Drift Compensation on Massive Online Electronic-Nose Responses

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

Download Full-text

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Sensors ◽

10.3390/s21227620 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7620

Author(s):

Zhenyi Ye ◽

Yuan Liu ◽

Qiliang Li

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Gas Sensor ◽

Electronic Nose ◽

Recent Progress ◽

Machine Learning Techniques ◽

Learning Methods ◽

Machine Learning Methods ◽

Sensor Drift ◽

Drift Compensation

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Download Full-text

Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose

Sensors ◽

10.3390/s18114028 ◽

2018 ◽

Vol 18 (11) ◽

pp. 4028 ◽

Cited By ~ 8

Author(s):

Tao Liu ◽

Dongqi Li ◽

Jianjun Chen ◽

Yanbing Chen ◽

Tao Yang ◽

...

Keyword(s):

Active Learning ◽

Gas Sensor ◽

Electronic Nose ◽

Efficiency Index ◽

Labelling Efficiency ◽

Data Space ◽

Sensor Drift ◽

Classification Boundary ◽

The Impact ◽

Massive Information

Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work.

Download Full-text