Polyester (PET) Fabric dyed with Lead (II) acetate-based Colorimetric Sensor for Detecting Hydrogen Sulfide (H2S)

Lee, Junyeop; Gon, Nam; Jeong, Dong Hyuk; Jung, Dong Geon; An, Hee Kyung; Kong, Seong Ho; Jung, Daewoong

doi:10.46670/jsst.2020.29.5.360

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The structure in Figure 1 briefly describes the working principle of the colorimetric sensor array, each of which plays an important role in this detection device. Once a substance needs to be detected, the first step is to collect the image of the detected object before the chemical reaction occurs, then to obtain the image after the reaction, and finally to summarize the information on the detection point where the chemical reaction occurs [12]. The detection process needs further elaboration, because there is a certain gap between the theoretical principle and the actual detection process.…”

Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Chen

Xing

2022

Advances in Multimedia

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The inspection of some substances usually includes two levels. One is the detection of the physical properties of the substance, which can be carried out through a series of physical detection methods and corresponding physical experiments. In the process of chemical detection, the color change after a chemical reaction is an extremely identifying optical property feature. In today’s increasingly mature Internet technology and related computer technology, the combination of this important identification chemical reaction and the former makes the chemical detection method visualized. The biggest difficulty in the application of this technology is to divide the color units produced by the chemical reaction in the contrast color sensor, which directly affects the identification process of the chemical reaction in the subsequent process. In order to better solve this problem, this paper will use a deep convolutional neural network to process the segmentation process of color units. And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. They achieve 79.11%, 76.13%, 77.61%, 91.11% 92.31%, 91.05%, 91.03%, and 91.03%, respectively, and the extraction rate for feature 3 at layer 4 reaches 96.19%. It can be known from the above results that the segmented part of the image generated by the colorimetric sensor array processed by the deep convolutional neural network will be more conducive to the final color unit identification.

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Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Chen

Xing

2022

Advances in Multimedia

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Encoded Image-Based Time Series Classification for Improving Colorimetric Detection of Hydrogen Sulfide (H₂S)

et al. 2022

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Fabrication of High-Performance Colorimetric Fiber-Type Sensors for Hydrogen Sulfide Detection

Jeong¹,

Maeng²,

Lee³

et al. 2022

KSS

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Polyester (PET) Fabric dyed with Lead (II) acetate-based Colorimetric Sensor for Detecting Hydrogen Sulfide (H2S)

Cited by 4 publications

References 10 publications

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Encoded Image-Based Time Series Classification for Improving Colorimetric Detection of Hydrogen Sulfide (H₂S)

Fabrication of High-Performance Colorimetric Fiber-Type Sensors for Hydrogen Sulfide Detection

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Polyester (PET) Fabric dyed with Lead (II) acetate-based Colorimetric Sensor for Detecting Hydrogen Sulfide (H2S)

Cited by 4 publications

References 10 publications

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Encoded Image-Based Time Series Classification for Improving Colorimetric Detection of Hydrogen Sulfide (H2S)

Fabrication of High-Performance Colorimetric Fiber-Type Sensors for Hydrogen Sulfide Detection

Contact Info

Product

Resources

About

Encoded Image-Based Time Series Classification for Improving Colorimetric Detection of Hydrogen Sulfide (H₂S)