# A Sensor Drift Compensation Method with a Masked Autoencoder Module  
[PERSON]  
1 School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (H.-D.J.) 1  
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2 Robot Innovation Group, Samsung Display Co., Yongin-si 17113, Republic of Korea; [EMAIL_ADDRESS] 2  
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1 School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (H.-D.J.) 1  
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3 Chem-Bio Technology Center, Agency for Defense Development, Daejeon 34186, Republic of Korea; [EMAIL_ADDRESS] 3  
[PERSON]  
1 School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (H.-D.J.) 1  
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analysis, partial least squares regression, or independent component analysis [11], it does not account for the nonlinear behavior of the drift. A representative approach to compensate for the drift in deep learning is fine-tuning new sensor data, adjusting the weight of some or all neurons in response to new samples [5]. Adaptive drift correction, which is a method that updates a classifier continuously using new samples, has been implemented in various deep learning structures [5]. Fine-tuning has also been carried out for novel deep learning models such as autoencoders [12], restricted Boltzmann machines [13], deep belief networks [14], and augmented convolutional neural networks [15]. Furthermore, online drift compensation methods, which can update trained models with new samples, have been studied. An online domain adaptation extreme learning machine, a combination of an existing domain adaptation extreme learning machine with an online learning theory, was utilized in [16] for gas identification. To reduce the annotation effort, an active learning method was applied to online drift compensation in [17]. This field of online sensor drift compensation has been expanded not only to gas classification but also to gas concentration estimations, and this research trend regarding sensor drift compensation via fine-tuning methods is still ongoing [18]. Nonetheless, the fine-tuning process can be time-consuming and complex, which may be problematic given the limited onboard memory in sensor systems [5; 15], and there are still many unsolved challenges [18]. Meanwhile, in the field of natural language processing, the concept of prompt-based learning has been actively studied as a substitute for fine-tuning [19; 20; 21]. A prompt is a snippet of context information that is added to the input training data during the training phase. During the training phase, a model learns to associate the prompts with the corresponding inputs and outputs. This method has been shown to be effective for large language models when used for different tasks without fine-tuning [19; 21; 22]. Prompts can be generated by humans or by the model, and the latter is called auto prompting. In this case, a model automatically produces an adequate prompt corresponding to data [23; 24]. The prompts can be classified as discrete prompts or continuous prompts based on their form. Discrete prompts are natural language, while continuous prompts are embedding vectors which function as a prompt [25; 26]. We use the concept of prompt-based learning to design an effective drift compensation method for a deep learning algorithm that estimates gas concentrations. In this paper, we propose a method for training neural networks by augmenting the input sensor data with a prompt which contains sensor drift information.