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Multi-Sensor Data Fusion with a Reconfigurable Module and Its Application to Unmanned Storage Boxes.
Lee, Sung-Kyu; Hong, Seung-Hyun; Jun, Won-Ho; Hong, Youn-Sik.
  • Lee SK; Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Korea.
  • Hong SH; Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Korea.
  • Jun WH; Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Korea.
  • Hong YS; Advanced Software Research Center, Incheon National University, Incheon 22012, Korea.
Sensors (Basel) ; 22(14)2022 Jul 19.
Article in English | MEDLINE | ID: covidwho-1938962
ABSTRACT
We present a multi-sensor data fusion model based on a reconfigurable module (RM) with three fusion layers. In the data layer, raw data are refined with respect to the sensor characteristics and then converted into logical values. In the feature layer, a fusion tree is configured, and the values of the intermediate nodes are calculated by applying predefined logical operations, which are adjustable. In the decision layer, a final decision is made by computing the value of the root according to predetermined equations. In this way, with given threshold values or sensor characteristics for data refinement and logic expressions for feature extraction and decision making, we reconstruct an RM that performs multi-sensor fusion and is adaptable for a dedicated application. We attempted to verify its feasibility by applying the proposed RM to an actual application. Considering the spread of the COVID-19 pandemic, an unmanned storage box was selected as our application target. Four types of sensors were used to determine the state of the door and the status of the existence of an item inside it. We implemented a prototype system that monitored the unmanned storage boxes by configuring the RM according to the proposed method. It was confirmed that a system built with only low-cost sensors can identify the states more reliably through multi-sensor data fusion.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Limits: Humans Language: English Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Limits: Humans Language: English Year: 2022 Document Type: Article