• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.259-261
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• 2017

SRK-BB(Skill-, Rule-, Knowledge-Based Behavior)는 주어진 사건을 처리할 때 인간이 행하는 행동을 체계적으로 식별하기 위한 하나의 이론이다. 이러한 SRK-BB에 대한 결과는 주어진 임무에 대한 '성공'과 '실패'로 나타낼 수 있다. 만약, 어느 사건에 대한 SRK-BB를 식별할 수 있고, 이에 대한 '성공/실패'의 결과를 알 수 있다면, SRK-BB를 이용하여 이들 사이에 연계된 확률적인 관계를 정립할 수 있다. 한편, 해양사고의 결과를 분석한 해양안전심판원의 재결서 또는 재결요약서에는 다양한 사고(즉, 실패한 사건)에 대해서 해기사가 어떠한 행동을 취했는지 상세하게 기록되어 있다. 이러한 해양안전심판원의 자료를 분석하면 실패한 해양사고에 대한 방대한 해기사의 SRK 분포를 확보할 수 있다. 본 연구의 목적은 다양한 해양사고에 나타난 해기사들의 행동을 SRK-BB로 식별한 후 해기사들이 추후 야기할 수 있는 인적오류를 예측하기 위한 모델 구축에 있다. 인적오류 모델을 구축하기 위해서는 우선 해양사고에 포함된 SRK 분포 분석이 필요하고, 시스템적인 입출력 관계를 통해서 SRK에 의한 인적오류의 결과를 예측하기 위한 예측 모델이 필요하다. 본 연구에서는 해기사의 인적오류에 의한 사고를 어떻게 SRK 분포를 이용하여 예측할 수 있는지에 대한 개념을 설명하고, 해양사고 데이터에서 획득한 SRK 분포의 의미와, SRK 분포를 이용하여 어떻게 해기사가 야기할 사고를 예측할 수 있는지에 대한 연구접근 방법을 소개하고자 한다.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.320-329
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• 2021

In this paper, an empirical model of air bubble size to be applied to an air masker for reduction of underwater radiation noise is presented. The proposed model improves the divergence problem under the low-speed flow condition of the existing model derived using Rayleigh's jet instability model and simple continuity condition by introducing a jet flow velocity of air. The jet flow velocity of air is estimated using the bubble size where the liquid is quiescent. In a medium without flow, the size of the bubble is estimated by an empirical method where bubble formation regime is divided into a laminar-flow range, a transition range, and a turbulent-flow range based on the Reynolds number of the injected air. The proposed bubble size model is confirmed to be in good agreement with the Computational Fluid Dynamics (CFD) analysis result and the experimental results of the existing literature. Using the acoustic inversion method, the air bubble population is estimated from the insertion loss measured during the air injection experiment of the air- masker model in a large cavitation tunnel. The results of the experiments and the bubble size model are compared in the paper.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.389-394
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• 2006

Mid-Frequency bistatic reverberation level is modeled using ray theoretic algorithms. The algorithm assumes multiple forward/backward scatter along with reciprocity in the Propagation paths. The environments modeled are assumed to be range independent in bathymetry, bottom scattering and surface scattering. Mid-Frequency bistatic scattering algorithm is used as a scattering model. A comparison of predicted reverberation versus time with measured data is presented to verify the bistatic reverberation model. The result demonstrates that it is possible to obtain reasonable reverberation Predictions in experimental site.

