• Title/Summary/Keyword: 해양데이터모델

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ASM 메시지를 이용한 스마트 항로표지 정보서비스 제공 방안

  • 김건웅
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.06a
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    • pp.56-58
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    • 2022
  • '스마트 항로표지 현장시설 고도화 연구'를 통해 항로표지의 상태뿐만 아니라, 다양한 센서를 통해 해상정보를 수집하고, 이를 선박과 육상 연계시스템에 전파하는 방안이 논의되고 있다. 본 논문에서는 스마트 항로표지에서 신규 정보서비스를 제공하기 위해 AIS 서비스와 데이터 모델, AIS AtoN 기지국의 필수 기능, ASM 도입 절차 등을 검토하고, 스마트 항로표지 통합 플랫폼에 장착할 센서들의 정보를 선박에 제공하는 방안을 제시한다. 이미 선정된 센서들의 정보의 경우 국제적 ASM 31번 메시지로 정보 제공이 가능하며, 기존에 논의되지 않은 신규 센서의 활용이 결정되면, 지역 ASM 메시지를 새로 정의하여 정보를 제공할 수 있다.

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A Study on the Effective VTS Communications Analysis by the Method of VCDF in Busan Port (VCDF 방식을 통한 효율적인 VTS 통신 데이터 분석에 관한 연구 - 부산항을 대상으로 -)

  • Kim, Bong-Hyun;Park, Young-Soo
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.22 no.4
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    • pp.311-318
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    • 2016
  • The VTS concept was located as a principal methods of maritime safety administration in world's major harbors and expected to become the pivotal role for the future of the maritime and harbor society with e-Navigation epoch. If recent limelight concept of big-data has been included in aspect of information gathering and analysis with various studies, it's required advanced studies to improve the information analysis capability and application range of the data that can be mining by the VTS. In this study, contrast to other studies that aimed quantitative analysis as communication number, it can be mining the time information and each of the communication VTS for the target vessel, including qualitative analysis, such as the purpose or the type of communication. This comparison across multiple items of the collected information, and presenting the VTS data mining model (VCDF) that can be analyzed for the purpose of analyzing way, type and number of communication by ship's type, also number of violations through VTS communication. First, In Busan port case, it shows frequently information service and shows frequently communicating with particular types of vessels. Second, Passive VTS carried out notwithstanding many kinds of traffic violations due to communication congestion. This arranged information can be used as data for the analysis, as possible the level of traffic for VTSO situational awareness, which pointed to the 'workloads' in 'IALA Guideline' and could be used as a database for future research of e-Navigation.

Design and Implementation of a Framework for Collaboration Systems in the Shipbuilding and Marine Industry (조선해양 설계분야에서 협업시스템을 위한 프레임워크의 설계 및 구현)

  • Yun, Moon-Kyeong;Kim, Hyun-Ju;Park, Min-Gil;Han, Myeong-Ki;Kim, Wan-Kyoo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.05a
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    • pp.270-273
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    • 2015
  • In shipbuilding and marine industry, engineering and design software solutions have upgraded from the original 2D schematic data based CAD system to a modern 3D drawing-based system. Due to the fact that the massive amount of data usage in real time and data volumes of various engineering models including graphic data have increased, several problems such as lack of server resources and improper handling of 3D drawings have been raised. Besides, increasing the number of session connections per server can cause deterioration of server performance. Recently, increasing the yard's sophisticated design capabilities highlighted the need to develop engineering and design system which would not only overcome the network performance issues, but would provide efficient collaborative design environment. This paper presents an overview of the framework for collaborative engineering design system based on the virtual application (Citrix XenApp 6.5)and acceleration hardware technology of 3D graphics (NVIDIA GRID K2 solution).

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A Study on Automatic Classification of Class Diagram Images (클래스 다이어그램 이미지의 자동 분류에 관한 연구)

  • Kim, Dong Kwan
    • Journal of the Korea Convergence Society
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    • v.13 no.3
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    • pp.1-9
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    • 2022
  • UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

Misconception on the Yellow Sea Warm Current in Secondary-School Textbooks and Development of Teaching Materials for Ocean Current Data Visualization (중등학교 교과서 황해난류 오개념 분석 및 해류 데이터 시각화 수업자료 개발)

  • Su-Ran Kim;Kyung-Ae Park;Do-Seong Byun;Kwang-Young Jeong;Byoung-Ju Choi
    • Journal of the Korean earth science society
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    • v.44 no.1
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    • pp.13-35
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    • 2023
  • Ocean currents play the most important role in causing and controlling global climate change. The water depth of the Yellow Sea is very shallow compared to the East Sea, and the circulation and currents of seawater are quite complicated owing to the influence of various wind fields, ocean currents, and river discharge with low-salinity seawater. The Yellow Sea Warm Current (YSWC) is one of the most representative currents of the Yellow Sea in winter and is closely related to the weather of the southwest coast of the Korean Peninsula, so it needs to be treated as important in secondary-school textbooks. Based on the 2015 revised national educational curriculum, secondary-school science and earth science textbooks were analyzed for content related to the YSWC. In addition, a questionnaire survey of secondary-school science teachers was conducted to investigate their perceptions of the temporal variability of ocean currents. Most teachers appeared to have the incorrect knowledge that the YSWC moves north all year round to the west coast of the Korean Peninsula and is strong in the summer like a general warm current. The YSWC does not have strong seasonal variability in current strength, unlike the North Korean Cold Current (NKCC), but does not exist all year round and appears only in winter. These errors in teachers' subject knowledge had a background similar to why they had a misconception that the NKCC was strong in winter. Therefore, errors in textbook contents on the YSWC were analyzed and presented. In addition, to develop students' and teachers' data literacy, class materials on the YSWC that can be used in inquiry activities were developed. A graphical user interface (GUI) program that can visualize the sea surface temperature of the Yellow Sea was introduced, and a program displaying the spatial distribution of water temperature and salinity was developed using World Ocean Atlas (WOA) 2018 oceanic in-situ measurements of water temperature and salinity data and ocean numerical model reanalysis field data. This data visualization materials using oceanic data is expected to improve teachers' misunderstandings and serve as an opportunity to cultivate both students and teachers' ocean and data literacy.

