• International Journal of Naval Architecture and Ocean Engineering
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• 제11권1호
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• 한국항공우주학회지
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• 제49권1호
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• pp.63-73
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• 2021

본 논문은 공중 혹은 해상배경에 표적과 화염이 동시에 존재할 때, 무인항공기에 장착된 EOTS(Electro-Optical Targeting System; 전자광학 추적장비)가 표적을 추적하기 위해 화염의 영향에 강건하도록 표적을 자동 인식하는 기법을 제안한다. 제안한 기법은 표적과 화염의 적외선 영상을 Gradient Vector Field로 변환하고, 각 Gradient magnitude를 Polynomial Curve Fitting 도구에 적용하여 다항식 계수를 추출 및 얕은 신경망 모델에 학습함으로써, 표적과 화염을 자동으로 인식한다. 확보한 표적 및 화염의 다양한 적외선 영상 DB를 학습데이터, 검증데이터, 시험데이터로 분류하여 제안한 기법의 표적 및 화염 자동 인식 성능을 확인하였다. 본 알고리듬을 활용하여 무인항공기의 자동비행 중 충돌회피, 산불탐지, 공중 및 해상의 목표물을 자동탐지 및 인식하는 분야에 적용될 수 있다.

• 한국해양공학회지
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• 제35권3호
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• pp.173-182
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• 2021

The maneuvering performance of a ship on the actual sea is very different from that in calm water due to wave-induced motion. Enhancement of a ship's maneuverability in waves at the design stage is an important way to ensure that the ship navigates safely. This paper focuses on the maneuvering prediction of a Russian trawler in wind and irregular waves. First, a unified seakeeping and maneuvering analysis of a Russian trawler is proposed. The hydrodynamic forces acting on the hull in calm water were estimated using empirical formulas based on a database containing information on several fishing vessels. A simulation of the standard maneuvering of the Russian trawler was conducted in calm water, which was checked using the International Maritime Organization (IMO) standards for ship maneuvering. Second, a unified model of seakeeping and maneuvering that considers the effect of wind and waves is proposed. The wave forces were estimated by a three-dimensional (3D) panel program (ANSYS-AQWA) and used as a database when simulating the ship maneuvering in wind and irregular waves. The wind forces and moments acting on the Russian trawler are estimated using empirical formulas based on a database of wind-tunnel test results. Third, standard maneuvering of a Russian trawler was conducted in various directions under wind and irregular wave conditions. Finally, the influence of wind and wave directions on the drifting distance and drifting angle of the ship as it turns in a circle was found. North wind has a dominant influence on the turning trajectory of the trawler.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1209-1216
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• 2020

본 연구에서는 무인항공기 원격탐사 기법과 딥러닝 기반 객체인식 알고리즘을 활용한 해안표착폐기물 탐지기법을 제안한다. 항공영상 내에 존재하는 해안표착폐기물을 탐지하기 위해 심층신경망 기반 객체 인식 알고리즘을 제안하였다. PET, 스티로폼, 기타 플라스틱의 3가지 클래스의 이미지 데이터셋으로 심층신경망 모델을 훈련시켰으며, 각 클래스별 탐지 정확도를 Darknet-53과 비교하였다. 이를 통해 해안표착 폐기물을 무인항공기를 통해 성상별 모니터링할 수 있었으며, 향후 본 연구에서 제안하는 방법이 적용될 경우 해변 전체에 대한 성상별 전수조사가 가능하며, 이를 통해 해양환경 감시 분야의 효율성 증대에 기여할 수 있을 것으로 판단된다.

• 융합신호처리학회논문지
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• 제21권4호
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• pp.170-176
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• 2020

본 연구는 선박의 조종성능에 직접 영향을 미치는 방향타의 양력, 항력 및 모멘트를 전산유체역학을 이용하여 연구하였다. 힘과 모멘트에 영향을 미치는 전형적인 선박 방향타 중 하나는 바다에서 조종하는 동안 선수 타이다. 따라서 선박 선수타에 대한 힘과 모멘트를 연구하였다. IFS 선수 타 시리즈 중 균형 IFS 54 BR 15는 연구에 사용되었다. 난류 모델로는 표준 k-epsilon이 연구에 적용되었다. 선수 타의 유체 역학, 특히 양력, 항력 및 모멘트 계수는 서로 다른 공격 각도에 대해 계산되었다. 본 연구에 사용된 유동과 선수 타 사이의 받침각은 0도에서 5도 간격씩 35도까지 총 6개이다. 선박의 운동 성능에 미치는 영향에 대한 계산 결과는 이전 실험 연구 결과와 비교되었다.

