• Journal of Korean Port Research
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• v.11 no.2
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• pp.231-240
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• 1997

In the United States, a number of ocean outfalls discharge primary treated effluent into deep sea water and contribute for more efficient wastewater treatment. The long multiport diffuser connected by long pipe from a treatment plant discharge wastewater into deep water due to the steep slope of the sea bed. However, Plume discharged from the diffuser can have significant impacts on coastal communities and possibly immediate consequence on public health. Therefore, there have been growing interests about the dynamics of plume in the vicinity of the ocean outfalls. It is expected that the ocean outfall should be considered for more efficient and reliable wastewater treatments as soon as possible around coastal area in South Korea. A number of studies of plume ynamics have used various models to predict plume behavior. However, in many cases, the calculated values of plume behavior are in significantly poor agreement with realistic values. Therefore, in this study, it is recommended that improvements should be made in the application of the plume model to more simulate the actual discharge characteristics and ocean conditions. It should be noted that input parameters in plume models reflect realistic ocean conditions like waves as well as currents. In this study, as one of the new parameters, current and long wave-influenced plume rise and initial dilution have been taken into account by using simple linear wave theory under some specific assumptions for more reliable plume behavior description. Among the improved plume models approved by EPA (Environmental Protection Agency), the RSB(Roberts-Snyder-Baurngartner) and UM(Updated Merge) models were chosen for the calculation of plume behavior, and the variation calculated by both models on the basis of long period wave was compared in terms of plume rise and initial dilution.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.843-856
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• 2023

The global surge in maritime traffic has resulted in an increased number of ship collisions, leading to significant economic, environmental, physical, and human damage. The causes of these maritime accidents are multifaceted, often arising from a combination of crew judgment errors, negligence, complexity of navigation routes, weather conditions, and technical deficiencies in the vessels. Given the intricate nuances and contextual information inherent in each incident, a methodology capable of deeply understanding the semantics and context of sentences is imperative. Accordingly, this study utilized the SentenceBERT model to analyze maritime safety tribunal decisions over the last 20 years in the Busan Sea area, which encapsulated data on ship collision incidents. The analysis revealed important keywords potentially responsible for these incidents. Cluster analysis based on the frequency of specific keyword appearances was conducted and visualized. This information can serve as foundational data for the preemptive identification of accident causes and the development of strategies for collision prevention and response.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.3
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• pp.223-233
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• 2015

In order to evaluate the pollution of heavy metals in offshore surface sediments around shipyards in Korea, surface sediment samples were collected at eleven stations around four major shipyards located in the southeastern coast of Korea in summer 2010 and nine kinds of heavy metals such as copper(Cu), zinc(Zn), cadmium(Cd), lead(Pb), chrome(Cr), arsenic(As), mercury(Hg), iron(Fe) and aluminum(Al) in sediments were analyzed. The concentrations of Cu at all sampling stations were in the range of 47.10~414.96 mg/kg and exceeded TEL(Threshold Effects Level) 20.6 mg-Cu/kg of Korean marine environmental standards for offshore sediments and ERL(Effect Range-Low) 34.0 mg-Cu/kg. The concentrations of Cu at seven stations around four shipyards were 65.18~414.96 mg/kg and exceeded PEL(Probable Effects Level) 64.4 mg-Cu/kg of Korean marine environmental standards for offshore sediments. The concentration of Cu at one station around B-shipyard was 414.96 mg/kg and exceeded ERM(Effect Range-Median) 270.0 mg-Cu/kg. The concentrations of Zn at all stations were in the range of 135.09~388.79 mg/kg which exceeded ERL 150.0 mg-Zn/kg. The concentrations of Zn at seven stations around four shipyards were 157.57~388.79 mg/kg and exceeded PEL 157.0 mg-Zn/kg. The concentration of Zn at one station around B-shipyard was 388.79 mg/kg and was approaching ERM 410.0 mg-Zn/kg. The concentrations of Cd at all stations were in the range of 0.11~0.54 mg/kg and were below TEL 0.75 mg-Cd/kg and ERL 1.2 mg-Cd/kg. The concentrations of Pb at all stations were in the range of 18.04~105.62 mg/kg. The concentrations of Pb at two stations around B-shipyard were 73.87~105.62 mg/kg which exceeded TEL 44.0 mg-Pb/kg and ERL 46.7 mg-Pb/kg, and were below PEL 119.0 mg-Pb/kg and ERM 218.0 mg-Pb/kg. The concentrations of Cr at all stations were in the range of 51.26~85.39 mg/kg. The concentration of Cr at one station around B-shipyard was 85.39 mg/kg and exceeded ERL 81.0 mg-Cr/kg. The concentrations of As at all stations were in the range of 8.70~22.15 mg/kg which exceeded ERL 8.2 mg-As/kg and were below ERM 70.0 mg-As/kg. The concentrations of As at eight stations around A-shipyard, B-shipyard and D-shipyard were 14.93~22.15 mg/kg which exceeded TEL 14.5 mg-As/kg and were below PEL 75.5 mg-As/kg. The concentrations of Hg at all stations were in the range of 0.02~0.35 mg/kg. The concentrations of Hg at three stations around A-shipyard were 0.11~0.13 mg/kg which were almost equal to TEL 0.11 mg-Hg/kg. Those at two stations around B-shipyard were 0.27~0.35 mg/kg which exceeded TEL 0.11 mg-Hg/kg and ERL 0.15 mg-Hg/kg, and were below PEL 0.62 mg-Hg/kg and ERM 0.71 mg-Hg/kg. The concentrations of Fe and Al at all stations were in the range of 2.90 3.66 % and 3.12 6.80 %, respectively. These results imply that heavy metals such as copper, zinc, lead, arsenic and mercury were likely to be transferred to marine environment from shipyards, especially from B-shipyard.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.