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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Micromachines (Basel) Ročník 16; číslo 5; s. 594
Hlavní autoři: Luo, Zhijie, Zhao, Bin, Liu, Wenjin, Zheng, Jianhua, Chen, Wenwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 19.05.2025
MDPI
Témata:
Accuracy
Algorithms
Analysis
Clustering
Control systems
Correlation analysis
DBSCAN-PCA-INFORMER model
digital microfluidics
Droplets
Electrodes
Energy consumption
Failure times
Forecasts and trends
Injection molding
Machine vision
microdroplets
Microfluidics
Neighborhoods
Neural networks
Prediction models
Principal components analysis
Silicones
time prediction
Time series
digital microfluidics
microdroplets
time prediction
DBSCAN-PCA-INFORMER model
ISSN:2072-666X, 2072-666X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 R2 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
These authors contributed equally to this work.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi16050594