Next Article in Journal
Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier
Next Article in Special Issue
Exploring Symmetry of Binary Classification Performance Metrics
Previous Article in Journal
A Method of Multiple Attribute Group Decision Making Based on 2-Tuple Linguistic Dependent Maclaurin Symmetric Mean Operators
Previous Article in Special Issue
Intra Prediction of Depth Picture with Plane Modeling
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Symmetry 2019, 11(1), 32; https://doi.org/10.3390/sym11010032

Optimal Rescue Ship Locations Using Image Processing and Clustering

1
Department of Marine Science and Production, Kunsan National University, Gunsan 54150, Jeonbuk, Korea
2
Mokpo Maritime University, Mokpo 58628, Jeonnam, Korea
*
Author to whom correspondence should be addressed.
Received: 15 October 2018 / Revised: 7 December 2018 / Accepted: 11 December 2018 / Published: 2 January 2019
(This article belongs to the Special Issue Symmetry in Computing Theory and Application)
Full-Text   |   PDF [5241 KB, uploaded 2 January 2019]   |  

Abstract

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. View Full-Text
Keywords: clustering-based optimization; location optimization; flood-filling algorithm; marine accident; rescue ship; shortest distance clustering-based optimization; location optimization; flood-filling algorithm; marine accident; rescue ship; shortest distance
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Jung, C.-Y.; Yoo, S.-L. Optimal Rescue Ship Locations Using Image Processing and Clustering. Symmetry 2019, 11, 32.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top