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Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering.

Wenqing Wang1, Yuan Yan2, Rundong Zhang3

  • 1School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China. wwq@xupt.edu.cn.

Sensors (Basel, Switzerland)
|September 28, 2019
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Summary
This summary is machine-generated.

This study introduces an adaptive multi-attribute fusion algorithm for smart city applications, improving industrial control systems. The novel approach enhances sensor data fusion for intelligent lighting systems.

Keywords:
data fusionevidence theoryfuzzy clustering

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Area of Science:

  • Industrial Control Systems
  • Smart City Technology
  • Sensor Data Fusion

Background:

  • Traditional industrial control systems use fixed switching thresholds.
  • Smart cities require adaptive systems that respond to changing conditions.
  • Multi-attribute sensor data fusion is crucial for adaptive control.

Purpose of the Study:

  • To propose a novel multi-attribute fusion algorithm for adaptive threshold setting.
  • To enhance the intelligence of industrial control systems, particularly in smart city contexts.
  • To demonstrate the algorithm's effectiveness in an intelligent lighting system.

Main Methods:

  • Fuzzy clustering for observation grouping.
  • Improved evidence theory for data fusion.
  • Application in a narrowband Internet of Things (IoT) based intelligent lighting system.

Main Results:

  • The proposed algorithm effectively fuses multi-attribute sensor observations.
  • Experimental results in an intelligent lighting system confirm the algorithm's efficacy.
  • Demonstrated adaptability for street light illumination and ambient light conditions.

Conclusions:

  • The developed fusion algorithm provides adaptive thresholding capabilities.
  • It is suitable for various multi-attribute fusion scenarios in smart cities.
  • Enhances the performance of intelligent lighting and other industrial control systems.