• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.122-122
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• 2018

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.81-81
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• 2017

• Journal of the Korean Society of Marine Environment & Safety
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• v.2 no.1
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• pp.121-123
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• 1996

Technical developments, particularly in communications, increase the potential for effective maritime search and rescue(SAR). The paper identifies some of the questions those seeking to proveid effective SAR service should ask, and outlines possible answers.

• Journal of Ocean Engineering and Technology
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• v.37 no.4
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• pp.145-157
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• 2023

Due to a recent increase in maritime activities in South Korea, the frequency of maritime distress is escalating and poses a significant threat to lives and property. The aim of this study was to validate a drift trajectory prediction technique to help mitigate the damages caused by maritime distress incidents. In this study, OpenDrift was verified using satellite drifter data from the Korea Hydrographic and Oceanographic Agency. OpenDrift is a Monte-Carlo-based Lagrangian trajectory modeling framework that allows for considering leeway, an important factor in predicting the movement of floating marine objects. The simulation results showed no significant differences in the performance of drift trajectory prediction when considering leeway using four evaluation methods (normalized cumulative Lagrangian separation, root mean squared error, mean absolute error, and Euclidean distance). However, leeway improved the performance in an analysis of location prediction conformance for maritime search and rescue operations. Therefore, the findings of this study suggest that it is important to consider leeway in drift trajectory prediction for effective maritime search and rescue operations. The results could help with future research on drift trajectory prediction of various floating objects, including marine debris, satellite drifters, and sea ice.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.41-41
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• 2018

• Journal of Korean Society of Forest Science
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• v.96 no.1
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• pp.70-76
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• 2007

The main purpose of this study was to realize Mountain Search and Rescue System based on Ubiquitous Sensor Network for applying search and rescue operations in the mountains. The on-site survey was conducted in order to test the possibility of data reception rate and sensor network stability, ten nodes was installed on the trail at 100m intervals Unson waterfall to Kwanumbong in Kyeryongsan national park. The results showed that the data reception rate was over 90.0% at least. To test sensor network stability, nodes were randomly turned off one by on. The result was 100% functionality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.676-682
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• 2018

In wireless sensor networks, we propose an optimization algorithm in order to minimize the consumed energy of nodes for maritime search and rescue. In the marine environment, search and rescue operations are mainly performed on the surveillance side and passively on the rescued side. A self-configurable wireless sensor network can build a system that can send rescue signals in the operations. A simulated annealing algorithm is proposed to minimize the consumed energy of nodes in the networks with many nodes. As the density of nodes becomes higher, the algorithmic computation will increase highly. To search the good result in a proper execution time, the proposed algorithm proposes a new neighborhood generating operation and improves the efficiency of the algorithm. The proposed algorithm was evaluated in terms of the consumed energy of the nodes and algorithm execution time, and the proposed algorithm performed better than other optimization algorithms in the performance results.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.1
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• pp.34-39
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• 2015

KCG, responsible Agency of maritime SAR operation has been known to address the marine casualties well, however, it was criticized because KCG failed to take prompt and effective response to the recent Ferry disaster at the very early stage. In this regards, the authors analyze the characteristics of marine incident with historical data, and also review and examine the national SAR system focusing on SAR coordination and human resources to make recommendation and suggestion in order to minimize the loss of lives to future marine incidents. As a result, KCG shall augment its capabilities, inter alia, fast libeboats and rescue helicopters for prompt response to marine incidents at the coastal waters, and KCG shall re-adjust the establishment of Rescue Coordination Center to improve operational problems of current system in accordance with IMO guidance, KCG is required to introduce mandatory training, qualification and certificate system to enhance the professionalism of SAR personnel.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.288-292
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• 2016

In this paper, we propose an infrared image based human victim recognition method for a search and rescue robot in dark environments, like general disaster situations. For recognizing a human victim, an infrared camera on a RGB-D camera, Microsoft Kinect, is used. The contrast and brightness of the infrared image are first improved by histogram equalization, and the noise on the image is removed by morphological operation and Gaussian filtering. For recognizing a human victim, the binarization and blob labeling methods are applied to the improved image. Finally, for verifying the effectiveness and feasibility of the proposed method, an experiment for human victim recognition is carried out in a dark environment.