Smart building's elevator with intelligent control algorithm based on Bayesian networks

Yerzhigit Bapin, Vasileios Zarikas

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation- Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested.

Original languageEnglish
Pages (from-to)16-24
Number of pages9
JournalInternational Journal of Advanced Computer Science and Applications
Volume10
Issue number2
Publication statusPublished - Jan 1 2019

Fingerprint

Intelligent buildings
Elevators
Intelligent control
Bayesian networks
Decision making
Image acquisition
Probability distributions
Learning algorithms
Sensitivity analysis
Learning systems
Sustainable development
Computational complexity
Image processing
Engines
Control systems

Keywords

  • Bayesian network
  • Decision support systems
  • Decision theory
  • Elevator control algorithm
  • Intelligent elevator system
  • Smart building
  • Smart city

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

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abstract = "Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation- Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94{\%} case of the scenarios tested.",
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