• 한국산업융합학회 논문집
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• 제25권1호
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• pp.121-129
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• 2022

With the recent increase in international trade volume the trade volume through ships is also continuously increasing. The treatment of ballast water goes through the following five steps, samples are taken and analyzed at each step, and samples are obtained using a suction pump. These suction pumps have low efficiency and thus need to be improved. In this study, it is to optimize the form of the impeller which affects directly improvements of performance to determine the capacity of suction pump and to fulfill the purpose of this research. To do it, we have carried out parametric design as an input variable, geometric form for the impeller. By conducting the flow analysis for the optimum form, it has confirmed the value of improved results and achieved the purpose to study in this paper. It has selected the necessary parameter for optimizing the form of the pump impeller and analyzed the property using experiment design. And it can reduce the factor of parameter for local optimization from findings to analyze the property of form parameter. To perform MOGA(Multi-Objective Genetic Algorithm) it has generated response surface using parameters for local optimization and conducts the optimization using multi-objective genetic algorithm. with created experiment cases, it has performed the computational fluid dynamics with model applying the optimized impeller form and checked that the capacity of the pump was improved. It could verify the validity concerning the improvement of pump efficiency, via optimization of pump impeller form which is suggested in this study.

• 한국해안·해양공학회논문집
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• 제34권2호
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• pp.27-36
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• 2022

파랑이 구조물과 만나게 되면 강한 처오름이 발생하게 되며, 이러한 처오름은 월파를 야기하게 된다. 월파에 의해 야기된 월파수괴에 의해 구조물 배후면에 강한 낙하파압이 작용하게 된다. 구조물 배후면의 포장체 등을 설계하기 위해서는 월파 등에 의한 낙하파압이 산정되어야 하지만 여전히 불명확한 부분이 있다. 본 연구에서는 경사식구조물을 대상으로 월파에 의한 낙하파압 계측을 위한 수리실험을 수행하였으며, 수리실험에서는 파랑조건, 여유고, 피복재 높이 및 파라펫의 유무에 따른 낙하파압의 변화를 계측하였다. 상치콘크리트에는 월파의 효과적인 저감을 위해 파라펫이 설치되기도 하지만, 파라펫 설치로 인해 배후면에 작용하는 낙하파압은 증가하는 것으로 검토되었다. 수행된 실험결과를 이용하여 경사식구조물의 상치콘트리트 배후면에 작용하는 최대 낙하파압을 산정할 수 있는 경험식을 제안하였다.

• 대한조선학회논문집
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• 제59권4호
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• Fisheries and Aquatic Sciences
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• 제25권4호
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• pp.189-201
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• 2022

Jellyfish is an emerging aquaculture species, farmed for Oriental cuisines and nutraceutical ingredients. This study aimed to examine antioxidative and antimicrobial potentials of various fractions of the jellyfish, Acromitus hardenbergi. The bell and oral arms of the jellyfish were sequentially extracted with petroleum ether (PE), dichloromethane (DCM), chloroform (CHCl₃), methanol (MeOH), and water (H₂O) to extract its bioactive in an increasing polarity gradient. Test fractions were assayed for antiradical activities using electron spin resonance spectrometry, β-carotene-linoleate model and Folin-Ciocalteu assay; and antimicrobial activity against 2 Gram-negative bacteria, 4 Gram-positive bacteria and 2 fungal species using the disc diffusion assay. All fractions were also subjected to Fourier Transform Infrared (FTIR) analysis to identify types of functional groups present. It was found that the hydrophilic extracts (H₂O fractions) possessed the most effective radical scavenging activity (p < 0.05) while the lipophilic extracts (PE fractions) the most active antimicrobial activity, especially against Gram-positive bacteria (p < 0.05). Total oxidation substrates content was found to be highest in the PE fractions of jellyfish bell and oral arms (p < 0.05). FTIR data showed that the H₂O and MeOH fractions contains similar functional groups including -OH, -C=O, -N-H and -S=O groups, while the PE, DCM, and CHCl₃ fractions, the -CH₃, -COOH groups. This study showed that A. hardenbergi contains antioxidants and antimicrobials, thereby supporting the traditional claim of the jellyfish as an anti-aging and health-promoting functional food. Bioassay-guided fractionation approach serves as a critical milestone for the strategic screening, purification, and elucidation of therapeutically significant actives from jellyfish.

• 한국지반공학회논문집
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• 제38권8호
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• pp.17-27
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• 2022

김해평야 퇴적층의 동적 특성을 결정하기 위해 6개의 위치에서 상시미동 수평-수직비(HVSR)법으로 지반 고유진동수(f₀)를 도출하고 High frequency f-k방법과 Modified Spatial Autocorrelation 방법을 적용하여 상시미동 기반 표면파 분석을 수행하였다. 레일리파 분산곡선과 지반 고유진동수를 바탕으로 역해석을 실시하여 전단파 속도 주상도를 도출하였다. 분석 결과에 의하면 김해평야 퇴적층 지역은 1hz 이상의 진동수 대역에서 상당한 규모의 지반운동 증폭이 예상된다. 천부 퇴적층은 대체로 200m/s 내외의 전단파 속도를 보이고 60m~100m 사이의 깊이에서 기반암이 존재할 것으로 추정되며, 다수의 위치에서 천부 퇴적층과 기반암 사이에 V_s=400m/s 가량의 하성 퇴적층이 존재하는 것으로 나타났다. 역해석 결과를 바탕으로 김해평야 퇴적층의 전단파 속도-깊이 모델을 도출하였으며, 본 연구에 따르면 김해평야 퇴적층 지역에서 내진설계일반(KDS 17 10 00)의 S6지반에 해당하는 위치의 면적은 상당할 것으로 예상된다.