• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.267-276
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• 2017

Damages have occurred in various fields such as agriculture, industry, and citizen's health due to the yellow dust. Therefore, it is urgent to take measures against it. In this regard, this study collected data of yellow dust over 11 days on a basis of Feb. 23. 2015 when yellow dust was the greatest after 2009, issue words analysis and recomposed health related tweet data. After testing the significance of yellow dust related diseases by association rule analysis with diseases, it obtained the study results as follows: As a result of significance test for the patients with rhinitis, asthma and conjunctivitis by acquiring the condition data of patients from the Health Insurance Review & Assessment Service, conjunctivitis appeared to be significant in 13 cities for 16 cities at 5% significance probability, while asthma and rhinitis showed a significance in 3 and 6 areas. As described above, it is possible to obtain information about citizens' health from SNS data, such as Tweet data and it is judged that these data will provide useful information for establishing measures of citizens' health care.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.47-63
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• 2018

This study suggested quantitative models to calculate a royalty rate as an important input factor of the relief from royalty method which has the characteristics of income approach method and market approach method that are generally used in the valuation of intangible assets. This study built a royalty rate regression model by referring to the patent infringement damages cases based on royalties, i.e., by using the royalty rates as a dependent variable and the patent indexes of the corresponding patent right as independent variables. Then, a logistic regression model was constructed by referring to inter-partes review cases of patent rights, i.e. by using not-unpatentable results as a dependent variable and the patent indexes of the corresponding patent right as independent variables. A final royalty rate was calculated by matching the royalty rate from the royalty rate regression model with a not-unpatentable probability from the logistic regression model. The suggested royalty rate was compared with the royalty rate obtained by the traditional methods to check its reliability.

• Journal of the Korea Concrete Institute
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• v.22 no.4
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• pp.535-546
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• 2010

Recently, the probability of collision accident between vehicles or vessels and infrastructures are increasing at alarming rate. Particularly, collision impact load can be detrimental to sub-structures such as piers and columns. The damaged pier from an impact load of a vehicle or a vessel can lead to member damages, which make the member more vulnerable to impact load due to other accidents which. In extreme case, may cause structural collapse. Therefore, in this study, the vehicle impact load on concrete compression member was considered to assess the quantitative design resistance capacity to improve, the existing design method and to setup the new damage assessment method. The case study was carried out using the LS-DYNA, an explicit finite element analysis program. The parameters for the case study were cross-section variation of pier, impact load angle, permanent axial load and axial load ratio, concrete strength, longitudinal and lateral rebar ratios, and slenderness ratio. Using the analysis results, the performance based resistance capacity evaluation method for impact load using satisfaction curve was developed using Bayesian probabilistic method, which can be applied to reinforced concrete column design for impact loads.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.481-489
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• 2004

Ship collisions and grounding continue to occur regardless of continuous efforts to prevent such accidents. With the increasing demand for safety at sea and for protection of the environment, it is of crucial importance to be able to reduce the probability of accidents, assess their consequences and ultimately minimize or prevent potential damages to the ships and the marine environment. Numerical simulations for actual collision problem are conducted with a special attention with respect to finite element size, fracture criteria and material properties, which require a careful consideration to improve the accuracy. A parametric analysis varying colliding speed, angle, design loading condition is conducted using nonlinear finite element analysis method for 46,00 dwt Product/chemical carrier. The relationship between the absorbed energy and indentation are derived quantitatively using the insights observed from this study, and a novel design concept for assessing the anti-collision performance are proposed.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.661-670
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• 2020

