• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.357-366
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• 2014

On December of 2003, IMO's $23^{rd}$ Assembly discussed 'Guidelines on places of refuge for ships in need of assistance' At the discussion, Res. A.949(23) has been selected to appoint recommended place of refuge for countries signatory to the IMO Convention. IMO defines "Places of Refuge" as a places where a ship in need of assistance can take action to enable it to stabilize its condition and reduce the hazards to navigation, and to protect human life and the environment. Appointing and managing a Place of refuge can be a delicate problem because of its close connection to each country's coastal and environmental protection policies. However, in case of marine accident, the appointment or management of the place of refuge has a potential to avoid further damage and reduce to the minimum any environmental and estate losses. Currently a number of foreign countries, designated and operated a place of refuge. But, place of refuge selected method criteria were different by country and also does not have any standardized designating place of refuge model. Therefor, this study suggested the model of assigned places of refuge according to objective indication in order to assign reasonable and efficient places of refuge in domestic waters in the future by investigating and analyzing imported facts in considering the assignment of places of refuge in foreign countries and describing these imported data into quantitative value. In designating the model place of refuge, the final place of refuge location was presented by evaluating the probability of marine accidents, analyzing the location, and evaluating the supporting establishment.

• Journal of Korean Society of Steel Construction
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• v.22 no.1
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• pp.13-20
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• 2010

Seismic risk assessment of railway bridges is an important issue for a transportation network, because loss of functionality of railway bridges could result in severe disruption of the railway line, as no redundant routing systems generally exist. Although many studies have been conducted by numerous researchers regarding fragility analyses of bridge structure, little or no studies have been done for fragility analyses of a class of bridge structures considering their geometric variability. This study performs a fragility analysis for Track-on Steel-Plate-Girder (TOSPG) railway bridges in Korea considering their span variability. Seismic fragility curves are developed for a series of bridges with different spans varying from 2 to 15. At last, the fragility curves for the whole TOSPG bridges in Korea are also developed using the total probability theorem. This study is expected to effectively contribute to the seismic risk assessment of railway lines, where a number of bridges are present.

• Spatial Information Research
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• v.10 no.2
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• pp.247-261
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• 2002

There are much damage of people and property because of heavy rain every year. Especially, there are problem to major facility such as dam, bridge, road, tunnel, and industrial complex in the ground stability. So the counter plan for landslide or ground failure must be necessary In the study, the technique of regional landslide susceptibility assessment near the Ulsan petrochemical complex and Kumgang railway bridge was developed and applied using GIS. For the assessment, the geological structures such as bedding and fault were surveyed and the geological structure, topographic, soil, forest, and land use spatial database were constructed using CIS. Using the spatial database, the factors that influence landslide occurrence, such as slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of forest, and land use were calculated or extracted from the spatial database. For application of geological structure, the geological structure line and fault density were calculated. Landslide susceptibility was analyzed using the landslide-occurrence factors by probability method that is summation of landslide occurrence probability values per each factors range or type. The landslide susceptibility map can be used to assess ground stability to protect major facility.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.1
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• pp.59-65
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• 2010

The purpose of this study is to analyze the risk of the forest fire, considering the topography and the forest, for establishing disaster prevention measures of cultural heritage, Uireung, over in Cheonjang-mountain. To do that, we estimate the occurrence and spread of the forest fire over in Cheonjang-mountain through a forest fire probability model(logistic regression), using the space characteristic data($100m{\times}100m$). The factor, occurrence of the forest fire, are diameter class, southeast, southwest, south, coniferous, deciduous, and mixed forest. We assume the probability of the fire forest in each point as follow : [1+exp{-(-4.8081-(0.02453*diameter class)+(0.6608*southeast)+(0.507*southwest)+(0.7943*south)+(0.29498*coniferous forest)+(0.28897*deciduous forest)+(0.17788*mixed forest))}]$^{-1}$. To divide dangerous zone of the big forest fire, we make the basic materials for disaster prevention measures, through the map of coniferous forests, deciduous forests, and mixed forest. The damage of cultural heritage caused by a forest fire will be reduced through the effective preventive measures, by forecast a forest fire to using this study.

