• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.2
• /
• pp.314-323
• /
• 2022

Vessel Traffic Services (VTS) operators perform traffic management tasks using VTS systems and sensor equipment designated as VTS facilities to promote the safety and efficiency of vessel traffic. The necessary VTS information for effective operations could be obtained through the additional access of various information channels other than the designated VTS facility. To unify these various information access windows, the development of the VTS cloud system is in progress. In this study, the operational information analysis for VTS was performed through VTS tasks-facility linkage analysis to identify the user required information according to the introduction of the VTS cloud system. The VTS task analysis was performed through research of the international and domestic literature, and expert interviews. The necessary information were identified and linked according to the VTS facilities. As a result of the analysis, 37 categories of necessary information were identified for internal and external information windows, and 8 information windows were selected other than the present VTS equipment. The identified user requirements would be applied to the structure design of the VTS cloud system. In the future, it is necessary to update user requirements through scenario-based user operation analysis and to conduct additional research on the system interface design.

• Journal of the Society of Naval Architects of Korea
• /
• v.58 no.5
• /
• pp.303-313
• /
• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.

• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.5
• /
• pp.330-337
• /
• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.4
• /
• pp.515-524
• /
• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• Journal of the Korean earth science society
• /
• v.43 no.4
• /
• pp.498-510
• /
• 2022

Cloud-aerosol interactions are one of the paramount but least understood forcing factors in climate systems. Generally, an increase in the concentration of aerosols increases the concentration of cloud droplet numbers, implying that clouds tend to persist for longer than usual, suppressing precipitation in the warm boundary layer. The cloud lifetime effect has been the center of discussion in the scientific community, partly because of the lack of cloud life cycle observations and partly because of cloud problems. In this study, the precipitation susceptibility (S_o) matrix was employed to estimate the aerosols' effect on precipitation, while the non-aerosol effect is minimized. The S_o was calculated for the typical coupled, well-mixed maritime stratocumulus decks and giant cloud condensation nucleus (GCCN) seeded clouds. The GCCN-artificially introduced to the marine stratocumulus cloud decks-is shown to initiate precipitation and reduces S_o to approximately zero, demonstrating the cloud lifetime hypothesis. The results suggest that the response of precipitation to changes in GCCN must be considered for accurate prediction of aerosol-cloud-precipitation interaction by model studies

• Journal of Computing Science and Engineering
• /
• v.10 no.1
• /
• pp.21-31
• /
• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• Journal of KIISE
• /
• v.45 no.3
• /
• pp.303-310
• /
• 2018

LoRaWAN(Long Range Wide Area Network) is a standard for low-power, long-range, low-speed communication as announced in the LoRa Alliance. LoRaWAN addresses the physical layer and medium access control layer and the technology used in the physical layer is referred to as LoRa. LoRa can be used for remote monitoring and remote control in maritime conditions. However, unlike land, marine environment is not only difficult to construct an infrastructure for service provision, but also difficult to analyze LoRa performance in maritime. In this study, we construct an infrastructure using cloud platform and analyze LoRa link performance in maritime conditions.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.21 no.2
• /
• pp.164-170
• /
• 2015

This study proposes the improvement of maritime traffic management for domestic ships and fishing vessels by introducing the e-navigation environment. This study discusses the development of present Vessel Traffic service (VTS) in a systematic aspect and a fuctional aspect. The concept and architecture of e-navigation operation system based on the General Information Center on Maritime Safety and Security (GICOMS) are proposed as a solution for the improvement of maritime traffic management in Korean coastal waters. Especially, means of data exchange between ships and ship-shore based on the Maritime Cloud and regional e-Data Center are discussed. This study will help to the implementation of the Korean e-navigation project which focuses on the safety of small ships and fishing vessels. In the future, it is needed to study for the development and operation of accident prevention system under the e-navigation environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.732-735
• /
• 2011

Cloud computing is a new paradigm that adopts a pay-as-you-go business model. So, clients can ues the various resources, although they have not own the resources. Already, three big players of IT industry, namely Amazon, Google and Microsoft, develop the many applications for cloud computing. In this paper, we describe the data consistency requirements for cloud computing. Data characteristics of cloud computing is replicated, distributed and large-scaled. And consistency and availability of data cannot be satisfied simultaneously. In this paper, we categorized the applications of cloud computing, and describe requirements of consistency criteria for applications. With this result, we can make the base of consistency criteria that can be adapted for cloud computing, in the near future.

• Journal of Ocean Engineering and Technology
• /
• v.37 no.6
• /
• pp.247-255
• /
• 2023

Interference and collisions often occur in the loading process at shipyards. Existing simulation methods focus primarily on resource processes and schedules, and there is a lack of real-time reflection in the complex and highly variable loading process. This study aims to develop a spatial environment incorporating real-time product data, such as hulls, and confirms its effectiveness by simulating various construction scenarios. As a method, a near real-time spatial environment based on broadband laser scanning was established, with the situation of loading heavy cargo assumed when converting an existing ship into an LNG dual-fuel propulsion ship. A case study simulation of near-real-time cargo loading processes was then conducted using Unity 3D to confirm the interference and collision risks within the spatial environment. The results indicated that interference occurred in structures previously not identified in the design data, and a collision occurred during the loading object erection phase. The simulation confirmed that the identification of interference and collision risks during the erection phase highlights the need for a relocation or removal process of potential hazards before erection takes place. An improved erection simulation that integrates near real-time data could effectively prevent interference and collision risks.