# A Sensor Drift Compensation Method with a Masked Autoencoder Module
## 4 Conclusions  
In this paper, we introduce an effective method for compensating for sensor drift using deep learning algorithms. Our method involves concatenating calibration feature vectors, embedded from a trained calibration feature encoder as prompts, with sensor data, which are then used as inputs to the neural network during training. The CFE receives transfer samples as inputs and encodes the calibration feature vectors which contain sensor drift information, allowing the neural network to learn sensor drifts and output a drift-compensated label based on a new sensor signal. We evaluate the effectiveness of this method by testing it on a 3-year dataset of gas sensor array drifts for six types of gases at different concentrations. Our results show that our method achieves a higher accuracy than a conventional MLP, an MLP with other feature extractors, and even an MLP with additional fine tuning. Our work verifies that representation learning methods like masked autoencoders can efficiently represent sensor drift information from transfer samples that have a complex and nonlinear structure. In addition, we show that training a neural network with informative prompts is effective in terms of model performance. As far as the authors are aware, the proposed methodology is the first proposal of compensating for sensor drift in deep learning models without-fine tuning. The research community can develop this prompt-based learning approach in future studies, leading to active research in the area. In this field, classifying gas types is just as important as gas concentration estimations; however, in this research, this method was only validated via concentration estimations, constituting a limitation. Furthermore, this methodology has only been validated on regression models based on multi-layer perceptrons. Thus, future work will focus on extending the proposed methodology across other tasks and various types of deep learning models.  
Conceptualization, S.K., J.-H.P., H.-D.J. and H.N.; methodology, S.K.; software, S.K.; validation, S.K. and H.-D.J.; formal analysis, S.K., [PERSON]-H.P., H.-D.J. and H.N.; investigation, S.K. and J.-H.P.; resources, S.K., [PERSON]-H.P. and H.N.; data curation, S.K., [PERSON]-H.P., H.-D.J. and H.N.; writing--original draft preparation, S.K., J.-H.P., H.-D.J. and H.N.; writing--review and editing, D.E.C.; visualization, S.K.; supervision, D.E.C.; project administration, D.E.C.; funding acquisition, D.E.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency For Defense Development Grant funded by the Korean Government (UI220039 ZD).  
Not applicable.  
A publicly available dataset was analyzed in this study. The dataset can be found at: [[https://doi.org/10.24432/C5](https://doi.org/10.24432/C5) RP6W]([https://doi.org/10.24432/C5](https://doi.org/10.24432/C5) RP6W) (19 March 2024).  
A Author [PERSON] was employed by the company Samsung Display Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MLP-CFE** & **MLP-AE** & **MLP** & **MLP-PLS** & **MLP-Tuned** \\ \hline time (s/epoch) & 0.0912 & 0.084 & 0.1592 & 0.239 & 0.132 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The training time per epoch (s/epoch) for each model.