• Journal of Wetlands Research
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• v.20 no.2
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• pp.105-115
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• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.233-238
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• 2020

In the cloud environment, IoT devices using sensors and wearable devices are being applied in various environments, and technologies that accurately determine the information generated by IoT devices are being actively studied. However, due to limitations in the IoT environment such as power and security, information generated by IoT devices is very weak, so financial damage and human casualties are increasing. To accurately collect and analyze IoT information, this paper proposes a topographic information-based key management technique that considers IoT information errors. The proposed technique allows IoT layout errors and groups topographic information into groups of dogs in order to secure connectivity of IoT devices in the event of arbitrary deployment of IoT devices in the cloud environment. In particular, each grouped terrain information is assigned random selected keys from the entire key pool, and the key of the terrain information contained in the IoT information and the probability-high key values are secured with the connectivity of the IoT device. In particular, the proposed technique can reduce information errors about IoT devices because the key of IoT terrain information is extracted by seed using probabilistic deep learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.603-611
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• 2020

Potholes on the road directly affect drivers' safety, satisfaction, and vehicle damage. Thus, real-time detection and response are required. Increasing frequency of patrols allows for potholes to be detected and responded to quickly, but this takes much manpower, money, and time. In addition, potholes have different occurrence characteristics depending on the rain conditions, so it is necessary to consider the optimal frequency from an economic and road-service perspective. Therefore, a quantitative analysis was done on the effects of rainfall on the occurrence characteristics of potholes. Information on the persistence, impact of rainfall intensity, and weather information was collected over a long period. Based on the results, a risk-based, optimized, and changeable road-patrol strategy is presented. The analysis results show that the probability of pothole occurrence increases by 2.4 times in rainy weather. Furthermore, the impact continues for 3 days even after the rain stops. The probability of pothole occurrence increases by 0.46% per 1 mm of rainfall, and the occurrence characteristics react sensitively to even a small amount of rain of around 1 mm. It was concluded that road patrol is required at least once every three days for an effect-free period, while twice a day is needed for the "sphere of influence" period to achieve a 95% reliability level.ys for effect-free period, while twice a day for sphere of influence period to satisfy 95% reliability level.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.623-631
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• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• Journal of Navigation and Port Research
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• v.47 no.1
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• pp.1-9
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• 2023

Many studies have analyzed marine accidents, and since marine accident information is updated every year, it is necessary to periodically analyze and identify the causes. The purpose of this study was to prevent accidents by identifying and analyzing the causes of marine accidents using previous and new data. In marine accident data, 1,921 decisions by the Korea Maritime Safety Tribunal on marine accidents on fishing ships over 16 years were collected in consideration of the specificity of fishing ships, and 1,917 cases of accident notification text history by the Ministry of Maritime Affairs and Fisheries over 3 years were collected. The decision data and text data were classified according to variables and quantified. Prior probability was calculated using a Bayesian network using the quantified data, and fishing ship marine accidents were predicted using backward propagation. Among the two collected datasets, the decision data did not provide the types of fishing ships and fishing areas, and because not all fishing ship accidents were included in the decision data, the text data were selected. The probability of a fishing ship marine accident in which engine damage would occur in the West Sea was 0.0000031%, as calculated by backward propagation. The expected effect of this study is that it is possible to analyze marine accidents suitable for the characteristics of actual fishing ships using new accident notification text data to analyze fishing ship marine accidents. In the future, we plan to conduct research on the causal relationship between variables that affect fishing ship marine accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.577-585
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• 2010

Traffic accidents are caused by several factors such as drivers, vehicles, and road environment. It is necessary to investigate and analyze them in advance to prevent similar and repetitive traffic accidents. Especially, the human factor is most significant element and traffic accidents by drinking-driving caused from human factor have become social problem to be paid attention to. The study analyzes traffic accidents resulting from drinking-driving and the effects of driver's attributes and environmental factors on them. The study is composed as two parts. First, the log-linear model is applied to analyze that accidents by drinking or non-drinking driving associate with road geometry, weather condition and personal characteristics. Probability is tested for drinking-driving accidents relative to non-drinking drive accidents. The study analyzes probability differences between genders, between ages, and between kinds of vehicles through odds multipliers. Second, traffic accidents related to drinking are classified into property damage, minor injury, heavy injury, and death according to their severity. Heavy injury is more serious than minor one and death is more serious than heavy injury. The ordinal regression models are established to find effecting factors on traffic accident severity.