• Title/Summary/Keyword: 해상 데이터 관리

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A Study on the Axial Dependence of the Traffic Distribution Function (통항분포함수 축방향 의존성에 관한 연구)

  • Yoo, Sang-Lok;Gang, Sang-Geun
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.21 no.2
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    • pp.179-187
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    • 2015
  • The purpose of this study is to identify the aspect that the traffic distribution function changes, according to the direction of the datum line and the horizontal and vertical positions of the datum point applied when it is calculated. Targeting routes at the entrance of Mokpo Harbor, this study tested using AIS survey data of January 2013 the effects of the three variables-direction of the datum line(${\theta}$), horizontal position($\mathfrak{L}_H$) and vertical position($\mathfrak{L}_V$) on mean ($\bar{x}$) and standard deviation (${\delta}$). The test result showed that $\bar{x}$ and ${\delta}$ were changed according to the change of ${\theta}$, because the extracted sample data were changed according to ${\theta}$; and the changes of $\bar{x}$ and ${\delta}$ according to ${\theta}$ were drawn as the relation of the sine function' sum. In addition, it was found that setting up ${\theta}$ that the change value of ${\delta}$ becomes the least as the direction of the datum line was valid, to determine the optimum passage distribution function on complex waters with multiple branches of route. The result of this study is expected to be used as basic data to understand maritime traffic flow based on more quantified data of normal distribution and make decisions related to maritime traffic safety management.

Estimation Model for Freight of Container Ships using Deep Learning Method (딥러닝 기법을 활용한 컨테이너선 운임 예측 모델)

  • Kim, Donggyun;Choi, Jung-Suk
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.5
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    • pp.574-583
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    • 2021
  • Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

Development of the PC Based Color Fish Finder (퍼스널 컴퓨터를 이용한 칼라 어군탐지기의 개발에 관한 연구)

  • 신현옥
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.31 no.3
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    • pp.247-255
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    • 1995
  • This paper describes a personal computer(PC) based color fish finder to improve some problem of the commercial one. The commercial fish finder has no function of the echo data logging and replaying. The authors developed two types of the PC based color fish finder. One is a master type composed of a PC, a digital input-output board, and analog to digital converting (A/D) board and an ultrasonic transceiver unit, the other is a slave type composed of a PC and an A/D board. To test the performances of the master type experiments were carried out in air and in a water tank. It is found that the designed master type fish finder displays very well an eight-colored echogram by one dot resolution to the left side of the PC monitor. Also, the depth of echo signal was corresponds very well to the range from the transducer to a target. The sampling interval of echo signal is about 0.1m and the time of A/D conversion is 30 $\mu$sec. On the other hand, to test the performances of the slave type a raw data of echo signals from a data logger was supplied directly or via RF transceivers to the slave type one. From this experiment, it is confirmed the slave type is useful to replay the echo signal from the data logger or a telesounder.

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Determinants on Transshipments in the Busan Port (부산항의 환적량 결정요인 분석)

  • Kim, Jeong-Su
    • Journal of Korea Port Economic Association
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    • v.27 no.2
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    • pp.183-194
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    • 2011
  • Countries in the world make a strategic effort to develop their own ports into hub ports and lure transshipment cargoes. Likewise, the Busan Port tries to become a container hub port in Northeast Asia, but there is lately a gradual decline in the number of transshipment cargoes. The purpose of this study was to examine the influential factors of port transshipment traffic in an effort to identify the determinants of transshipments in the Busan Port. In existing studies, harbor infrastructure, maritime transshipment cost, port cost and port service were primarily presented as the determinants of transshipment traffic after surveys were conducted by experts. In this study, the transshipment traffic in the Busan Port was selected as a dependent variable, and the container traffic and transshipment traffic of ports in adjacent countries and each country's amount of trade and economic growth rate were selected as explanatory variables to analyze what factors determined the transshipment traffic in the port.

