• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.334-340
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• 2016

As Information Technology (IT) is developed constantly, big data is becoming important in various industries, including ship and offshore industry where a lot of data are being generated. However, it is difficult to apply big data to ship and offshore industry because there is no generalized platform for its application. Therefore, this study presents a big data platform based on the Hadoop for applications in ship and offshore industry. The Hadoop is one of the most popular big data technologies. The presented platform includes existing data of shipyard and is possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weight of offshore plant topsides. The result shows that the platform can be one of alternatives to use effectively big data in ship and offshore industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.379-384
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• 2013

For effective ship management in VTS(Vessel Traffic Service), it needs to assess the external force acting on ship. Big data in maritime traffic can be roughly categorized into two groups. One is the traffic information including ship's particulars. The other is the external force information e.g., wind, sea wave, tidal current. This paper proposes the method to assess the external force acting on ship using big data in maritime traffic. To approach Big data in maritime traffic, we propose the Waterway External Force Code(WEF code) which consist of wind, wave, tidal and current information, Speed Over the Water(SOW) of each ship, weather information. As a results, the external force acting a navigating ship is estimated.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.6
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• pp.82-90
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• 2019

To reduce emissions of marine pollutants, regulations are being tightened around the world. In the shipbuilding and shipping industries, various countermeasures are being put forward. As there are limits to applying countermeasures to ships already in operation, however, it is necessary for these vessels to use energy efficiently. The sensors installed on ships typically gather a very large amount of data, and thus a big data platform is needed to manage and analyze the data. In this paper, we build a big data analysis platform based on Hadoop and Spark, and we present a method to detect abnormal ship operation using the platform. We also utilize real ship operation data to discuss the data analysis experiment.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.440-454
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• 2020

To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.3
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• pp.243-251
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• 2018

In the shipyard, a lot of data is generated, stored, and managed during design, construction, and operation phases to build ships and offshore structures. However, it is difficult to handle such big data efficiently using existing data-handling technologies. As the big data technology is developed, the ship and offshore industries start to focus on the existing big data to find valuable information from it. In this paper, the material requirement estimation method of offshore structure piping materials using big data analysis is proposed. A big data platform for the data analysis in the shipyard is introduced and it is applied to the analysis of material requirement estimation to solve the problems in piping design by a designer. The regression model is developed from the big data of piping materials and verified using the existing data. This analysis can help a piping designer to estimate the exact amount of material requirement and schedule the purchase time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.171-178
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• 2019

Korea's shipbuilding industry is in a critical condition due to changes in the domestic and international environment. To overcome this crisis, preemptive development of products and technologies through prediction of new demand for ships is necessary. The goal of this research is to develop an artificial intelligence algorithm based on ship big data in order to predict new demand for ships. We intend to develop a big data analytics platform specialized in predicting ship demand and to utilize the forecast results of new ship demand through data analysis for planning/development of new products. By doing so, the development of sustainable new business models for equipment and equipment manufacturers will create new growth engines for shipyard and shipbuilders. Furthermore, it is expected that shipbuilders will be able to create business cases based on measurable performance, plan market-oriented products and services, and continuously achieve innovation that has high market destructive power.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.7
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• pp.851-857
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• 2019

This study is about ship horsepower prediction of machine learning method using the big data of ship. Currently, new ships use the ISO15016 method to predict external environmental resistance through mathematical equations but due to complicated equations and requires many input variables so it is less applicable to be used in ship. In this recent research, we propose a model capable of predicting ship performance with high performance using SVM (Support Vector Machine) algorithm which shows excellent performance in recent prediction and recognition. The proposed predictive model has the advantage of being able to predict better performance than ISO15016 only if secured big data is used. In this study, we compared the ISO15016 technique and the SVM algorithm-based horsepower analysis method using the 178K bulk carrier's voyage data to reduce ship model data preparation, which is a disadvantage of ISO15016, and improve inaccurate horsepower prediction performance.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.244-244
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• 2017

Recently, A study is actively underway to apply to various industries, which are one of the major changes in the key drivers of the industry 4.0.. The data generated by the ship include various indicators such as the fuel volume, engine power, ground speed, speed, speed, main engine rpm, DFOC, SFOC, and FOC. This paper analyzes the sensitivity of the Gathering data and analyzes the impact energy efficiency of the vessel operation by analyzing the influence among each parameter, using the mathematical models, you create an surrogate model using the math model, comparative analysis of actual measurement data and predictive results were analyzed. Through the use of big data analysis technology, it is possible to identify the sensitivity between the energy efficiency related variables of the ship, The possibility of utilization of fuel efficiency indicators using of the surrogate model is identified.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.