• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.265-267
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• 2022

선박 기관시스템이 효율적이고 안이정적인 운용을 위해서는 실시간 상태 모니터링 기반의 이상탐지, 고장진단 더 나아가 고장예측에 따른 대응조치를 할 수 있는 기술이 필요하며 이를 상태기반 유지관리(Condition Based Maintenance, CBM)이라 지칭한다. 해당 기술을 개발 및 확보하기 위해서는 가장 우선적으로 기관시스템에 대한 다양한 고장 데이터가 확보되어야 하며 이후, 확보된 데이터에 대한 특징추출 등 전처리 알고리즘, 고장 진단 및 예측 알고리즘 등을 개발하여야 한다. 본 연구에서는 선박 추진용 엔진 및 발전기 엔진에 대한 상태기반 유지관리 기술의 개발현황과 향후 지속적인 연구 추진방향을 소개하고자 한다.

• Journal of Korea Ship Safrty Technology Authority
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• v.14
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• pp.30-43
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• 2004

국내 소형 어선은, 목선이 80% 이상, FRP선이 20%내외, 강선이 1% 이내를 차지하고 있다. 소형 목선과 FRP선은 크기가 작고 구성물질이 레이더 전자파를 반사키는 강도가 약하므로 중. 대형 선박에서 탐지하지 못하여 충돌에 의한 해난사고가 가장 큰 비율로 발생하고 있다.<중략>

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.227-232
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• 2006

In this paper, a Kalman filter application for GDSS(Group Digital Surveillance System) developed to protect an aquaculture farms is discussed GDSS is composed by a WIWAS(Watching, Identification, Warning, and Action System) and a FDS(Fishery Detection System) that will monitor incoming and outgoing vessels in the aquaculture farms. In the FDS, a tracking function to track vessels without F-AIS(Fishery Automatic Identification System) is needed and the Kalman filter is applied to track vessels around the aquaculture farms. Some simulation results for the multi-objects with white noise is presented and the adaptation possibility for tracking system is discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.286-288
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• 2022

선박의 여러 시스템 중 데이터 통합플랫폼과 화물의 고박센서를 통합 이상치 탐지는 선박의 운항 지원 및 안전에 중요한 역할을 한다. 이 연구에서는 데이터 통합플랫폼과 화물의 고박센서 기반 시스템등의 이기종 통신 시스템의 신뢰성 검증을 위한 시뮬레이터를 개발하고 분석하였고, 관련 절차서를 연구하였다.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.997-1008
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• 2023

Ship detection at sea can be performed in various ways. In particular, satellites can provide wide-area surveillance, and Synthetic Aperture Radar (SAR) imagery can be utilized day and night and in all weather conditions. To propose an efficient ship detection method from SAR images, this study aimed to apply the You Only Look Once Version 5 (YOLOv5) model to Sentinel-1 images and to analyze the difference between individual vs. integrated models and the accuracy characteristics by polarization. YOLOv5s, which has fewer and lighter parameters, and YOLOv5x, which has more parameters but higher accuracy, were used for the performance tests (1) by dividing each polarization into HH, HV, VH, and VV, and (2) by using images from all polarizations. All four experiments showed very similar and high accuracy of 0.977 ≤ AP@0.5 ≤ 0.998. This result suggests that the polarization integration model using lightweight YOLO models can be the most effective in terms of real-time system deployment. 19,582 images were used in this experiment. However, if other SAR images,such as Capella and ICEYE, are included in addition to Sentinel-1 images, a more flexible and accurate model for ship detection can be built.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1125-1130
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• 2014

In this paper, we propose a sound reception system against surrounding noise for ships based on digital signal processing technologies. In order to suppress unwanted surrounding noises, a digital band-pass filter is designed, which the pass-band of the filter is between 70Hz to 820Hz. Also, we develope a sound direction indicating algorithm with 4 microphones. After filtering the audio signals from 4 microphones, the developed sound direction indicating algorithm can indicate 8 directions. In addition, we implement prototype board for the sound reception using a digital signal processor chip and audio codecs, and verify the proposed algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.381-382
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• 2022

흘수는 선체가 물속에 얼마나 잠겨있는지를 나타내는 용어로, 선박에서는 화물의 양을 계산하거나 안정성을 평가하기 위해 흘수를 측정한다. 흘수를 측정하는 방법으로는 항해사가 부두에서 육안으로 확인하거나, 사다리를 타고 내려가 직접 확인하는 방법이 있다. 이러한 방법들은 경우에 따라 흘수 측정이 불가능하거나, 추락의 위험이 항상 존재한다는 문제가 있다. 이러한 문제를 해결하기 위해 드론 등을 통해 카메라로 선박의 흘수선 부근을 촬영하고, 필터링 및 이미지 검출 기법을 사용하여 선박의 흘수선을 탐지하는 방안을 제시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.996-999
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• 2020

노후 선박의 증가로 선체 검사의 필요성이 높아지고 있다. 하지만 선체 벽면의 균열을 찾고 보수하는 작업은 위험성이 높고 효율성이 낮다. 이에 본 논문에서는 선체의 벽면에 진공 흡착하여 장애물에 부딪히지 않고 선체 벽면을 이동하면서 균열을 탐지하는 로봇을 개발하였다. 선체 균열탐지 로봇은 선체뿐만 아니라 사람이 직접 균열을 찾기 힘들거나 위험한 곳에 유용할 것이며 균열로 인한 선박 사고 발생을 줄여줄 것으로 기대된다.