Intelligent Monitoring System of Cremation Equipment Based on Internet of Things

  • Lin Tian
  • Fengguang Huang
  • Lingyu FangEmail author
  • Yu Bai
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)


The cremation of the remains and the burning of relics and sacrifices are the core and key to the funeral and funeral services. With the development of computer and numerical computation, the research of cremation process is becoming more and more important. Changing the traditional combustion method is of great significance for efficient operation of equipment and energy saving and emission reduction. In this paper, we transmit the combustion data collected by the smart sensor to the remote server terminal in real time through GPRS data transmission technology. Then we set up a database for data storage. Logistic regression, random forest, XGBoost algorithm three data analysis models were used to establish a multi-input and multi-output simulation model of cremation equipment. And the actual working conditions in the process of cremation equipment were simulated to provide guidance. An intelligent monitoring system for cremation equipment is established, which integrates computer technology, sensing technology, automatic control technology, network technology and communication technology. This is of great significance for promoting the scientific development of modern funeral business.


Cremation equipment Database Logistic regression Random forest XGBoost 


  1. 1.
    L. Renqing, Green burials the way of the future (China Daily, 06 April 2011)Google Scholar
  2. 2.
    D. Yun Wang, Advanced studying on microsoft SQL server 2008 data mining. Appl. Mech. Mat. 893(20) (2010)Google Scholar
  3. 3.
    Anonymous, Review: microsoft SQL server 2008 R2. Network World (2010)Google Scholar
  4. 4.
    L. Saro, J. Seong Woo, O. Kwan-Young, L. Moung-Jin, The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: a case study of Inje, Korea. Open Geosci. 8(1) (2016)Google Scholar
  5. 5.
    G.M. Fitzmaurice, N.M. Laird, Binary response models and logistic regression (Elsevier Inc., 06 April 2011)Google Scholar
  6. 6.
    C. Su, S. Ju, Y. Liu, Z. Yu, Improving random forest and rotation forest for highly imbalanced datasets. Intell. Data Anal. 19(6) (2015)Google Scholar
  7. 7.
    A. Sim, D. Tsagkrasoulis, G. Montana, Random forests on distance matrices for imaging genetics studies. Stat. Appl. Genetics Mol. Biol. 12(6) (2013)Google Scholar
  8. 8.
    P. Cichosz, Ł. Pawełczak, Imitation learning of car driving skills with decision trees and random forests. Int. J. Appl. Math. Comput. Sci. 24(3) (2014)Google Scholar
  9. 9.
    J. Błaszczyński, J. Stefanowski, Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 150 (2015)Google Scholar
  10. 10.
    A.M. Prasad, L.R. Iverson, A. Liaw, Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2), 181–199 (2006)CrossRefGoogle Scholar
  11. 11.
    B. Pan, Application of XGBoost algorithm in hourly PM2.5 concentration prediction. IOP Conf. Series Earth Environ. Sci. 113(1) (2018)Google Scholar
  12. 12.
    A. Kadiyala, A. Kumar, Applications of python to evaluate the performance of decision tree-based boosting algorithms. Environ. Progress Sustain. Energy 37(2) (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lin Tian
    • 1
  • Fengguang Huang
    • 1
  • Lingyu Fang
    • 2
    Email author
  • Yu Bai
    • 2
  1. 1.The 101 Research Institute of Ministry of Civil AffairsBeijingChina
  2. 2.School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijingChina

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