Gas Sensor Drift Compensation by an Optimal Linear Transformation

Liu, Qihe; Zhou, Shijie; Cheng, Xianqiong; Cheng, Hongrong; Zhang, Huabo

doi:10.1109/bigcom.2017.64

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Figure 4 shows the optimal N p corresponding to different N c . We can see that it is easier to get better performance when N p ∈ [9,16].…”

Section: Results and Analysis Of The Proposed Algorithmmentioning

confidence: 90%

“…To reduce the effect of baseline drift, some methods focus on compensation using sensor data obtained during gas exposure. For instance, some researchers try to correct the response by using a compensation function that takes observation data as the input signal [16,17]. Several machine learning (ML) approaches have been used for gas classification, and the construction of a high accuracy classification model is also effective for baseline drift compensation [18,19].…”

Section: Introductionmentioning

confidence: 99%

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A two-stage method for real-time baseline drift compensation in gas sensors

Zhang

Wang

Pan³

et al. 2022

Meas. Sci. Technol.

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Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

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“…Figure 4 shows the optimal N p corresponding to different N c . We can see that it is easier to get better performance when N p ∈ [9,16].…”

Section: Results and Analysis Of The Proposed Algorithmmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A two-stage method for real-time baseline drift compensation in gas sensors

Zhang

Wang

Pan³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This architecture can assess the quality of unidentified tea samples, providing an economical and efficient solution for the tea industry. In another notable development, Brezmes et al designed a sensor system specifically for measuring fruit ripeness, tailored to application-specific requirements [ 14 ]. This system enables a precise and timely evaluation of fruit maturity, assisting in the optimization of harvesting and storage operations.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks

Zhu,

Wu,

Yang

et al. 2024

Sensors

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Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model’s generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques.

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“…In recent years, with the rapid development of the machine olfactory technology, the gas identification systems have been widely applied in many fields, such as food testing, medical diagnosis, and environmental monitoring [1,2,3]. In the gas identification systems, the gas sensors are often used as the core function for sensing, identifying, and measuring different gases.…”

Section: Introductionmentioning

confidence: 99%

“…The key to the sensors is to realize the function of human smell to improve the accuracy of sensors [4,5]. However, the drift phenomenon is inevitable and it cannot be ignored during the use of the sensor over time [1]. There are several forms of the drifting, such as Zero and Span drift and Concept drift.…”

Section: Introductionmentioning

confidence: 99%

Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models

Zhao

Xiao

et al. 2019

Sensors

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Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.

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Gas Sensor Drift Compensation by an Optimal Linear Transformation

Cited by 5 publications

References 15 publications

A two-stage method for real-time baseline drift compensation in gas sensors

A two-stage method for real-time baseline drift compensation in gas sensors

Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks

Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models

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