• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.190-203
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• 2015

VRI(Vulnerability-Resilience Index), which is defined as a function of 3 variables: climate exposure, sensitivity, and adaptive capacity, has been quantified for the case of Typhoon which is one of the extreme weathers that will become more serious as climate change proceeds. Because VRI is only indicating the relative importance of vulnerability between regions, the VRI quantification is prerequisite for the effective adaptation policy for climate in Korea. For this purpose, damage statistics such as amount of damage, occurrence frequency, and major damaged districts caused by Typhoon over the past 20 years, has been employed. According to the VRI definition, we first calculated VRI over every district in the case of both with and without weighting factors of climate exposure proxy variables. For the quantitative estimation of weighting factors, we calculated correlation coefficients (R) for each of the proxy variables against damage statistics of Typhoon, and then used R as weighting factors of proxy variables. The results without applying weighting factors indicates some biases between VRI and damage statistics in some regions, but most of biases has been improved by applying weighting factors. Finally, due to the relations between VRI and damage statistics, we are able to quantify VRI expressed as a unit of KRW, showing that VRI=1 is approximately corresponding to 500 hundred million KRW. This methodology of VRI quantification employed in this study, can be also practically applied to the number of future climate scenario studies over Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.531-552
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• 2020

In the previous study, both the wave characteristics at the tip of composite breakwater and on caisson were investigated by applying olaFlow numerical model of three-dimensional regular waves. In this paper, the same numerical model and layout/shape of composite breakwater as applied the previous study under the action of one directional irregular waves were used to analyze two and three-dimensional spatial change of wave force including the impulsive breaking wave pressure applied to trunk of breakwater, the effect of rear region, and the occurrence of diffracted waves at the tip of caisson located on the high crested rubble mound. In addition, the frequency spectrum, mean significant wave height, mean horizontal velocity, and mean turbulent kinetic energy through the numerical analysis were studied. In conclusion, the larger wave pressure occurs at the front wall of caisson around the still water level than the original design conditions when it generates the shock-crushing wave pressure in three-dimensional analysis condition. Which was not occurred by two-dimensional analysis. Furthermore, it was confirmed that the wave pressure distribution at the caisson changes along the length of breakwater when the same significant incident wave was applied to the caisson. Although there is difference in magnitude, but its variation shows the similar tendency with the case of previous study.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.887-900
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• 2012

Drought is a non-negligible disaster of nature and it is mainly caused by rainfall shortage for a long time though there are many definitions of drought. 'Standard Precipitation Index' (SPI) that is widely used to express the level of meteorological drought intensity has a limit of not being able to consider the hydrological changes such as rainfall and evapotranspiration caused by climate change, because it does not consider the temperature-related variables other than the precipitation. Recently, however, 'Standardized Precipitation Evapotranspiration Index' (SPEI), a drought index of new concept which is similar to SPI but can reflect the effect of temperature variability as well as the rainfall change caused by climate variation, was developed. In this study, the changes of drought occurrence in South Korea were analyzed by applying SPEI for meteorological data (1973~2011) of 60 climate observatories under Korea Meteorological Administration (KMA). As the result of application, both of SPI and SPEI showed the trend of deepening drought in spring and winter and mitigating drought in summer for the entire nation, with SPI showing greater drought intensity than SPI. Also, SPI and SPEI with 12 months of duration showed that severe droughts with low frequency of around 6 years are generally being repeated.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5175-5179
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• 2015

Background: We aimed to determine the frequency of early and late complications following groin surgery for vulvar cancer and analyze possible risk factors. Materials and Methods: This retrospective cohort study included 99 women who underwent for vulvar cancer. The early (${\leq}1$ month) complications were wound infection, breakdown and lymphocyst and late (>1 month) complications were lower limb lymphedema, incontinence and erysipelas. The risk factors for developing each of the complications were analyzed with regression analysis. Results: In the entire cohort, 29 (29.3%) women experienced early and 12 (12.1%) had late complications. Wound complications including infection and breakdown were the leading early complications (23.2%). In the multivariate analysis, both obesity (body mass index ${\geq}30kg/m^2$) and advanced age (${\geq}65years$) were found as independent predictive factors for early complications. Obese women of advanced age had 6.32 times more risk of experiencing any of the early complications, when compared to non-obese and young women (55.6% vs 8.7%). The most common late complication was lower limb lymphedema (10.1%) that was more frequently seen in young women. However, neither age nor lymph node count were significantly associated with the occurrence of lower limb lymphedema. Conclusions: More than 40% of the women suffered from postoperative complications after inguinofemoral lymphadenectomy in the current study. While advanced age and obesity were the significant predictors for any of the early complications, there was no identified risk factor for lower limb lymphedema.

• Pediatric Infection and Vaccine
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• v.8 no.1
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• pp.123-128
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• 2001

Clonorchiasis, known as the parasitic fluke in the intrahepatic bile ducts, occurs throughout Korea, Japan, Taiwan, and Vietnam with high frequency. Most of the infections follow the consumptions of raw or undercooked freshwater fish, which explains its rare occurrence to childhood even in those areas mentioned earlier above. The authors have experienced it with an 8 year old boy. Abdominal ultrasonogram revealed multiple low echogenic nodules in the right lobe of liver and lymph nodes proliferation around the porta hepatis area. And abdominal C.T. scan also found multiple low attenuated nodules with delayed enhancement in liver parenchyma with hepatomegaly and multiple conglomerated lymph nodes in porta hepatis. Both abdominal ultrasonogram and C.T. scan didn't show any dilatation of peripheral intrahepatic bile ducts. In repeated stool examination, Clonorchis sinensis eggs were found in feces and the result of the skin test for Clonorchis sinensis was positive. The patient had a history of having eaten raw pond smelt back in Cheongju a month before the symptom onset. On the basis of the patient's history and the fact that Clonorchis eggs were found in feces 3 weeks after eosinophilia and symptoms were noted, the authors estimated that the findings of the patient's abdominal sonogram and C.T. scan were the radiologic findings of the acute phase of clonorchiasis, of which no report had ever been made before. So the authors report a case of clonorchiasis with atypical radiologic findings in an 8 year old boy.

• Korean Journal of Plant Taxonomy
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• v.42 no.2
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• pp.116-125
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• 2012

The distribution, vegetation characteristics and an assessment of the conservation status of the Korea endemic species Coreanomecon hylomeconoides Nakai were investigated to collect biological basic data to formulate a conservation strategy. According to the distribution map of C. hylomeconoides based on the literature, specimen information, and local field surveys, natural populations of C. hylomeconoides are intensively distributed in the Southern regions of the peninsula, around Gyeongsangnam-do and Jeollanan-do. C. hylomeconoides was distributed in the middle and low slope adjacent to a valley. The altitude ranged from 227 m to 744 m, with inclinations of $5-10^{\circ}$. As a result of a vegetation survey within natural populations, a total of 238 taxa were identified from 29 quadrates in 8 natural populations. The importance value of C. hylomeconoides is 25.34% based on the coverage and frequency of the herbaceous layer with in the populations. The species diversity of the occurrence of the species in 8 natural populations was 1.52, while the averages of species evenness and the dominance values were calculated to be 0.83 and 0.17, respectively. As a result of assessing the conservation status through IUCN Red List criteria, C. hylomeconoides was evaluated as Near Threatened (NT). Conservation strategies are also discussed for the sustainable conservation of C. hylomeconoides.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.1-18
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• 2021

While content word-based frequency analysis has obvious limitations to intentional deception or irony, KLIWC has evolved into functional word analysis and KrKwic has evolved as a way to visualize co-occurrence frequencies. However, after more than 10 years of development, several issues still need improvement. Therefore, we tried to develop a new psychological language analysis program by analyzing KLIWC and KrKwic. First, the two programs were analyzed. In particular, the morpheme classification of KLIWC and the Korean morpheme analyzer was compared to enhance the functional word analysis function, and the psychological dictionary were analyzed to strengthen the psychological analysis. As a result of the analysis, the Hannanum part-of-speech analyzer was the most subdivided, but KLIWC for personal pronouns and KKMA for endings and endings were more subdivided, suggesting the integrated use of multiple part-of-speech analyzers to strengthen functional word analysis. Second, the research trends of studies that analyzed texts with these programs were analyzed. As a result of the analysis, the two programs were used in various academic fields, including the field of Interdisciplinary Studies. In particular, KrKwic was used a lot for the analysis of papers and reports, and KLIWC was used a lot for the comparative study of the writer's thoughts, emotions, and personality. Based on these results, the necessity and direction of development of a new psychological language analysis program were suggested.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.179-189
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• 2022

Ice and water droplets rise and fall above the freezing altitude under the effects of strong updrafts and downdrafts, grow into hail, and then fall to the ground in the form of balls or irregular lumps of ice. Although such hail, which occurs in a local area within a short period of time, causes great damage to the agricultural and forestry sector, there is a paucity of domestic research toward predicting hail. The objective of this study was to introduce Land-Atmosphere Modeling Package (LAMP) hail prediction and measure its performance for 50 hail events that occurred from January 2020 to July 2021. In the study period, the frequency of occurrence was high during the spring and during afternoon hours. The average duration of hail was 15 min, and the average diameter of the hail was 1 cm. The results showed that LAMP predicted hail events with a detection rate of 70%. The hail prediction performance of LAMP deteriorated as the hail prediction time increased. The radar reflectivity of actual cases of hail indicated that the average maximum reflectivity was greater than 40 dBZ regardless of altitude. Approximately 50% of the hail events occurred when the reflectivity ranged from 30~50 dBZ. These results can be used to improve the hail prediction performance of LAMP in the future. Improved hail prediction performance through LAMP should lead to reduced economic losses caused by hail in the agricultural and forestry sector through preemptive measures such as net coverings.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.