Optimal Rescue Ship Locations Using Image Processing and Clustering

Currently, globalization of the world economy has also resulted in a shipping volume increase. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features such as islands are pre...

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Main Authors: Cho-Young Jung, Sang-Lok Yoo
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Symmetry
Subjects:
Online Access:http://www.mdpi.com/2073-8994/11/1/32
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spelling doaj-8d2145c4392c4769a98ffe0bc47444f22020-11-25T00:58:12ZengMDPI AGSymmetry2073-89942019-01-011113210.3390/sym11010032sym11010032Optimal Rescue Ship Locations Using Image Processing and ClusteringCho-Young Jung0Sang-Lok Yoo1Department of Marine Science and Production, Kunsan National University, Gunsan 54150, Jeonbuk, KoreaMokpo Maritime University, Mokpo 58628, Jeonnam, KoreaCurrently, globalization of the world economy has also resulted in a shipping volume increase. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features such as islands are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites, such as in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest pixel value between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance between rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.http://www.mdpi.com/2073-8994/11/1/32clustering-based optimizationlocation optimizationflood-filling algorithmmarine accidentrescue shipshortest distance
collection DOAJ
language English
format Article
sources DOAJ
author Cho-Young Jung
Sang-Lok Yoo
spellingShingle Cho-Young Jung
Sang-Lok Yoo
Optimal Rescue Ship Locations Using Image Processing and Clustering
Symmetry
clustering-based optimization
location optimization
flood-filling algorithm
marine accident
rescue ship
shortest distance
author_facet Cho-Young Jung
Sang-Lok Yoo
author_sort Cho-Young Jung
title Optimal Rescue Ship Locations Using Image Processing and Clustering
title_short Optimal Rescue Ship Locations Using Image Processing and Clustering
title_full Optimal Rescue Ship Locations Using Image Processing and Clustering
title_fullStr Optimal Rescue Ship Locations Using Image Processing and Clustering
title_full_unstemmed Optimal Rescue Ship Locations Using Image Processing and Clustering
title_sort optimal rescue ship locations using image processing and clustering
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2019-01-01
description Currently, globalization of the world economy has also resulted in a shipping volume increase. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features such as islands are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites, such as in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest pixel value between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance between rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.
topic clustering-based optimization
location optimization
flood-filling algorithm
marine accident
rescue ship
shortest distance
url http://www.mdpi.com/2073-8994/11/1/32
work_keys_str_mv AT choyoungjung optimalrescueshiplocationsusingimageprocessingandclustering
AT sanglokyoo optimalrescueshiplocationsusingimageprocessingandclustering
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