Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

被引：4
|
作者：
Hua, Hongling [1 ,2 ]
Xie, Xiaohui [1 ,2 ]
Sun, Jinjin [1 ,2 ]
Qin, Ge [1 ,2 ]
Tang, Caiyan [1 ,2 ]
Zhang, Zhen [1 ,2 ]
Ding, Zhaoqiang [1 ,2 ]
Yue, Weiwei [1 ,2 ]
机构：
[1] Shandong Normal Univ, Sch Phys & Elect, Shandong Prov Key Lab Med Phys & Image Proc Techn, Jinan 250014, Shandong, Peoples R China
[2] Shandong Normal Univ, Inst Mat & Clean Energy, Jinan 250014, Shandong, Peoples R China
来源：
ADVANCES IN CONDENSED MATTER PHYSICS | 2018年 / 2018卷
基金：
中国国家自然科学基金;
关键词：
VAPOR-DEPOSITION; NICKEL FOAM; NANOPARTICLES; FLUORESCENCE; DISCRIMINATION; FABRICATION;
D O I：
10.1155/2018/2361571
中图分类号：
O469 [凝聚态物理学];
学科分类号：
070205 ;
摘要：
A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.
引用
收藏
页数：8
相关论文
共 50 条
  • [21] Power System Load Forecasting by Improved Principal Component Analysis and Neural Network
    Liu Xiao-fei
    Shang Li-qun
    2016 IEEE INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION (ICHVE), 2016,
  • [22] A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases
    Harun Uğuz
    Journal of Medical Systems, 2012, 36 : 61 - 72
  • [23] Prediction of Converter Tapping Weight Based on Principal Component Analysis-Whale Optimization Algorithm-Backpropagation Algorithm Neural Network Model
    Xue, Chao
    Xu, Weili
    Bao, Yanping
    Zhao, Lihua
    Liu, Xin
    Zheng, Ruixuan
    STEEL RESEARCH INTERNATIONAL, 2024, 95 (03)
  • [24] A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases
    Uguz, Harun
    JOURNAL OF MEDICAL SYSTEMS, 2012, 36 (01) : 61 - 72
  • [25] PALMPRINT RECOGNITION BASED ON PRINCIPAL COMPONENT NEURAL NETWORK
    Ghandehari, Azadeh
    VISAPP 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, 2011, : 156 - 161
  • [26] Pictorial Image Analysis by neural network Principal Component Analysis
    Nicole, S
    Pistolesi, G
    CYBERNETICA, 1996, 39 (04): : 357 - 369
  • [27] Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis
    Wang, Qing
    Zhou, Yewang
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, 2016, 127
  • [28] Teaching Quality Assessment Based on Principal Component Analysis and Elman Neural Network
    Hu, Shuai
    Gu, Yan
    Jiang, Hua
    2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1, 2015, : 443 - 446
  • [29] Optimization of Machining Parameters Based on Principal Component Analysis and Artificial Neural Network
    Yuan, Lei
    Zeng, Shasha
    PROCEEDINGS OF 2019 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND ROBOTICS ENGINEERING (ICMRE 2019), 2019, : 46 - 49
  • [30] Productivity prediction method based on analysis model of principal component neural network
    Li Mei
    Cluster Computing, 2019, 22 : 4389 - 4395
12345