Challenges of Genome Wide Sequencing Technologies in Prenatal Medicine (산전 진단에서의 염기 서열 분석 방법의 의의)

  • Kang, Ji-Un
    • The Journal of the Korea Contents Association
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    • v.22 no.2
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    • pp.762-769
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    • 2022
  • Genetic testing in prenatal diagnosis is a precious tool providing valuable information in clinical management and parental decision-making. For the last year, cytogenetic testing methods, such as G-banding karyotype analysis, fluorescent in situ hybridization, chromosomal microarray, and gene panels have evolved to become part of routine laboratory testing. However, the limitations of each of these methods demonstrate the need for a revolutionary technology that can alleviate the need for multiple technologies. The recent introduction of new genomic technologies based on next-generation sequencing has changed the current practice of prenatal testing. The promise of these innovations lies in the fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy for prenatal diagnosis. Here, we review the current state of sequencing-based pediatric diagnostics, associated challenges, as well as future prospects.

Predicting the Baltic Dry Bulk Freight Index Using an Ensemble Neural Network Model (통합적인 인공 신경망 모델을 이용한 발틱운임지수 예측)

  • SU MIAO
    • Korea Trade Review
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    • v.48 no.2
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    • pp.27-43
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    • 2023
  • The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

National Management Measures for Reducing Air Pollutant Emissions from Vessels Focusing on KCG Services (선박 대기오염물질 배출 현황 및 저감을 위한 국가 관리 대책 연구: 해양경찰 업무를 중심으로)

  • Lee, Seung-Hwan;Kang, Byoung-Yong;Jeong, Bong-Hun;Gu, Ja-Yeong
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.26 no.2
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    • pp.163-174
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    • 2020
  • Particulate matter levels are rapidly increasing daily, and this can affect human health. Therefore, air pollutant emissions from sea vessels require management. This study evaluates the status of air pollutants, focusing on air pollutant emissions from the vessels of the Korea Coast Guard (KCG), and proposes national management measures to reduce emissions. According to a report recently released (2018) by the National Institute of Environmental Research (NIER), emissions from vessels constituted 6.4 % of the total domestic emissions, including 13.1 % NOx, 10.9 % SOx, and 9.6 % particulate matter (PM10/PM2.5). Among the rates of pollutant emission from vessels, the emission rates of domestic and overseas cargo vessels were the highest (50.6 %); the ratio of fishing boats was 42.6 %. With respect to jurisdictional sea area, 44.1 % of the emissions are from the south sea, including the Busan and Ulsan ports, and 24.8 % of the emissions are from the west sea, including the Gwangyang and Yeosu ports. The KCG inspects boarding lines to manage emission conditions and regulate air pollutant emissions, but it takes time and effort to operate various discharge devices and measure fuel oil standards. In addition, owing to busy ship schedules, inspection documents are limited in terms of management. Therefore, to reduce the air pollutant emissions of such vessels, regulations will be strengthened to check for air pollutants, and a monitoring system based on actual field data using KCG patrol ships will be established, for each sea area, to manage the emissions of such vessels. Furthermore, there is a need for technological development and institutional support for the introduction of environmentally friendly vessels.

3D Measurement Method Based on Point Cloud and Solid Model for Urban SingleTrees (Point cloud와 solid model을 기반으로 한 단일수목 입체적 정량화기법 연구)

  • Park, Haekyung
    • Korean Journal of Remote Sensing
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    • v.33 no.6_2
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    • pp.1139-1149
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    • 2017
  • Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

