Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment

Accidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is bec...

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Main Author: Gunwoo Lee
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/10/14/4721
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author Gunwoo Lee
author_facet Gunwoo Lee
author_sort Gunwoo Lee
collection DOAJ
description Accidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is becoming common on land, to enhance maritime safety. Specifically, a recurrent neural network (RNN)-based hybrid localization system (RHLS) that provides accurate and efficient user-tracking results is proposed. RHLS performs hybrid positioning by receiving wireless signals, such as Wi-Fi and Bluetooth, as well as inertial measurement unit data from smartphones. It utilizes the RNN to solve the problem of tracking accuracy reduction that may occur when using data collected from various sensors at various times. The results of experiments conducted in an offshore environment confirm that RHLS provides accurate and efficient tracking results. The scalability of RHLS provides managers with more intuitive monitoring of assets and crews, and, by providing information such as the location of safety equipment to the crew, it promotes welfare and safety.
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spelling doaj.art-b6202f4feab1484382e27dfc3d7017ca2023-11-20T06:16:18ZengMDPI AGApplied Sciences2076-34172020-07-011014472110.3390/app10144721Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore EnvironmentGunwoo Lee0Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, KoreaAccidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is becoming common on land, to enhance maritime safety. Specifically, a recurrent neural network (RNN)-based hybrid localization system (RHLS) that provides accurate and efficient user-tracking results is proposed. RHLS performs hybrid positioning by receiving wireless signals, such as Wi-Fi and Bluetooth, as well as inertial measurement unit data from smartphones. It utilizes the RNN to solve the problem of tracking accuracy reduction that may occur when using data collected from various sensors at various times. The results of experiments conducted in an offshore environment confirm that RHLS provides accurate and efficient tracking results. The scalability of RHLS provides managers with more intuitive monitoring of assets and crews, and, by providing information such as the location of safety equipment to the crew, it promotes welfare and safety.https://www.mdpi.com/2076-3417/10/14/4721indoor localizationrecurrent neural networkhybrid positioning
spellingShingle Gunwoo Lee
Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
Applied Sciences
indoor localization
recurrent neural network
hybrid positioning
title Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
title_full Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
title_fullStr Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
title_full_unstemmed Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
title_short Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
title_sort recurrent neural network based hybrid localization for worker tracking in an offshore environment
topic indoor localization
recurrent neural network
hybrid positioning
url https://www.mdpi.com/2076-3417/10/14/4721
work_keys_str_mv AT gunwoolee recurrentneuralnetworkbasedhybridlocalizationforworkertrackinginanoffshoreenvironment