Prototype-Optimized unsupervised domain adaptation via dynamic Transformer encoder for sensor drift compensation in electronic nose systems

Sun, Jie; Zheng, Hao; Diao, Wenxing; Sun, Zhilin; Qi, Zhengdong; Wang, Xiaozheng

doi:10.1016/j.eswa.2024.125444

Expert Systems with Applications

2025

DOI: 10.1016/j.eswa.2024.125444

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Prototype-Optimized unsupervised domain adaptation via dynamic Transformer encoder for sensor drift compensation in electronic nose systems

Jie Sun,

Hao Zheng,

Wenxing Diao

et al.

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2025

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Cited by 2 publications

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Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data

Fang,

Easwaran,

Genest

et al. 2025

Expert Systems with Applications

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Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data

Fang,

Easwaran,

Genest

et al. 2025

Expert Systems with Applications

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Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Zhou,

Du,

Zhong

et al. 2024

Materials

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Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

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Prototype-Optimized unsupervised domain adaptation via dynamic Transformer encoder for sensor drift compensation in electronic nose systems

Cited by 2 publications

References 22 publications

Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data

Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

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