Remote Sensing을 이용한 태화강 하구 수심정보 획득 - Landsat 7 ETM 다중분광영상을 사용

  • Oh, Chang-Seok;Cho, Hong-Je;Song, Yeong-Min
    • Proceedings of the Korea Water Resources Association Conference
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    • 2006.05a
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    • pp.1530-1534
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    • 2006
  • 원격탐사 기법을 이용한 수심측정은 하나 혹은 그 이상의 파장대에서 수심과 반사되는 에너지 사이의 관계를 찾아내는데 달려 있다. 수심 정보를 획득하기 위한 스펙트럼의 최적 파장길이는 다중분광영상(Landsat 7 ETM)의 blue band에 해당하는 약 $0.48{\mu}m$이며, 이 band를 이용하여 연안의 수심을 측량하기도 한다. 하지만 단일밴드에 의해서 측정된 값을 이용한 수심측정은 해저표면에 의한 반사에 심각한 영향을 받을 수 있기 때문에 신뢰할 만한 결과를 얻을 수 없다. 따라서 본 연구에서는 해수와 관련한 여러 가지 변수들을 결정하기 위하여 다량의 실측 데이터를 필요로 하지 않는 선형다중밴드방식을 이용하여 2개의 Landsat 영상으로 태화강 하구의 수심정보를 추출하고 태화강 본류에 대한 수심정보획득과 하상변동에 대한 분석 가능성을 파악하였다. 그 결과 임의로 선정한 표본 50개 지점에 대한 영상분석에 의한 수심값과 해도의 수심값의 잔차 평균이 각각 2.29m, 2.43m로 비교적 큰 잔차를 보였다. 하지만 20m 미만의 수심대의 표본만을 확인한 결과 각각 1.73m, 1.88m로 잔차 평균이 크게 감소하였다. 2000년, 2003년 영상을 비교한 결과, 1번 2번 3번 지역에서 평균적으로 약 1.838m정도 2003년 수심이 감소한 것으로 나타났다. 본 연구에서 20m 미만의 수심 측량은 낮은 해상도의 위성영상이라도 실제 수심과 근접하고 있는 것으로 판단 할 수 있었다. 이것으로 넓은 지역을 경제적으로 수심자료를 획득할 수 있는 위성영상분석을 이용한 수심측정은 활용성이 있는 것으로 나타났다. 하지만 해저표면의 형태와 해수면의 상태 등 수심측정에 미치는 영향에 관한 실측데이터에 대한 자료수집과 분석이 선행된다면 더욱 좋은 결과를 도출할 수 있을 것으로 판단된다.A}$는 최대암모니아 섭취률을 이용하여 구한 결과 $0.65d^{-1}$로 나타났다.EX>$60%{\sim}87%$가 수심 10m 이내에 분포하였고, 녹조강과 남조강이 우점하는 하절기에는 5m 이내에 주로 분포하였다. 취수탑 지점의 수심이 연중 $25{\sim}35m$를 유지하는 H호의 경우 간헐식 폭기장치를 가동하는 기간은 물론 그 외 기간에도 취수구의 심도를 표층 10m 이하로 유지 할 경우 전체 조류 유입량을 60% 이상 저감할 수 있을 것으로 조사되었다.심볼 및 색채 디자인 등의 작업이 수반되어야 하며, 이들을 고려한 인터넷용 GIS기본도를 신규 제작한다. 상습침수지구와 관련된 각종 GIS데이타와 각 기관이 보유하고 있는 공공정보 가운데 공간정보와 연계되어야 하는 자료를 인터넷 GIS를 이용하여 효율적으로 관리하기 위해서는 단계별 구축전략이 필요하다. 따라서 본 논문에서는 인터넷 GIS를 이용하여 상습침수구역관련 정보를 검색, 처리 및 분석할 수 있는 상습침수 구역 종합정보화 시스템을 구축토록 하였다.N, 항목에서 보 상류가 높게 나타났으나, 철거되지 않은 검전보나 안양대교보에 비해 그 차이가 크지 않은 것으로 나타났다.의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수

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Real-time Reefer Container Control Device Using M2M Communication (M2M통신을 이용한 실시간 냉동컨테이너 제어 장비)

  • Moon, Young-Sik;Choi, Sung-Pill;Lee, Eun-Kyu;Kim, Tae-Hoon;Lee, Byung-Ha;Kim, Jae-Joong;Choi, Hyung-Rim
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.9
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    • pp.2216-2222
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    • 2014
  • A recent trend of increasing container traffic volume using reefer container demands continuous management of reefer container in transit. However, reefer containers can only be monitored at terminal or in ship during marine transportation instead of throughout entire section. In the case of inland transportation section using truck or train, monitoring is not possible currently. The reason is because the reefer container monitoring method using PCT recommended by IMO and conventional monitoring methods using TCP/IP, RFID communication require establishing additional communication infrastructure. This paper will propose a new reefer container control device that not only solves these problems and monitors during inland transportation section but also controls reefer container. Using data port attached to every reefer container, the proposed device collects the information of reefer container and using M2M communication technology, it transmits information to server without the need to establish additional communication infrastructure. In addition, it can control the operational status of reefer container upon receiving control information set in server such as temperature of reefer container.