• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.11a
• /
• pp.192-193
• /
• 2023

The International Association of Navigational Aids to Navigation (IALA) utilizes the S-200 standard model and test data for product specification development and validation related to generating datasets compliant with the S-201 data format. Currently, they are focusing on improving the S-200 testbed DB performance and extending the IALA Questionnaire survey functionality. In this study, we investigate the design and implementation of features required to manage additional questionnaires in the S-200 testbed.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.26 no.1
• /
• pp.76-80
• /
• 1990

DC-servomotor acts an important role in robots and manipulatirs. But the precise control of DC-motor is difficult by a using usual linear controller because of the nonlinear characteristics of DC-motor. This study suggests the use of fuzzy controller in the control of DC-servomotor speed. The fuzzy controller is designed from a fuzzy model which can represent nonlinear systems very well. Hence the fuzzy controller is very useful in the control of nonlinear systems such as DC-motor. We construct a fuzzy model of DC-servomotor, design a fuzzy controller from the fuzzy model, and compare that with a linear controller. When we use the fuzzy controller, the static ripples are reduced and the rise time is required 20% less than in using a linear controller.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.34 no.1
• /
• pp.19-24
• /
• 2021

This paper presents a method to generate three-dimensional AR/VR models using the information in Isogen data files (IDFs). An IDF is an output file produced by ISOGEN that contains piping isometric drawings. A piping isometric drawing is used for pipeline installation in the shipyard; therefore, the drawing describes assembly information with symbolic features, not with detailed geometric features. An IDF specifies relationships between piping routes and components with three-dimensional points and tag information as well as the bill of the materials of a pipeline. The key idea of this paper is that AR/VR models can be generated with the piping route points data and piping components tag information in real time, without any conversion of standard data exchange file formats, such as STP, IGES, and SAT. This paper describes IDF data structure and suggests the geometry generation process with IDF data and parametric functions.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.18 no.6
• /
• pp.545-550
• /
• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• Journal of Navigation and Port Research
• /
• v.28 no.3
• /
• pp.161-167
• /
• 2004

Maritime safety and marine environmental protection are the most important topic in marine society. But, so many marine accidents have been occurred with the development of marine transportation industry. On the other side, ship is being operated under a highly dynamic environment and many factors are related with ship's collision Nowadays, the increasing tendency to the human errors of ship's collision is remarkable, and the investigation of the human errors has been heavily concentrated. This study analysed on the human errors of ship's collision related to the negligence of lookout and classified basic error type using GEMS(Generic Error Modeling System) dynamic model.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.7
• /
• pp.1274-1280
• /
• 2022

The resistance performance evaluation of general ships using computational fluid dynamics requires a lot of time and cost, and various methods are being studied to reduce the time and cost. Existing methods using main particulars or cross sections of ships have limitations in estimating resistance performance that is greatly dependent on the shape of the ship. In this paper, we propose a deep neural network model that can quickly predict the resistance performance of the hull surface by inputting the geometric information of the hullform mesh. The proposed deep neural network model based on Perceiver IO can immediately predict resistance performance, unlike computational fluid dynamics techniques that require calculation in each time step. It shows the result of estimating the resistance performance with an average error of less than 1% in the data set for a 50 K tanker ship, a type of low-speed full ship.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.13-21
• /
• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.7 no.1
• /
• pp.22-29
• /
• 2004

An international project, known as Argo, for collecting data on temperature, salinity and velocity of currents in the world's oceans, has been started in the year 2000 and the full Argo array of approximately 3000 floats will be deployed by 2006. 18 countries deployed 1,023 floats, which are operating in the ocean of the world as of December 2003. In the present study, we tried to predict float distribution and a rate of drifting ashore of the floats after their termination based upon a product of the ocean general circulation model of JAMSTEC (Japan Marine Science and Technology Center). We first evaluated reliability of the model prodilct quantitatively by comparing trajectories of surface buoys of WOCE Surface Velocity Program (SVP) and those predicted by the model surface current field. It is found that the model is acceptable for practical application to deploy floats and to estimate those trajectories. 653 particles at 3-degree spacing are used to investigate the ratio of floats drifted ashore, given that during the first 4 years floats cycle between the surface and 2000m for 10 days and then floats are on just the surface for 100 years. The simulation indicates that about 29% of deployed floats will be drifted ashore within 100-year.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.2
• /
• pp.147-156
• /
• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.

• The Journal of the Acoustical Society of Korea
• /
• v.35 no.1
• /
• pp.1-7
• /
• 2016

This paper proposes an adequate UWA (Underwater Acoustic) communication system of underwater communication network in the Yellow Sea. UWA channel is obtained from Bellhop ray tracing method with Yellow Sea environments. Based on this channel, communication parameters for CDMA (Code Division Multiple Access) and SC-FDM (Single Carrier-Frequency Division Multiplexing) using diversity techniques are calculated. In order to prove the proposed methods, BER (Bit Error Rate) and data rate are obtained using computer simulations and the adequate communication system for long rms delay spread and low Eb/No environments is proposed from the simulation.