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 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 Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1555 ◽  
Author(s):  
Estefania Munoz Diaz ◽  
Maria Caamano ◽  
Francisco Sánchez
Keyword(s):  
Pedestrian Navigation ◽  
Compensation Algorithm ◽  
Drift Compensation

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
Sign in / Sign up

Export Citation Format

Share Document