收藏切换
Node Seismometer RFID Ranging Using the Optimized Convolutional Neural Network Based on Non-Dominated Sorting Genetic Algorithm
收藏切换
Cong PANG1, 2, Chunxiao LIN3, Zhongya LI1, 2, Yong JIANG1, Guoqing CHEN4, Yingying SONG5, *
Journal of Geodesy and Geodynamics | 2025, 45(10) : 1079 - 1084
Less
收藏切换
Journal of Geodesy and Geodynamics | 2025, 45(10): 1079-1084
Node Seismometer RFID Ranging Using the Optimized Convolutional Neural Network Based on Non-Dominated Sorting Genetic Algorithm
Full
Cong PANG1, 2, Chunxiao LIN3, Zhongya LI1, 2, Yong JIANG1, Guoqing CHEN4, Yingying SONG5, *
Affiliations
  • 1 Institute of Seismology, CEA, 40 Hongshance Road, Wuhan 430071, China
  • 2 Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, 40 Hongshance Road, Wuhan 430071, China
  • 3 School of Information Engineering, Qingdao Institute of Technology, 236 South-Fuzhou Road, Jiaozhou 266300, China
  • 4 Mathematical Modeling Research Center, Chengdu Jincheng College, 1 Xiyuan Road, Chengdu 611731, China
  • 5 Department of Computer Science and Engineering, Guangzhou Institute of Technology, 638 Third-Xingtai Road, Guangzhou 510540, China
  • PANG Cong, engineer, majors in artificial intelligence seismology and software development, E-mail:

About Author:

PANG Cong, engineer, majors in artificial intelligence seismology and software development, E-mail:

Published: 2025-10-15 doi: 10.14075/j.jgg.2024.11.499
Outline
收藏切换

Aiming at the problems of wireless node seismometer can not be accurately located and may be lost in the field under complex exploration environment, the study of UHF RFID high-precision ranging and positioning is of great significance. Firstly, we use the received signal strength indicator(RSSI) approximation calculation formula to sieve out the sampling values with large errors. Then, two optimization objective functions of the third-generation non-dominated sorting genetic algorithm(NSGA-Ⅲ) are designed, whose independent variables are one-dimensional convolutional neural network(1D-CNN) hyper-parameters, such as learning rate decreasing factor, initial learning rate, and batch size, and whose dependent variables are determination coefficient(R2) and mean bias error(MBE) of network prediction and theoretical values. Finally, the best hyperparameter values are used to form a new model of NSGAⅢ-1D-CNN, which is designed to improve the stability and accuracy of RFID ranging model. The prediction experiments show that the new model has a small prediction error of node seismometer RFID ranging under 100 rounds of cyclic experiments, and its performance is excellent in several indexes, such as R2, root mean square error(RMSE), mean absolute error(MAE), MBE, with the mean values of 0.977 9, 0.058 6 m, 0.047 2 m, -0.001 3 m, respectively, which has higher ranging and positioning accuracy compared with other models. The new model has certain application value in field physical exploration.

node seismometer  /  RFID ranging  /  one-dimensional convolutional neural network  /  hyperparameter optimization  /  non-dominated sorting genetic algorithm  /  multi objective optimization
庞聪, 林春晓, 李忠亚, 江勇, 陈国庆, 宋莹莹. 利用非支配排序遗传算法优化卷积神经网络研究节点地震仪RFID测距. 大地测量与地球动力学, 2025 , 45 (10) : 1079 -1084 . DOI: 10.14075/j.jgg.2024.11.499
Cong PANG, Chunxiao LIN, Zhongya LI, Yong JIANG, Guoqing CHEN, Yingying SONG. Node Seismometer RFID Ranging Using the Optimized Convolutional Neural Network Based on Non-Dominated Sorting Genetic Algorithm[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (10) : 1079 -1084 . DOI: 10.14075/j.jgg.2024.11.499
  • Scientific Research Fund of Institute of Seismology, CEA and National Institute of Natural Hazards, MEM(IS202236328)
  • Doctoral Special Support Program Project(2025JCKY(B)0018)
  • Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station(WHYWZ202208)
  • Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station(WHYWZ202406)
  • The Spark Program of Earthquake Technology of CEA(XH24025YC)
  • Unveiling and Leadership Project of Hubei Earthquake Agency(ZF-HX-20230005)
  • Research Grants from National Institute of Natural Hazards, MEM(ZDJ2024-31)
Year 2025 volume 45 Issue 10
51
26
Cite this Article
BibTeX
Article Info
doi: 10.14075/j.jgg.2024.11.499
  • Receive Date:2024-11-04
  • Online Date:2026-10-29
  • Published:2025-10-15
Article Data
Affiliations
History
  • Received:2024-11-04
Funding
Scientific Research Fund of Institute of Seismology, CEA and National Institute of Natural Hazards, MEM(IS202236328)
Doctoral Special Support Program Project(2025JCKY(B)0018)
Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station(WHYWZ202208)
Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station(WHYWZ202406)
The Spark Program of Earthquake Technology of CEA(XH24025YC)
Unveiling and Leadership Project of Hubei Earthquake Agency(ZF-HX-20230005)
Research Grants from National Institute of Natural Hazards, MEM(ZDJ2024-31)
Affiliations
    1 Institute of Seismology, CEA, 40 Hongshance Road, Wuhan 430071, China
    2 Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, 40 Hongshance Road, Wuhan 430071, China
    3 School of Information Engineering, Qingdao Institute of Technology, 236 South-Fuzhou Road, Jiaozhou 266300, China
    4 Mathematical Modeling Research Center, Chengdu Jincheng College, 1 Xiyuan Road, Chengdu 611731, China
    5 Department of Computer Science and Engineering, Guangzhou Institute of Technology, 638 Third-Xingtai Road, Guangzhou 510540, China
References
Share
https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.11.499
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT