• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.92-101
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• 2020

To prevent and mitigate damage to farms due to heavy snowfall, snow weight information should be provided in addition to snow depth. This study reviews four formulae regarding snow density and weight used in extant studies and applies them in Suwon area to estimate snow weight in Korea. We investigated the observed snow depth of 94 meteorological stations and automatic weather stations (AWS) data over the past 30 years (1988-2017). Based on the spatial distribution of snow depth by area in Korea, much of the fresh snow cover, due to heavy snowfall, occurred in Jeollabuk-do and Gangwon-do. Record snowfalls occurred in Gyeongsangbuk-do and Gangwon-do. However, the most recent heavy snowfall in winter occurred in Gyeonggi-do, Gyeongsangbuk-do, and Jeollanam-do. This implies that even if the snow depth is high, there is no significant damage unless the snow weight is high. The estimation of snow weight in Suwon area yielded different results based on the calculation method of snow density. In general, high snow depth is associated with heavy snow weight. However, maximum snow weight and maximum snow depth do not necessarily occur on the same day. The result of this study can be utilized to estimate the snow weight at other locations in Korea and to carry out snow weight prediction based on a numerical model. Snow weight information is expected to aid in establishing standards for greenhouse design and to reduce the economic losses incurred by farms.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.356-367
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• 2018

A series of tests dedicated to station blackout (SBO) accident scenarios have been recently performed at the $Prim{\ddot{a}}rkreislauf-Versuchsanlage$ (primary coolant loop test facility; PKL) facility in the framework of the OECD/NEA PKL-3 project. These investigations address current safety issues related to beyond design basis accident transients with significant core heat up. This work presents a detailed analysis using the best estimate thermal-hydraulic code TRACE (v5.0 Patch4) of different SBO scenarios conducted at the PKL facility; failures of high- and low-pressure safety injection systems together with steam generator (SG) feedwater supply are considered, thus calling for adequate accident management actions and timely implementation of alternative emergency cooling procedures to prevent core meltdown. The presented analysis evaluates the capability of the applied TRACE model of the PKL facility to correctly capture the sequences of events in the different SBO scenarios, namely the SBO tests H2.1, H2.2 run 1 and H2.2 run 2, including symmetric or asymmetric secondary side depressurization, primary side depressurization, accumulator (ACC) injection in the cold legs and secondary side feeding with mobile pump and/or primary side emergency core coolant injection from the fuel pool cooling pump. This study is focused specifically on the prediction of the core exit temperature, which drives the execution of the most relevant accident management actions. This work presents, in particular, the key improvements made to the TRACE model that helped to improve the code predictions, including the modeling of dynamical heat losses, the nodalization of SGs' heat exchanger tubes and the ACCs. Another relevant aspect of this work is to evaluate how well the model simulations of the three different scenarios qualitatively and quantitatively capture the trends and results exhibited by the actual experiments. For instance, how the number of SGs considered for secondary side depressurization affects the heat transfer from primary side; how the discharge capacity of the pressurizer relief valve affects the dynamics of the transient; how ACC initial pressure and nitrogen release affect the grace time between ACC injection and subsequent core heat up; and how well the alternative feeding modes of the secondary and/or primary side with mobile injection pumps affect core quenching and ensure stable long-term core cooling under controlled boiling conditions.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.318-329
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• 2016

There has been a growing interest on the effect of volcanic eruption on the aviation safety, air travel and economy especially after the eruption of Eyjafjallajokull in Iceland. Since volcanic eruption is influential on a large geographic region, the effect usually extends to other neighboring countries. Korea also has an active volcano named Mountain Baekdu. Hence, the need to estimate in advance the quantitative impact of the potential eruption of Mt. Baekdu on South Korean air transportation system. However, previous studies with quantitative estimation were confined to the calculation of the direct economic loss from shut down of the airports, grounding of airlines, and trade deficits caused by the eruption. Therefore, this paper introduces a new approach to assess more accurate impact simultaneously considering volcanic ash dispersal and aviation routes. This approach is then applied to a virtual scenario to predict the damage to air traffic. With further development, this method can help estimate the damage in the air transportation industry in more accurate and faster ways. Prediction outcomes can also be utilized in setting up the emergency response plan for the air transportation industry and contribute to the creation of more proactive and predictive measures in the future.

• Journal of Preventive Medicine and Public Health
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• v.22 no.3 s.27
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• pp.313-322
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• 1989

The present status of the traffic accident rate in Korea shows that it is the highest in the world with a continuously increasing trend. Human factors account for 90% of the causes of traffic accidents. Therefore, the purpose of this study was to determine some human factors related to traffic accidents by studying the relationship between health status and traffic accidents. To accomplish this purpose, all taxi companies located in the Seoul area were divided in three groups according to the number of taxi possessed, then some companies in each ?roup were randomly selected for study, and a total of 222 drivers in those selected companies were questioned and examined from April 15 to April 22, 1989. Seventy drivers among 222 had experienced a traffic accident. A $x^2$-test was performed on the data, then, factor analysis and discrminant analysis were executed with the following results: 1. The drivers complaining of gastroenteric symptoms numbered 110(49.5%), which was the major symptom among all drivers complaining of poor health. 2. In the primary analysis, variables related to traffic accidents were divided into general, occupational, and health characteristics. Drivers having no traffic accident experience and drivers having that experience were subjected to question about age, educational level, residential status, monthly average income, working hours and days, degree of satisfaction with their profession and homelife, degree of worry about health. degree of fatigue, medication, drunken driving, and illness, but there were no statistical significances. 3. In the factor analysis, the 8 health variables which cause traffic accidents were classified into 3 common factors which were perceived health factor, sleeping and drunken driving, and visual acuity and smoking factor. Perceived health was the factor which contributed most to explaining accidents. 4. In the discriminant analysis, a correct prediction rate of 68.0% was obtained in the factors of all the characteristics. 5. Degree of sttisfaction with their homelife and educational and economic factor in the general characteristics, degree of satisfaction with their profession in the occupational characteristics, and sleeping and drunken driving in the health characteristics were selected as statistically significant factors to discriminant the traffic accident. 6. Among the factors of the general, occupational, and health characteristics, degree of satisfaction with their homelife, driving experience, family factor, perceived factor were selected as the statistically significant factors.

