A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning

2021 ◽  
Vol 179 ◽  
pp. 175-182
Author(s):  
Keji Mao ◽  
Jinyu Xu ◽  
Runhui Jin ◽  
Yuxiang Wang ◽  
Kai Fang
Keyword(s):  
Carbon Dioxide ◽  
Deep Learning ◽  
Single Channel ◽  
Carbon Dioxide Sensor ◽  
Calibration Algorithm

Synthesis and Characterization of a Composite Zeolite-Metglas Carbon Dioxide Sensor

2005 ◽  
Vol 15 (7) ◽  
pp. 1165-1170 ◽  
Author(s):  
I. G. Giannakopoulos ◽  
D. Kouzoudis ◽  
C. A. Grimes ◽  
V. Nikolakis
Keyword(s):  
Carbon Dioxide ◽  
Synthesis And Characterization ◽  
Carbon Dioxide Sensor

scholarly journals CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

2021 ◽  
Vol 11 (4) ◽  
pp. 456
Author(s):  
Wenpeng Neng ◽  
Jun Lu ◽  
Lei Xu
Keyword(s):  
Deep Learning ◽  
Single Channel ◽  
Sleep Stage ◽  
Learning Model ◽  
Actual Performance ◽  
Feature Extraction Method ◽  
Inductive Bias ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Deep Learning Model

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.

A Hybrid Deep-Learning Approach for Single Channel HF-SSB Speech Enhancement

2021 ◽  
pp. 1-1
Author(s):  
Yantao Chen ◽  
Binhong Dong ◽  
Xiaoxue Zhang ◽  
Pengyu Gao ◽  
Shaoqian Li
Keyword(s):  
Deep Learning ◽  
Speech Enhancement ◽  
Single Channel ◽  
Learning Approach

The Varieties of Gas Analyzers

2018 ◽  
pp. 193-206
Author(s):  
John R. B. Lighton
Keyword(s):  
Carbon Dioxide ◽  
Water Vapor ◽  
Fuel Cell ◽  
Metabolic Rate ◽  
Single Channel ◽  
Dual Wavelength ◽  
Rate Measurement ◽  
Operating Characteristics ◽  
Different Types

All analyzers have strengths and limitations that vary with the technology used, and directly affect their suitability for different types of metabolic rate measurement. It is important for researchers to become familiar with the characteristics of the analyzer(s) they are using. This chapter discusses the chief technologies utilized in aerial gas analyzers for O2, CO2, and water vapor, and their advantages, disadvantages, and operating characteristics. For oxygen analyzers, the single channel and differential heated zirconia cell, single channel and differential fuel cell, and paramagnetic types are described. For carbon dioxide analyzers, the single-wavelength and dual-wavelength nondispersive infrared types are discussed. For water vapor analyzers, the chilled-mirror, infrared and capacitive types are considered.

Mid-infrared carbon dioxide sensor with wireless and anti-condensation capability for use in greenhouses

2018 ◽  
Vol 51 (6) ◽  
pp. 266-273 ◽  
Author(s):  
J. N. Wang ◽  
Q. S. Xue ◽  
G. Y. Lin ◽  
Q. J. Ma
Keyword(s):  
Carbon Dioxide ◽  
Mid Infrared ◽  
Carbon Dioxide Sensor
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