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Sensors 2017, 17(5), 1076; doi:10.3390/s17051076

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

1
School of Information Science and Technology, Northwest University, Xi’an 710021, China
2
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
3
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)
View Full-Text   |   Download PDF [6067 KB, uploaded 11 May 2017]   |  

Abstract

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
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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.

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