In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches

2019 ◽  
Vol 213 ◽  
pp. 640-658 ◽  
Author(s):  
Dušan B. Topalović ◽  
Miloš D. Davidović ◽  
Maja Jovanović ◽  
Alena Bartonova ◽  
Zoran Ristovski ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensors ◽  
Air Pollutants ◽  
Low Cost ◽  
Ambient Air ◽  
Calibration Method ◽  
Field Calibration ◽  
Ambient Air Pollutants ◽  
Network Approaches

scholarly journals Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahrokh Jalili ◽  
Mohammad Hassan Ehrampoush ◽  
Mehdi Mokhtari ◽  
Ali Asghar Ebrahimi ◽  
Faezeh Mazidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Time Series ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Ambient Air ◽  
Multivariate Linear Regression ◽  
Artificial Neural

AbstractThis study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

scholarly journals Ambient air pollutants are associated with morning serum cortisol in overweight and obese Latino youth in Los Angeles

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
C. M. Toledo-Corral ◽  
T. L. Alderete ◽  
M. M. Herting ◽  
R. Habre ◽  
A. K. Peterson ◽  
...  
Keyword(s):  
Hpa Axis ◽  
Air Pollutants ◽  
Latino Youth ◽  
Serum Cortisol ◽  
Ambient Air ◽  
Pubertal Status ◽  
Ambient Air Pollutants ◽  
Morning Cortisol ◽  
Morning Serum Cortisol ◽  
Cortisol Levels

Abstract Background Hypothalamic-pituitary-adrenal (HPA)-axis dysfunction has been associated with a variety of mental health and cardio-metabolic disorders. While causal models of HPA-axis dysregulation have been largely focused on either pre-existing health conditions or psychosocial stress factors, recent evidence suggests a possible role for central nervous system activation via air pollutants, such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM). Therefore, in an observational study of Latino youth, we investigated if monthly ambient NO2, O3, and PM with aerodynamic diameter ≤ 2.5 (PM2.5) exposure were associated with morning serum cortisol levels. Methods In this cross-sectional study, morning serum cortisol level was assessed after a supervised overnight fast in 203 overweight and obese Latino children and adolescents (female/male: 88/115; mean age: 11.1 ± 1.7 years; pre-pubertal/pubertal/post-pubertal: 85/101/17; BMI z-score: 2.1 ± 0.4). Cumulative concentrations of NO2, O3 and PM2.5 were spatially interpolated at the residential addresses based on measurements from community monitors up to 12 months prior to testing. Single and multi-pollutant linear effects models were used to test the cumulative monthly lag effects of NO2, O3, and PM2.5 on morning serum cortisol levels after adjusting for age, sex, seasonality, social position, pubertal status, and body fat percent by DEXA. Results Single and multi-pollutant models showed that higher O3 exposure (derived from maximum 8-h exposure windows) in the prior 1–7 months was associated with higher serum morning cortisol (p < 0.05) and longer term PM2.5 exposure (4–10 months) was associated with lower serum morning cortisol levels (p < 0.05). Stratification by pubertal status showed associations in pre-pubertal children compared to pubertal and post-pubertal children. Single, but not multi-pollutant, models showed that higher NO2 over the 4–10 month exposure period associated with lower morning serum cortisol (p < 0.05). Conclusions Chronic ambient NO2, O3 and PM2.5 differentially associate with HPA-axis dysfunction, a mechanism that may serve as an explanatory pathway in the relationship between ambient air pollution and metabolic health of youth living in polluted urban environments. Further research that uncovers how ambient air pollutants may differentially contribute to HPA-axis dysfunction are warranted.

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

scholarly journals Childhood Exposure to Ambient Air Pollutants and the Onset of Asthma: An Administrative Cohort Study in Québec

2016 ◽  
Vol 124 (8) ◽  
pp. 1276-1282 ◽  
Author(s):  
Louis-Francois Tétreault ◽  
Marieve Doucet ◽  
Philippe Gamache ◽  
Michel Fournier ◽  
Allan Brand ◽  
...  
Keyword(s):  
Cohort Study ◽  
Air Pollutants ◽  
Ambient Air ◽  
Childhood Exposure ◽  
Ambient Air Pollutants
Sign in / Sign up

Export Citation Format

Share Document