A Study on the Detection of Fallen Workers in Shipyard Using Deep Learning (딥러닝을 이용한 조선소에서 쓰러진 작업자의 검출에 관한 연구)

  • Park, Kyung-Min;Kim, Seon-Deok;Bae, Cherl-O
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.26 no.6
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    • pp.601-605
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    • 2020
  • In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.

Optimal Camera Placement Leaning of Multiple Cameras for 3D Environment Reconstruction (3차원 환경 복원을 위한 다수 카메라 최적 배치 학습 기법)

  • Kim, Ju-hwan;Jo, Dongsik
    • Smart Media Journal
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    • v.11 no.9
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    • pp.75-80
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    • 2022
  • Recently, research and development on immersive virtual reality(VR) technology to provide a realistic experience is being widely conducted. To provide realistic experience in immersive virtual reality for VR participants, virtual environments should consist of high-realistic environments using 3D reconstruction. In this paper, to acquire 3D information in real space using multiple cameras in the reconstruction process, we propose a novel method of optimal camera placement for accurate reconstruction to minimize distortion of 3D information. Through our approach in this paper, real 3D information can obtain with minimized errors during environment reconstruction, and it is possible to provide a more immersive experience with the created virtual environment.

GOCI-IIVisible Radiometric Calibration Using Solar Radiance Observations and Sensor Stability Analysis (GOCI-II 태양광 보정시스템을 활용한 가시 채널 복사 보정 개선 및 센서 안정성 분석)

  • Minsang Kim;Myung-Sook Park;Jae-Hyun Ahn;Gm-Sil Kang
    • Korean Journal of Remote Sensing
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    • v.39 no.6_2
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    • pp.1541-1551
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    • 2023
  • Radiometric calibration is a fundamental step in ocean color remote sensing since the step to derive solar radiance spectrum in visible to near-infrared wavelengths from the sensor-observed electromagnetic signals. Generally, satellite sensor suffers from degradation over the mission period, which results in biases/uncertainties in radiometric calibration and the final ocean products such as water-leaving radiance, chlorophyll-a concentration, and colored dissolved organic matter. Therefore, the importance of radiometric calibration for the continuity of ocean color satellites has been emphasized internationally. This study introduces an approach to improve the radiometric calibration algorithm for the visible bands of the Geostationary Ocean Color Imager-II (GOCI-II) satellite with a focus on stability. Solar Diffuser (SD) measurements were employed as an on-orbit radiometric calibration reference, to obtain the continuous monitoring of absolute gain values. Time series analysis of GOCI-II absolute gains revealed seasonal variations depending on the azimuth angle, as well as long-term trends by possible sensor degradation effects. To resolve the complexities in gain variability, an azimuth angle correction model was developed to eliminate seasonal periodicity, and a sensor degradation correction model was applied to estimate nonlinear trends in the absolute gain parameters. The results demonstrate the effects of the azimuth angle correction and sensor degradation correction model on the spectrum of Top of Atmosphere (TOA) radiance, confirming the capability for improving the long-term stability of GOCI-II data.

Study on Cochlodinium polykrikoides Red tide Prediction using Deep Neural Network under Imbalanced Data (심층신경망을 활용한 Cochlodinium polykrikoides 적조 발생 예측 연구)

  • Bak, Su-Ho;Jeong, Min-Ji;Hwang, Do-Hyun;Enkhjargal, Unuzaya;Kim, Na-Kyeong;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.6
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    • pp.1161-1170
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    • 2019
  • In this study, we propose a model for predicting Cochlodinium polykrikoides red tide occurrence using deep neural networks. A deep neural network with eight hidden layers was constructed to predict red tide occurrence. The 59 marine and meteorological factors were extracted and used for neural network model training using satellite reanalysis data and meteorological model data. The red tide occurred in the entire dataset is very small compared to the case of no red tide, resulting in an unbalanced data problem. In this study, we applied over sampling with adding noise based data augmentation to solve this problem. As a result of evaluating the accuracy of the model using test data, the accuracy was about 97%.

Convergence of Artificial Intelligence Techniques and Domain Specific Knowledge for Generating Super-Resolution Meteorological Data (기상 자료 초해상화를 위한 인공지능 기술과 기상 전문 지식의 융합)

  • Ha, Ji-Hun;Park, Kun-Woo;Im, Hyo-Hyuk;Cho, Dong-Hee;Kim, Yong-Hyuk
    • Journal of the Korea Convergence Society
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    • v.12 no.10
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    • pp.63-70
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    • 2021
  • Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.