• Journal of the Society of Naval Architects of Korea
• /
• v.58 no.5
• /
• pp.330-338
• /
• 2021

This paper focuses on the improvement of the influence coefficient matrix method for estimation of local ice load on the icebreaking research vessel ARAON. The influence coefficient matrix relates ice pressure on the hull plate to the measured/calculated hull strain/stress. Conventionally von Mises equivalent stresses representing hull stresses and ice pressure acting on the hull plate are utilized to assemble the influence coefficient matrix. Because of the three dimensional features of the ship-ice collision process, an enhanced method to assemble the influence coefficient matrix is derived considering ice loads in the X, Y, and Z direction simultaneously. Furthermore the location of ice loads acting on hull-plate may fall outside the measuring sensor area, and the enhanced influence coefficient matrix is modified to reduce the difference between the actual and the estimated ice loads by expanding the domain outward from the sensor area. The developed method for enhanced influence coefficient matrix is applied to IBRV ARAON during the 2019 Antarctic ice field test and the local ice loads in three directions are efficiently calculated compared to those by a conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1195-1202
• /
• 2016

Recently, a lot of studies that applying the big data technology to various fields, are progressing actively. In the maritime domain, the big data is the meaningful information which makes and gathers by the navigation and communication equipment from the many ships on the ocean. Also, importance of the maritime safety is emphasized, because maritime accidents are rising with increasing of maritime traffic. To support prevention of maritime accidents, in this paper, we developed a vessel traffic display and statistic system based on AIS messages from the many vessels of maritime. Also, to verify the developed system, we conducted tests for vessel track display function and vessel traffic statistic function based on two test scenarios. Therefore, we verified the effectiveness of the developed system for vessel tracks display, abnormal navigation patterns, checking failure of AIS equipments and maritime traffic statistic analyses.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.5
• /
• pp.93-102
• /
• 2020

The design of large complex mechanical systems, such as automobile, aircraft, and ship, is a kind of Multidisciplinary Design Optimization (MDO) because it requires both experience and expertise in many areas. With the rapid development of technology and the demand to improve human convenience, the complexity of these systems is increasing further. The design of such a complex system requires an integrated system design, i.e., MDO, which can fuse not only domain-specific knowledge but also knowledge, experience, and perspectives in various fields. In the past, the MDO relied heavily on the designer's intuition and experience, making it less efficient in terms of accuracy and time efficiency. Process integration and the design optimization framework mainly support MDO owing to the evolution of IT technology. This paper examined the procedure and methods to implement an efficient MDO with reasonable effort and time using RCE, an open-source PIDO framework. As a benchmarking example, the authors applied the proposed MDO methodology to a bulk carrier's conceptual design synthesis model. The validity of this proposed MDO methodology was determined by visual analysis of the Pareto optimal solutions.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.4
• /
• pp.374-381
• /
• 2020

According to international regulations, it is mandatory for navigators or engineers to acquire suitable skillsets before their designation as a duty officer on board. One of the most important elements is Maritime English (ME), wherein students are taught a required set of basic skills that enable them to process various documents related to accidents, ship conditions, and inspections. Students have to be equipped not only with the use of general English skills but also with the coherent use of technical terms and phrases. However, due to the unique circumstances that exist in the maritime domain, the methods used for imparting maritime knowledge and the manner in which it is evaluated are restricted. Hence, this study aims to utilize an online Maritime English learning and testing platform that can be accessed on smart devices to analyze its impact on the students' learning process. An experiment was conducted on two groups of cadets, one that used the platform and another group that did not. After six-week, the experiment results showed a significant difference between the ME test scores of the two groups. The test scores were further analyzed by incorporating the students' personal elements to measure the ef icacy of the ME test platform. Therefore, the learning and evaluation processes are expected to be implemented in ways that are appropriate and convenient to specific circumstances and be widely used in the field of maritime education in the future.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.6
• /
• pp.472-483
• /
• 2013

Actual maritime exercise for improving the capability of submarine sonar operator leads to a lot of cost and constraints. Sonar simulator maximizes the capability of sonar operator and training effect by solving these problems and simulating a realistic battlefield environment. In this study, a passive sonar simulator algorithm is suggested, where the simulator is divided into three modules: maneuvering module, noise source module, and sound propagation module. Maneuvering module is implemented in three-dimensional coordinate system and time interval is set as the rate of vessel changing course. Noise source module consists of target noise, ocean ambient noise, and self noise. Target noise is divided into modulated/unmodulated and narrowband/broadband signals as their frequency characteristics, and they are applied to ship radiated noise level depending on the vessel tonnage and velocity. Ocean ambient noise is simulated depending on the wind noise considering the waveguide effect and other ambient noise. Self noise is also simulated for flow noise and insertion loss of sonar-dome. The sound propagation module is based on ray propagation, where summation of amplitude, phase, and time delay for each eigen-ray is multiplied by target noise in the frequency domain. Finally, simulated results based on various scenarios are in good agreement with generated noise in the real ocean.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.1
• /
• pp.184-192
• /
• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.