• Journal of Science and Technology Studies
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• v.3 no.2 s.6
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• pp.29-60
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• 2003

Inter-Korean Cooperation in Science and Technology will contribute to building the trust between S.K and N.K as a leading factor and cut down the cost of unification by diminishing the technology lag and the gap of economic level. This study has shown that we can increase the productivity of unified Korea Innovation system if we systematically analyse the present condition of the Inter-Korean Cooperation and promote Inter-Korean Cooperation. In this study, the author analyses the present condition of the Inter-Korean Cooperation with integrated framework of three aspects to clear up the policy of Inter-Korean Cooperation. First, in the national aspect, we make use of the notion of international cooperation and multilateral mechanism of an international organization. Thereafter, we make out the alternatives in the aspects of international relationship and legal and institutional view Second, in the unification aspect, we consider the Inter-Korean Cooperation by the notion of national innovation system. Thereafter, we make out the alternative in the aspect of a phase-dependent approach. Finally, in technology aspect, we consider the Inter-Korean Cooperation by the notion of technology gap, the framework of technology transfer, and technology dependency theory. As a conclusion, through this study, the author have tried to integrate the various theoretical backgrounds. As a result, the author could analyse the present condition of ter-Korean Cooperation in Science and Technology and team a good lesson from it; Therefore, we can use it as a means of evaluation on a cooperation program and prediction for the future status of cooperation.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.361-371
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• 2019

In 2017, the ratio of the number of victims and deaths in the construction industry was the highest with 25.2% and 29.6%, respectively. Especially, as safety accidents at construction sites continue to increase, the economic loss is greatly increased too. Therefore, in order to prevent safety accidents in the construction work, the safety risk assessment index by type of construction was developed, and the main results of this study are as follows. First, 17 factors related to safety accidents at construction sites were derived through survey and interview survey, and this study suggested 9 items(process, type of construction, progress rate, contract amount, number of floors, safety education, working days and weather) throughout the expert advisory meeting. Second, the risk assessment index for safety accidents was developed based on the ratio and intensity of safety accidents. Third, to verify the risk assessment model, the construction safety risk assessment index by type of construction was derived by surveying and analyzing the statistics of the construction accident. In addition, the risk strength was calculated by dividing human damage caused by construction safety accidents into those killed and injured. The risk assessment index based on the frequency and intensity of safety accidents by type of construction is expected to be utilized as basic data when assessing the risk of similar projects in the future.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.3
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• pp.45-52
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• 2018

Currently, apartments have become an important research subject for the overall area of politics, economics, and culture as well as urban architectural study. However, there are few analyses of the research trends related to the current interest in the apartment research and prediction of the future changes of an apartment in politics and industry. In this study, the research information related to the apartment has classified, and the changes in the research trends have analyzed. Based on the classified data, the first thesis and dissertation related to the apartment and changes of academic notation have discovered. In addition, future interests and future research directions through Frequency of Appearance, Degree Centrality Analysis, and Betweenness Centrality Analysis of author keywords were predicted. As a result of the analysis, 'Space,' 'Residential Mobility' and 'Apartment Complex' studies were found to be important research topics throughout the entire period. 'Han Gang Apartment,' 'Small Size Apartment,' 'Civic Apartments,' 'Jamsil,' and 'Child' were newly interested topics until 70's era. '(Super) High-rise Apartment,' 'Perception,' 'Jugong Apartment,' 'Housing Environment,' 'Housewife,' 'Apartment Layout,' and 'Busan' were newly interested topics during the 80's and 90's era. 'Apartment Price,' 'Energy,' 'Remodeling,' 'Noise,' 'Resident Satisfaction,' 'Community,' and 'Apartment Lotting-out' were newly interested topics after the year 2000. New concerns for last decade are found to be 'Super High-rise Apartment', 'Remodeling', 'Indoor'(2007), 'Apartment Reconstruction Project', 'Brand', 'AHP', 'Housing Environment'(2008), 'Ventilation'(2009), 'Apartment Lotting-out'(2010), 'Economic Assessment'(2011), 'Cost'(2012), 'Green Building', 'Apartment Sales', 'Law', 'Society'(2013), 'Floor Impact Noise', 'Seoul'(2014), 'Noise'(2015), 'Hedonic Model'(2016). In addition, following research topics are expected to be active in the future: In maturity stage of the research development is going to be 'Apartment Price', 'Space', 'Management of Apartment Housing'; the hedonic model, which is research growth and development stage, is going to be '(Floor Impact) Noise', 'Community', 'Energy.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.