Gas Sensor Drift Compensation by an Optimal Linear Transformation

2017 ◽  
Author(s):  
Qihe Liu ◽  
Shijie Zhou ◽  
Xianqiong Cheng ◽  
Hongrong Cheng ◽  
Huabo Zhang
Keyword(s):  
Gas Sensor ◽  
Linear Transformation ◽  
Sensor Drift ◽  
Optimal Linear ◽  
Drift Compensation

scholarly journals Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 78
Author(s):  
Jianhua Cao ◽  
Tao Liu ◽  
Jianjun Chen ◽  
Tao Yang ◽  
Xiuxiu Zhu ◽  
...  
Keyword(s):  
Active Learning ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Effective Means ◽  
Learning Paradigm ◽  
Class Label ◽  
Learning Framework ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Noisy Labels

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.

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

Sensors ◽  
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.

Chemical gas sensor drift compensation using classifier ensembles

2012 ◽  
Vol 166-167 ◽  
pp. 320-329 ◽  
Author(s):  
Alexander Vergara ◽  
Shankar Vembu ◽  
Tuba Ayhan ◽  
Margaret A. Ryan ◽  
Margie L. Homer ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Classifier Ensembles ◽  
Sensor Drift ◽  
Drift Compensation

Optimal linear transformation for MRI feature extraction

1996 ◽  
Vol 15 (6) ◽  
pp. 749-767 ◽  
Author(s):  
H. Soltanian-Zadeh ◽  
J.P. Windham ◽  
D.J. Peck
Keyword(s):  
Feature Extraction ◽  
Linear Transformation ◽  
Optimal Linear

Sensor Drift Compensation Algorithm based on PDF Distance Minimization

2009 ◽  
Author(s):  
Namyong Kim ◽  
Hyung-Gi Byun ◽  
Krishna C. Persaud ◽  
Jeung-Soo Huh ◽  
Matteo Pardo ◽  
...  
Keyword(s):  
Compensation Algorithm ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Distance Minimization

scholarly journals Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 742 ◽  
Author(s):  
Zhiyuan Ma ◽  
Guangchun Luo ◽  
Ke Qin ◽  
Nan Wang ◽  
Weina Niu
Keyword(s):  
Extreme Learning Machine ◽  
Domain Adaptation ◽  
Online Sensor ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Learning Machine
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