Using detrending methods for intelligent processing of construction process monitoring data

Author:

Kagan Pavel1,Parshin Dmitriy1

Affiliation:

1. Moscow State University of Civil Engineering

Abstract

Data processing of monitoring systems of various processes at the stage of construction and operation of buildings requires the development of special tools that belong to the field of artificial intelligence. The trend removal method is one of the ways to preprocess data collected from various sensors and IoT devices that monitor the state of buildings, structures and soil masses during construction and operation. This article analyzes how different approaches to detrending time series affect the performance and accuracy of algorithms for CI computational intelligence models. The analysis compares three approaches: linear detrending, non-linear detrending, and first-order differentiation. Five representative methods are used as CI models: DENFIS dynamic evolving fuzzy neural network, GP Gaussian process, MLP multilayer perceptron, OP-ELM optimally trimmed extremal learning machine, and SVM support vector machine. There are three main conclusions from the experiments performed on the four datasets: 1) detrending does not improve overall performance, 2) the empirical mode decomposition method provides better performance than linear detrending, and 3) first-order differentiation in some cases can be effective and in some cases counterproductive for series with common patterns.

Publisher

RIOR Publishing Center

Subject

Industrial and Manufacturing Engineering,Polymers and Plastics,Business and International Management

Reference36 articles.

1. Nie, S.Y.; Wu, X.Q. A historical study about the developing process of the classical linear time series models. // In Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA); Springer: Berlin, Heidelberg, Germany, 2013; Volume 212, pp. 425–433., Nie, S.Y.; Wu, X.Q. A historical study about the developing process of the classical linear time series models. // In Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA); Springer: Berlin, Heidelberg, Germany, 2013; Volume 212, pp. 425–433.

2. Kenmei, B., Antoniol, G., di Penta, M. Trend analysis and issue prediction in large-scale open source systems. // In 2008 12th European Conference on Software Maintenance and Reengineering; 2008; pp. 73–82., Kenmei, B., Antoniol, G., di Penta, M. Trend analysis and issue prediction in large-scale open source systems. // In 2008 12th European Conference on Software Maintenance and Reengineering; 2008; pp. 73–82.

3. Gao, K., Khoshgoftaar, T.M.: A comprehensive empirical study of count models for software fault prediction. // IEEE Transactions on Reliability 56(2); 2007; pp. 223–236., Gao, K., Khoshgoftaar, T.M.: A comprehensive empirical study of count models for software fault prediction. // IEEE Transactions on Reliability 56(2); 2007; pp. 223–236.

4. Agosto, A.; Cavaliere, G.; Kristensen, D.; Rahbek, A. Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX). J. Empir. Financ.; 2016; pp. 640–663., Agosto, A.; Cavaliere, G.; Kristensen, D.; Rahbek, A. Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX). J. Empir. Financ.; 2016; pp. 640–663.

5. Karlis, D.; Meligkotsidou, L. Multivariate Poisson regression with covariance structure. // Stat. Comput.; 2005; pp. 255–265., Karlis, D.; Meligkotsidou, L. Multivariate Poisson regression with covariance structure. // Stat. Comput.; 2005; pp. 255–265.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Creation of intelligent situational centers at construction sites;Construction and Architecture;2023-12-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3