquisa Aplicada |
Texto Completo: | http://revistas.poli.br/index.php/repa/article/view/2159 |
Resumo: | Hospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees. |
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Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital RefrigeratorsCriação de Modelo de Detecção de Anomalias para Termômetro IoT Usado em Refrigeradores HospitalaresHospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees.Ambientes hospitalares precisam de refrigeradores hospitalares para armazenar fármacos, vacinas, bolsas de sangue, dentre outros. Tais equipamentos são configurados de forma a manter determinada faixa de temperatura, visto que os produtos armazenados são sensíveis a mudanças de temperatura fora dessa faixa. Este projeto objetiva analisar as variações de temperatura acima do adequado. Nos experimentos realizados foram implementados diferentes técnicas de detecção de anomalias utilizando três métodos de agrupamento: k-means, DBSCAN e Isolation Forest. Levando em consideração a acurácia encontrada (76,7%), o método utilizado foi o DBSCAN. Com a análise realizada, foi possível perceber diversas relações entre os valores de temperatura, quantidade de alertas e os horários que eles aconteceram. Observou-se que a maior parte das anomalias encontradas aconteceram entre às 6:00 e às 8:00 horas da manhã, coincidindo com o horário de troca de turnos entre funcionários.Escola Politécnica de Pernambuco2021-11-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://revistas.poli.br/index.php/repa/article/view/215910.25286/repa.v6i5.2159Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-1282525-425110.25286/repa.v6i5reponame:Revista de Engenharia e Pesquisa Aplicadainstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEporhttp://revistas.poli.br/index.php/repa/article/view/2159/796http://revistas.poli.br/index.php/repa/article/view/2159/797-Copyright (c) 2021 Diego Mendes da Silva, Ingrid Bruno Nunes, Selton Felipe Guedes da Silva, Elyr Teixeira Alveshttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessda Silva, Diego MendesNunes, Ingrid Brunoda Silva, Selton Felipe GuedesAlves, Elyr Teixeira2021-11-25T12:22:35Zoai:ojs.poli.br:article/2159Revistahttp://revistas.poli.br/index.php/repaONGhttp://revistas.poli.br/index.php/repa/oai||repa@poli.br2525-42512525-4251opendoar:2021-11-25T12:22:35Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)false |
dc.title.none.fl_str_mv |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators Criação de Modelo de Detecção de Anomalias para Termômetro IoT Usado em Refrigeradores Hospitalares |
title |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
spellingShingle |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators da Silva, Diego Mendes |
title_short |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
title_full |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
title_fullStr |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
title_full_unstemmed |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
title_sort |
Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators |
author |
da Silva, Diego Mendes |
author_facet |
da Silva, Diego Mendes Nunes, Ingrid Bruno da Silva, Selton Felipe Guedes Alves, Elyr Teixeira |
author_role |
author |
author2 |
Nunes, Ingrid Bruno da Silva, Selton Felipe Guedes Alves, Elyr Teixeira |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
da Silva, Diego Mendes Nunes, Ingrid Bruno da Silva, Selton Felipe Guedes Alves, Elyr Teixeira |
description |
Hospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2159 10.25286/repa.v6i5.2159 |
url |
http://revistas.poli.br/index.php/repa/article/view/2159 |
identifier_str_mv |
10.25286/repa.v6i5.2159 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2159/796 http://revistas.poli.br/index.php/repa/article/view/2159/797 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.coverage.none.fl_str_mv |
- |
dc.publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
dc.source.none.fl_str_mv |
Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128 Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128 2525-4251 10.25286/repa.v6i5 reponame:Revista de Engenharia e Pesquisa Aplicada instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
instname_str |
Universidade Federal de Pernambuco (UFPE) |
instacron_str |
UFPE |
institution |
UFPE |
reponame_str |
Revista de Engenharia e Pesquisa Aplicada |
collection |
Revista de Engenharia e Pesquisa Aplicada |
repository.name.fl_str_mv |
Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE) |
repository.mail.fl_str_mv |
||repa@poli.br |
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1798036000421707776 |