• Journal of Korea Port Economic Association
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• v.39 no.1
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• pp.17-34
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• 2023

The maritime industry is undergoing significant changes due to digitalization and decarbonization, collectively known as "2D." This study investigates how these transformations are impacting the industry's business models. Since the changes are still ongoing, a cognitive approach was used to derive business models, rather than relying on actual case studies. The study presents experimental maritime business models that correspond to the four types of business model frameworks (or archetypes), along with recent trends for each model. The research results show that new business models are emerging in various areas, including the commercial and technical fields of the maritime industry. This thought-provoking study is significant as a pioneering investigation that will stimulate subsequent case-based research in academia and provide strategic guidance to market participants or policy makers in the maritime industry.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.587-601
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• 2020

The ocean circulation was simulated in the East Sea and Ulleungdo-Dokdo region using ROMS (Regional Ocean Modeling System) model. By adopting the East Sea 3 km model and the HYCOM 9 km data, Ulleungdo 1 km model and Ulleungdo-Dokdo 300 m model were constructed with one-way grid nesting method. During the model development, a correction method was proposed for the distortion of the open boundary data which may be caused by the bathymetry data difference between the mother and child models and the interpolation/extrapolation method. Using this model, a super-high resolution ocean circulation with a horizontal resolution of 300 m near the Ulleungdo and Dokdo region was simulated for year 2018. In spite of applying the same conditions except for the initial and boundary data, the numerical models result indicated significantly different characteristics in the study area. Therefore, these results were compared and verified by using the surface current data estimated by satellites altimeter data and temperature data from NIFS (National Institute of Fisheries Science). They suggest that in general, the improvement of the one-way grid nesting with the HYCOM data on RMSE, Mean Bias, Pattern correlation and Vector correlation is greater in 300 m model than in the 1 km model. However, the nesting results of using East Sea 3 km model showed that simulations of the 1 km model were better than 300 m model. The models better resolved distinct ridge/trough structures of isotherms in the vertical sections of water temperature when using the higher horizontal resolution. Furthermore, Karman vortex street was simulated in Ulleungdo-Dokdo 300 m model due to the terrain effect of th islands that was not shown in the Ulleungdo 1 km model.

• 한국해양학회지
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• v.27 no.4
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• pp.311-323
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• 1992

The study of ocean surface mixed layer modelling has three different approaches: integral models. diffusive models including K theory and higher turbulence closure scheme, and transilient models. None of them is suitable for all occasions because each model has its specific merits and defects. In the present paper, these three types mixed layer models are described, and their relative advantages and applicabilities are discussed in order to guide the researchers who initiate ocean mixed layer study.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.293-294
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• 2017

대기-해양-천문 수치 모델은 주로 3차원 모델이고, 3차원적 해석을 위해서는 사용자가 쉽게 접근하고 사용할 수 있는 3차원 과학적 가시화 도구가 필요하다. 이러한 요구에 대응하기 위하여 무료/오픈소스 기반의 3차원 과학적 가시화 도구인 VAPOR가 사용자 친화적인 방향으로 개발되고 있다. VAPOR는 대기모델 WRF, CAM, GRIMs, 해양모델 MOM4, POP, ROMS의 직접 가시화가 가능하며, VAPOR 자료 형식 변환 과정을 통하여 천문 분야 모델(RAMSES) 가시화도 가능하다. 매년 개발을 통하여, VAPOR는 사용자가 많이 사용하는 일반적인 2, 3차원 표출 기능과 단순 통계 기능을 제공하게 되었으며, 향후 다중 모델 동시 표출 기능도 제공할 계획이다.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.379-384
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• 2022

관계 추출은 두 개체 간의 관계를 식별하는 작업이며, 비정형 텍스트를 구조화시키는 역할을 하는 작업 중 하나이다. 현재 관계 추출에서 다양한 모델에 대한 연구들이 진행되고 있지만, 한국어 관계 추출 모델에 대한 연구는 영어에 비해 부족하다. 따라서 본 논문에서는 NE(Named Entity)태그 정보가 반영된 TEM(Typed Entity Marker)과 의존 구문 그래프를 이용한 한국어 관계 추출 모델을 제안한다. 모델의 학습과 평가 말뭉치는 KLUE에서 제공하는 관계 추출 학습 말뭉치를 사용하였다. 실험 결과 제안 모델이 68.57%의 F1 점수로 실험 모델 중 가장 높은 성능을 보여 NE태그와 구문 정보가 관계 추출 성능을 향상시킬 수 있음을 보였다.