• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.842-848
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• 2018

The Smart plug is a device that connects between the outlet and the product at home, and it is an IoT type device that can drive energy saving and transmit information to the outside by power on / off control function and power measurement function. In this case, a smart plug that incorporates deep learning of intelligence technology that allows people to learn how to think about a computer, automatically classifies a product as it operates, and automatically tests the operating status of the washing machine by using input AC current pattern. Through this study, even if the product does not function as IoT, it can classify product type and operation state by smart plug connection alone, so we can draw a new paradigm of life pattern and energy saving in one family.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.642-648
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• 2019

Vessels sail according to the COLREG to prevent a collision. However, it is difficult to apply COLREG under special situation as heavy traffic, at this time personal skills of the operator are required. In this case, traffic control is required through the maritime traffic monitoring system. Therefore, maritime traffic management is globally implemented by VTS. In this system, VTS of icer uses the VTS system to assess risks and recommends possible safety operation to vessels with radio systems. This study considers that the risk analysis method with AI (Artificial Intelligence) technology from the operator's aspect. In addition, the research explains the Maritime Traffic Safety Monitoring System, Including AR (Augmented Reality) technology to increase vessel control efficiency. This system is able to predict hazards and risk priorities, and it leads to sequential elimination of dangerous situations. Especially, the hazard situations can be analyzed from operator's perspective of each vessel instead of the VTS officer's aspect, which is more practical than the conventional method. Furthermore, the result of analysis enables to comprehend quantitative hazardous areas and support recommended routes to avoid a collision. As a result, I firmly believe that the system will support to prevent a collision in complex traffic waters. In particular, it could be adopted as a collision prevention system for Maritime Autonomous Surface Ship, which occupies a significant proportion in Maritime 4th industrial revolution.

• Journal of Ocean Engineering and Technology
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• v.37 no.5
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• pp.181-189
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• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.870-874
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• 2018

In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.

• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.109-116
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• 2022

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

• Journal of the Korean Society of Visualization
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• v.21 no.2
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• pp.83-90
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• 2023

The shape of an airfoil is a critical factor in determining aerodynamic characteristics such as lift and drag. Aerodynamic properties of an airfoil have a decisive impact on the performance of various engineering applications, including airplane wings and wind turbine blades. Therefore, it is essential to analyze the aerodynamic characteristics of airfoils. Various analytical tools such as experiments, computational fluid dynamics, and Xfoil are used to perform these analyses, but each tool has its limitation. In this study, airfoil parameterization, image recognition, and artificial intelligence are combined to overcome these limitations. Image and coordinate data are collected from the UIUC airfoil database. Airfoil parameterization is performed by recognizing images from image data to build a database for deep learning. Trained model can predict the aerodynamic characteristics not only of airfoil images but also of sketches. The mean absolute error of untrained data is 0.0091.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.298-299
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• 2022

Digital Transformation in the maritime transportation sector means "by utilizing digital technologies such as artificial intelligence, big data, Internet of Things, block chain, and cloud to create new business models, products, and services for maritime transportation-related companies. It can be defined as a continuous process that adapts to or drives disruptive changes in the market" (Chang, 2021). In a situation where various digital conversion technologies are applied and started to be used in the domestic port logistics field, active acceptance by members can bring about the success of digital conversion. Therefore, in this study, in order to investigate the acceptability of digital transformation in the domestic port logistics sector,

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.388-389
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. However, if software-based method employing GPU is used for AI applications, there is a problem that we can not change the internal circuit of processing unit. In this method, if high-level jobs are required for AI system, we need high-performance GPU, therefore, we have to change GPU or graphic card to perform the jobs. In this work, we developed a CNN-based FPGA (Field Programmable Gate Array) chip to solve this problem.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.70-71
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• 2020

In the era of autonomous ships, artificial intelligence technology will be able to provide a more advanced immersive experience by combining AR and VR. Immersion technology can provide a real-like experience and sensibility by dazzling the five senses of the user. In autonomous operation through VR or AR, it is necessary to provide a natural environment in which seafarers can immerse themselves in navigation and interact with virtual ships. In this study, in preparation for the era of autonomous ships, what are the key characteristics that seafarer can realize immersive user experience during autonomous navigation through VR or AR.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.77-82
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• 2006

Automatic vessel collision-avoidance systems have been studied in the fields of artificial intelligence and navigation for decades. And to facilitate automatic collision-avoidance decision-making in two-vessel-encounter situation, several expert and fuzzy expert systems have been developed. However, none of them can negotiate with each other as seafarers usually do when they intend to make a more economic overall plan of collision avoidance in the COLREGS-COST-HIGH situations where collision avoidance following the International Regulations for Preventing Collisions at Sea(COLREGS) costs too much. Automatic Identification System(AIS) makes data communication between two vessels possible, and negotiation methods can be used to optimize vessel collision avoidance. In this paper, a negotiation framework is put forward to enable vessels to negotiate to optimize collision avoidance in the COLREGS-COST-HIGH situations at open sea. A vessel vector space is defined and therewith a cost model is put forward to evaluate the cost of collision-avoidance actions. Negotiations between a give-way vessel and a stand-on vessel and between two give-way vessels are considered respectively to reach overall low cost agreements. With the framework proposed in this paper, two vessels involved in a COLREGS-COST-HIGH situation can negotiate with each other to get a more economic overall plan of collision avoidance than that suggested by the traditional collision-avoidance expert systems.