Restoration of multi-channel signal loss using autoencoder with recursive input strategy
Abstract Multi-channel sensor data often suffer from missing or corrupted values due to sensor failures, communication disruptions, or environmental interference. These issues severely limit the accuracy of intelligent systems relying on sensor data integration. Existing data restoration techniques...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98374-5 |
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| author | Jaejun Lee Yonggyun Yu Hogeon Seo |
| author_facet | Jaejun Lee Yonggyun Yu Hogeon Seo |
| author_sort | Jaejun Lee |
| collection | DOAJ |
| description | Abstract Multi-channel sensor data often suffer from missing or corrupted values due to sensor failures, communication disruptions, or environmental interference. These issues severely limit the accuracy of intelligent systems relying on sensor data integration. Existing data restoration techniques often fail to capture complex correlations among sensor channels, especially when data losses occur randomly and continuously. To overcome these limitations, we propose an autoencoder-based data recovery algorithm that recursively feeds reconstructed outputs back into the model to progressively refine estimates. A dynamic termination criterion monitors reconstruction improvements, automatically stopping iterations when further refinements become negligible. This recursive input strategy significantly enhances restoration accuracy and computational efficiency compared to conventional single-step methods. Experiments on multivariate sensor datasets show that the proposed method significantly outperforms the one-time autoencoder restoration method and maintains robust performance across diverse datasets and missing data scenarios. This approach provides a scalable and adaptable solution to ensure data integrity in complex sensor networks, enabling improved reliability and operational efficiency in industrial and technological applications. |
| format | Article |
| id | doaj-art-3cdee8e702f8462cbe3b3a7b91d7d6a7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3cdee8e702f8462cbe3b3a7b91d7d6a72025-08-20T03:14:06ZengNature PortfolioScientific Reports2045-23222025-04-0115111110.1038/s41598-025-98374-5Restoration of multi-channel signal loss using autoencoder with recursive input strategyJaejun Lee0Yonggyun Yu1Hogeon Seo2Korea Atomic Energy Research InstituteKorea Atomic Energy Research InstituteKorea Atomic Energy Research InstituteAbstract Multi-channel sensor data often suffer from missing or corrupted values due to sensor failures, communication disruptions, or environmental interference. These issues severely limit the accuracy of intelligent systems relying on sensor data integration. Existing data restoration techniques often fail to capture complex correlations among sensor channels, especially when data losses occur randomly and continuously. To overcome these limitations, we propose an autoencoder-based data recovery algorithm that recursively feeds reconstructed outputs back into the model to progressively refine estimates. A dynamic termination criterion monitors reconstruction improvements, automatically stopping iterations when further refinements become negligible. This recursive input strategy significantly enhances restoration accuracy and computational efficiency compared to conventional single-step methods. Experiments on multivariate sensor datasets show that the proposed method significantly outperforms the one-time autoencoder restoration method and maintains robust performance across diverse datasets and missing data scenarios. This approach provides a scalable and adaptable solution to ensure data integrity in complex sensor networks, enabling improved reliability and operational efficiency in industrial and technological applications.https://doi.org/10.1038/s41598-025-98374-5Data restorationAutoencoderRecursive input strategyDynamic termination |
| spellingShingle | Jaejun Lee Yonggyun Yu Hogeon Seo Restoration of multi-channel signal loss using autoencoder with recursive input strategy Scientific Reports Data restoration Autoencoder Recursive input strategy Dynamic termination |
| title | Restoration of multi-channel signal loss using autoencoder with recursive input strategy |
| title_full | Restoration of multi-channel signal loss using autoencoder with recursive input strategy |
| title_fullStr | Restoration of multi-channel signal loss using autoencoder with recursive input strategy |
| title_full_unstemmed | Restoration of multi-channel signal loss using autoencoder with recursive input strategy |
| title_short | Restoration of multi-channel signal loss using autoencoder with recursive input strategy |
| title_sort | restoration of multi channel signal loss using autoencoder with recursive input strategy |
| topic | Data restoration Autoencoder Recursive input strategy Dynamic termination |
| url | https://doi.org/10.1038/s41598-025-98374-5 |
| work_keys_str_mv | AT jaejunlee restorationofmultichannelsignallossusingautoencoderwithrecursiveinputstrategy AT yonggyunyu restorationofmultichannelsignallossusingautoencoderwithrecursiveinputstrategy AT hogeonseo restorationofmultichannelsignallossusingautoencoderwithrecursiveinputstrategy |