• 한국해양공학회지
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• 제26권1호
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• pp.60-63
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• 2012

The accident risks in the marine environment are increasing because of the tendency to build faster and larger ships. To secure ship safety, risk-based ship design (RBSD) was recently suggested based on a formal safety assessment (FSA). In the process of RBSD, a ship designer decides which risk reduction option is most cost-effective in the design stage using a cost-benefit analysis (CBA). There are three dimensions of risk in this CBA: fatality, environment, and asset. In this paper, we present an approach to estimate the environmental costs based on the size of an oil tanker involved in an accident using a neural network. An appropriate neural network model is suggested for the estimation,and the neural network is trained using IOPCF data. Finally,the learned neural network is compared with the cost regression equation by IMO MEPC 62/WP.13 (2011).

• 한국항행학회논문지
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• 제16권2호
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• pp.278-286
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• 2012

향후 대용량 정보 제공 서비스를 위한 선박용 광 네트워크 감시 시스템의 하드웨어와 소프트웨어를 설계 제작하였고, 개발된 광 네트워크 감시 시스템이 선박이라는 특수 상황과 관련 있는 선로 연장, 매크로밴딩, 이물질이 부착된 광 커넥터에 의한 손실 등 3가지의 이벤트를 정확히 모니터링하는지를 실험을 통해 확인해 보았다. 감시 시스템의 하드웨어는 선박의 네트워크 구조에 맞는 chained branch와 dark fiber 병합 방식으로 설계 제작하였고, 3가지 이벤트에 대한 감시 시험 결과 3가지 모두 5 m 이내의 범위로 모니터링하는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.86-90
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• 2021

The article assesses the level of navigation safety, in theoretical terms, defines the complexity of managing navigational risks in practice. The issues of assessing the navigational safety have been studied due to the importance and relevance of the issue in question, however, due to the great complexity of the problem under consideration, the article considers and indicates the directions for the development of the solution of the given direction, where, first of all, it became necessary to analyze the issue of assessing the levels of navigation risks when navigating vessels of various types in difficult navigation conditions.

• 한국정밀공학회지
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• 제19권9호
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• pp.106-110
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• 2002

A need fer stable and comfortable cabins in the high-speed passenger ships has increased. For active control of the motion of the ship cabin, a few control algorithms have been applied to the three dimensional real models in the vibration basin. Experimental results show that the feedforward neural network with a linear feedback controller is one of the promising control algorithms for this active control.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.241-247
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• 2021

In the article, an analysis of the factors was carried out, which injected into the efficiency of the function of navigation systems in the minds of unimportance. Carrying out analyzes allowing for the visibility of relevant direct adjustments to the effectiveness of the function Inertial navigation systems in the minds of non-value. The designation of the navigation system was assigned to a complex of navigation systems on ships processing of the vector of navigation parameters, so that they can be victorious in the control systems of the ship's collapse, and the safety of floating is safe.

• 한국항만학회지
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• 제6권2호
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• pp.3-24
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• 1992

In the fuzzy control of shop the qualitative knowledge and information that the ship's operators have acquired through their experience can be logically described by the Linguistic control Rule (LCR). The algorithm of the control is made of the LCR and the control of the shop is performed by processing this algorithm implementing a computer. The problem in the fuzzy control is that it is very difficult to describe qualitative human knowledge in the LCR correctly. To tackle this difficulty a Fuzzy Neural Network (FNN) was introduced in this paper. The characteristics of the multi-layer FNN control system applied to the ship's steering system is investigated through the computer simulation, and the results were compared with those of the ordinary fuzzy control system of a ship. The results showed that the FNN method is a very effective to translate human knowledge into the LCR.

• 해양환경안전학회지
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• 제28권7호
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• pp.1274-1280
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• 2022

전산유체역학을 사용하는 일반적인 선박의 저항성능 평가는 많은 시간과 비용이 필요하며, 이를 줄이기 위한 다양한 방법이 연구되고 있다. 선박의 주요 치수나 단면을 이용하는 기존의 방법들은 선형에 크게 좌우되는 저항성능을 추정하는데 한계가 있다. 본 논문에서는 선형 격자의 기하학적 정보를 입력으로 선체 표면의 저항성능을 빠르게 추정할 수 있는 심층신경망 모델을 제안한다. Perceiver IO 기반의 제안하는 심층신경망 모델은 시간 단계별로 계산이 필요한 전산유체역학 기법과 달리 바로 저항성능 추정이 가능하며, 저속비대선의 일종인 50K 탱커 선박을 대상으로 한 데이터집합에서 평균 1% 미만의 오차로 저항성능을 추정하는 결과를 보인다.

• Structural Engineering and Mechanics
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• 제30권6호
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• pp.679-698
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• 2008

On marine vessels, delicate instruments such as navigation radars are normally mounted on ship masts. However the vibrations at the top of mast where the radar is mounted often cause serious deterioration in radar-tracking resolution. The most serious problem is caused by the rotational vibrations at the top of mast that may be due to wind loading, inertial loading from ship rolling and base excitations induced by the running propeller. This paper presents a method of semi-active vibration control using magneto-rheological (MR) dampers to reduce the rotational vibration of the mast. In the study, the classical optimal control algorithm, the independent modal space control algorithm and the double input - single output fuzzy control algorithm are employed for the vibration control. As the phenomenological model of an MR damper is highly nonlinear, which is difficult to analyse, a back- propagation neural network is trained to emulate the inverse dynamic characteristics of the MR damper in the analysis. The trained neural network gives the required voltage for each MR damper based on the displacement, velocity and control force of the MR damper quickly. Numerical simulations show that the proposed control methods can effectively suppress the rotational vibrations at the top of mast.

• 한국항행학회논문지
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• 제24권4호
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• pp.267-272
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• 2020

선박의 상태를 공간 제약 없이 광범위하게 측정하고 이를 정밀히 분석하기 위한 수단들 중 하나로 무선 네트워크의 활용이 하나의 방법이 될 수 있다. 여러 개의 센서들을 적절한 곳에 배치하고 이들 간 네트워크가 자동으로 구성이 되고나면 선박의 상태를 한 장소에서 모니터링 할 수 있다. 아울러 배치된 모든 센서들이 동일한 시각정보를 갖는다면, 이들로부터 감지된 동일한 이벤트에 대하여 위치 또는 이동방향 등도 알아낼 수 있다. 따라서 동기화된 센서들의 활용은 메타정보의 생산을 위한 중요한 요소가 될 수 있다. 이 논문은 선박에서의 효율적 활용을 위한 센서 간 시각동기 알고리즘을 제안한다.

• Ocean Systems Engineering
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• 제10권4호
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.