Next Article in Journal
Radon Mitigation Approach in a Laboratory Measurement Room
Previous Article in Journal
Sound Source Localization Using Non-Conformal Surface Sound Field Transformation Based on Spherical Harmonic Wave Decomposition
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 1076;

A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things

School of Information Science and Technology, Northwest University, Xi’an 710021, China
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Department of Communication, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 3 March 2017 / Revised: 29 April 2017 / Accepted: 4 May 2017 / Published: 11 May 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [6067 KB, uploaded 11 May 2017]   |  


An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins. View Full-Text
Keywords: pattern prediction; multivariate sequential data; earthen ruin pattern prediction; multivariate sequential data; earthen ruin

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xiao, Y.; Wang, X.; Eshragh, F.; Wang, X.; Chen, X.; Fang, D. A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things. Sensors 2017, 17, 1076.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top