The water supply system has a wider installation range and various components of it than other infrastructure, making it difficult to secure stability against earthquakes. Therefore, it is necessary to develop methods for evaluating the seismic performance of water supply systems. Ground Motion Prediction Equation (GMPE) is used to evaluate the seismic performance (e.g, failure probability) for water supply facilities such as pump, water tank, and pipes. GMPE is calculated considering the independent variables such as the magnitude of the earthquake and the ground motion such as PGV (Peak Ground Velocity) and PGA (Peak Ground Acceleration). Since the large magnitude earthquake data has not accumulated much to date in Korea, this study tried to select a suitable GMPE for the domestic earthquake simulation by using the earthquake data measured in Korea. To this end, GMPE formula is calculated based on the existing domestic earthquake and presented the results. In the future, it is expected that the evaluation will be more appropriate if the determined GMPE is used when evaluating the seismic performance of domestic waterworks. Appropriate GMPE can be directly used to evaluate hydraulic seismic performance of water supply networks. In other words, it is possible to quantify the damage rate of a pipeline during an earthquake through linkage with the pipe failure probability model, and it is possible to derive more reasonable results when estimating the water outage or low-pressure area due to pipe damages. Finally, the quantifying result of the seismic performance can be used as a design criteria for preparing an optimal restoration plan and proactive seismic design of pipe networks to minimize the damage in the event of an earthquake.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s02
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• pp.13-33
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• 1989

Agroclimate should be analyzed and evaluated accurately to make better use of available chimatic resources for the establishment of optimum cropping systems. Introducing of appropriate cultivars and their cultivation techniques into classified agroclimatic zone could contribute to the stability and costs of crop production. To classify the agroclimatic zones, such climatic factors as temperature, precipitation, sunshine, humidity and wind were considered as major influencing factors on the crop growth and yield. For the classification of rice agroclimatic zones, precipitation and drought index during transplanting time, the first occurrence of effective growth temperature (above 15$^{\circ}C$) and its duration, the probability of low temperature occurrence, variation in temperature and sunshine hours, and climatic productivity index were used in the analysis. The agroclimatic zones for rice crop were classified into 19 zones as follows; (1) Taebaek Alpine Zone, (2) Taebaek Semi-Alpine Zone, (3) Sobaek Mountainous Zone, (4) Noryeong Sobaek Mountainous Zone, (5) Yeongnam Inland Mountainous Zone, (6) Northern Central Inland Zone, (7) Central Inland Zone, (8) Western Soebaek Inland Zone, (9) Noryeong Eastern and Western Inland Zone, (10) Honam Inland Zone, (ll) Yeongnam Basin Zone, (12) Yeongnam Inland Zone, (13) Western Central Plain Zone, (14) Southern Charyeong Plain Zone, (15) South Western Coastal Zone, (16) Southern Coastal Zone, (17) Northern Eastern Coastal Zone, (18) Central Eastern Coastal Zone, and (19) South Eastern Coastal Zone. The classification of agroclimatic zones for cropping systems was based on the rice agroclimatic zones considering zonal climatic factors for both summer and winter crops and traditional cropping systems. The agroclimatic zones were identified for cropping systems as follows: (I) Alpine Zone, (II) Mountainous Zone, (III) Central Northern Inland Zone, (IV) Central Northern West Coastal Zone, (V) Cental Southern West Coastal Zone, (VI) Gyeongbuk Inland Zone, (VII) Southern Inland Zone, (VIII) Southern Coastal Zone, and (IX) Eastern Coastal Zone. The agroclimatic zonal characteristics of climatic disasters under rice cultivation were identified: as frequent drought zones of (11) Yeongnam Basin Zone, (17) North Eastern Coastal Zone with the frequency of low temperature occurrence below 13$^{\circ}C$ at root setting stage above 9.1％, and (2) Taebaek Semi-Alpine Zone with cold injury during reproductive stages, as the thphoon and intensive precipitation zones of (10) Hanam Inland Zone, (15) Southern West Coastal Zone, (16) Southern Coastal Zone with more than 4 times of damage in a year and with typhoon path and heavy precipitation intensity concerned. Especially the three east coastal zones, (17), (18), and (19), were subjected to wind and flood damages 2 to 3 times a year as well as subjected to drought and cold temperature injury.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.