scholarly journals A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2089 ◽  
Author(s):  
Mohammad Rahman ◽  
Chalie Charoenlarpnopparut ◽  
Prapun Suksompong ◽  
Pisanu Toochinda ◽  
Attaphongse Taparugssanagorn
Keyword(s):  
False Alarm ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Reduction Method ◽  
Alarm Reduction

scholarly journals Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information

2019 ◽  
Vol 11 (22) ◽  
pp. 2677
Author(s):  
Zihan Li ◽  
Anxi Yu ◽  
Zhen Dong ◽  
Zhihua He ◽  
Tianzhu Yi
Keyword(s):  
False Alarm ◽  
Reduction Method ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Moving Target ◽  
Edge Region ◽  
Edge Information ◽  
D 74 ◽  
Alarm Reduction

VideoSAR (Video Synthetic Aperture Radar) technology provides an important mean for real-time and continuous earth observation, whereas the ever-changing scattering characteristics may destroy the accuracy of target motion perception and bring in massive false alarms subsequently. False alarms emerge easily in the edge region for its sharper variations of the scattering characteristics. Utilizing the gradient difference between the target shadow edge and other edge regions in the image, this letter proposes a VideoSAR false alarm reduction method based on gradient-weighted edge information. By considering the reasonable gradient and area of the overlapping edge region between changing region and background, this method could reduce the amount of false alarms ( P f a = 18 . 4 % ) and retain the correct shadow of moving target ( P d = 74 . 8 % ). Experiments on a real footage verify the excellent effect of the proposed method.

A false alarm reduction method for PWB pattern inspection system

2003 ◽  
Author(s):  
T. Serizawa ◽  
K. Takagi ◽  
K. Hamada ◽  
G. Odawara ◽  
Y. Tamiya ◽  
...  
Keyword(s):  
False Alarm ◽  
Reduction Method ◽  
Inspection System ◽  
Alarm Reduction

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 MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 209
Author(s):  
Davide Marzorati ◽  
Luca Mainardi ◽  
Giulia Sedda ◽  
Roberto Gasparri ◽  
Lorenzo Spaggiari ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Rate ◽  
Mortality Rate ◽  
Early Diagnosis ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

Sign in / Sign up

Export Citation Format

Share Document