DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems
In recent years, emerging digital microfluidic technology has shown great application potential in fields such as biology and medicine due to its simple structure, sample-saving properties, ease of integration, and wide range of manipulation. Currently, due to potential faults in chips during produc...
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MDPI AG
2025-05-01
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| author | Zhijie Luo Bin Zhao Wenjin Liu Jianhua Zheng Wenwen Chen |
| author_facet | Zhijie Luo Bin Zhao Wenjin Liu Jianhua Zheng Wenwen Chen |
| author_sort | Zhijie Luo |
| collection | DOAJ |
| description | In recent years, emerging digital microfluidic technology has shown great application potential in fields such as biology and medicine due to its simple structure, sample-saving properties, ease of integration, and wide range of manipulation. Currently, due to potential faults in chips during production and usage, as well as high safety requirements in their application domains, thorough testing of chips is essential. This study records data using a machine vision-based digital microfluidic driving control system. As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. Subsequently, principal component analysis (PCA) is applied for dimensionality reduction on the clustered data. Using the INFORMER model, we predict changes in droplet motion time and conduct correlation analysis, comparing results with traditional long short-term memory (LSTM), frequency-enhanced decomposed transformer (FEDformer), inverted transformer (iTransformer), INFORMER, and DBSCAN-INFORMER prediction models. Experimental results show that the DBSCAN-PCA-INFORMER model substantially outperforms LSTM and other benchmark models in prediction accuracy. It achieves an R<sup>2</sup> of 0.9864, an MSE of 3.1925, and an MAE of 1.3661, indicating an excellent fit between predicted and observed values.The results demonstrate that the DBSCAN-PCA-INFORMER model achieves higher prediction accuracy than traditional LSTM and other approaches, effectively identifying the health status of experimental devices and accurately predicting failure times, underscoring its efficacy and superiority. |
| format | Article |
| id | doaj-art-d2e057a1dcd24f2c94cda5e1a34e57dd |
| institution | DOAJ |
| issn | 2072-666X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-d2e057a1dcd24f2c94cda5e1a34e57dd2025-08-20T03:14:46ZengMDPI AGMicromachines2072-666X2025-05-0116559410.3390/mi16050594DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic SystemsZhijie Luo0Bin Zhao1Wenjin Liu2Jianhua Zheng3Wenwen Chen4College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaIn recent years, emerging digital microfluidic technology has shown great application potential in fields such as biology and medicine due to its simple structure, sample-saving properties, ease of integration, and wide range of manipulation. Currently, due to potential faults in chips during production and usage, as well as high safety requirements in their application domains, thorough testing of chips is essential. This study records data using a machine vision-based digital microfluidic driving control system. As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. Subsequently, principal component analysis (PCA) is applied for dimensionality reduction on the clustered data. Using the INFORMER model, we predict changes in droplet motion time and conduct correlation analysis, comparing results with traditional long short-term memory (LSTM), frequency-enhanced decomposed transformer (FEDformer), inverted transformer (iTransformer), INFORMER, and DBSCAN-INFORMER prediction models. Experimental results show that the DBSCAN-PCA-INFORMER model substantially outperforms LSTM and other benchmark models in prediction accuracy. It achieves an R<sup>2</sup> of 0.9864, an MSE of 3.1925, and an MAE of 1.3661, indicating an excellent fit between predicted and observed values.The results demonstrate that the DBSCAN-PCA-INFORMER model achieves higher prediction accuracy than traditional LSTM and other approaches, effectively identifying the health status of experimental devices and accurately predicting failure times, underscoring its efficacy and superiority.https://www.mdpi.com/2072-666X/16/5/594digital microfluidicsmicrodropletsDBSCAN-PCA-INFORMER modeltime prediction |
| spellingShingle | Zhijie Luo Bin Zhao Wenjin Liu Jianhua Zheng Wenwen Chen DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems Micromachines digital microfluidics microdroplets DBSCAN-PCA-INFORMER model time prediction |
| title | DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems |
| title_full | DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems |
| title_fullStr | DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems |
| title_full_unstemmed | DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems |
| title_short | DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems |
| title_sort | dbscan pca informer based droplet motion time prediction model for digital microfluidic systems |
| topic | digital microfluidics microdroplets DBSCAN-PCA-INFORMER model time prediction |
| url | https://www.mdpi.com/2072-666X/16/5/594 |
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