Traceability and analysis method for measurement laboratory testing data based on intelligent Internet of Things and deep belief network
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| Title: | Traceability and analysis method for measurement laboratory testing data based on intelligent Internet of Things and deep belief network |
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| Authors: | He PeiDong, Li XiaoJun, Shen WenQi, Deng ShuYu, Xiao Li, Zhang Yang Fan |
| Source: | Journal of Intelligent Systems, Vol 33, Iss 1, Pp 6821-30 (2024) |
| Publisher Information: | De Gruyter, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Science LCC:Electronic computers. Computer science |
| Subject Terms: | intelligent iot, deep belief network, metrology laboratory, data traceability, abnormal analysis, stacked denoising autoencoder model, cosine similarity, gaussian bernoulli restricted boltzmann machine, cloud-management-edge-end, Science, Electronic computers. Computer science, QA75.5-76.95 |
| Description: | A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of anomalies. First, a data analysis system for the metrology laboratory is designed based on an intelligent IoT architecture of “cloud-management-edge-end.” Then, the Gaussian Bernoulli-Restricted Boltzmann machine is introduced to improve the DBN model, which is deployed on the edge side for learning the ledger data sample library to determine the anomaly detection data of the metrology device. Finally, a stacked denoising autoencoder model is used in the cloud center to extract historical electricity consumption curve features, and the cause of anomalies is determined by calculating the cosine similarity between it and the target device feature curve to complete traceability analysis. Based on the selected dataset, the proposed method is experimentally demonstrated, and the results show that its traceability accuracy and time consumption are 88.72% and 3.949 s, respectively, which can meet the detection requirements of the metrology laboratory. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2191-026X |
| Relation: | https://doaj.org/toc/2191-026X |
| DOI: | 10.1515/jisys-2024-0076 |
| Access URL: | https://doaj.org/article/b2a8183e0b4746a7aaeba8a4f7b0893c |
| Accession Number: | edsdoj.b2a8183e0b4746a7aaeba8a4f7b0893c |
| Database: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstract: | A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of anomalies. First, a data analysis system for the metrology laboratory is designed based on an intelligent IoT architecture of “cloud-management-edge-end.” Then, the Gaussian Bernoulli-Restricted Boltzmann machine is introduced to improve the DBN model, which is deployed on the edge side for learning the ledger data sample library to determine the anomaly detection data of the metrology device. Finally, a stacked denoising autoencoder model is used in the cloud center to extract historical electricity consumption curve features, and the cause of anomalies is determined by calculating the cosine similarity between it and the target device feature curve to complete traceability analysis. Based on the selected dataset, the proposed method is experimentally demonstrated, and the results show that its traceability accuracy and time consumption are 88.72% and 3.949 s, respectively, which can meet the detection requirements of the metrology laboratory. |
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| ISSN: | 2191026X |
| DOI: | 10.1515/jisys-2024-0076 |