DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems

TL;DR

This paper introduces a new model combining DBSCAN, PCA, and INFORMER to predict droplet motion times in digital microfluidic systems, improving prediction accuracy and device health monitoring.

Contribution

The novel DBSCAN-PCA-INFORMER model significantly enhances prediction accuracy for droplet motion time in digital microfluidic systems.

Findings

The DBSCAN-PCA-INFORMER model achieves an R2 of 0.9864, showing excellent prediction accuracy.

It outperforms traditional LSTM and other models in predicting droplet motion time.

The model effectively identifies device health status and predicts failure times.

Abstract

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.

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