• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.632-644
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• 2022

Purpose To determine the incidence of atypical ductal hyperplasia (ADH) in needle biopsy and the upgrade rate to carcinoma, and to evaluate difference in findings between the upgrade and non-upgrade groups. Materials and Methods Among 9660 needle biopsies performed over 48 months, we reviewed the radiologic and histopathologic findings of ADH and compared the differences in imaging findings (mammography and breast US) and biopsy methods between the upgrade and non-upgrade groups. Results The incidence of ADH was 1.7% (169/9660). Of 112 resected cases and 30 cases followed-up for over 2 years, 35 were upgraded to carcinoma (24.6%, 35/142). The upgrade rates were significantly different according to biopsy methods: US-guided core needle biopsy (USCNB) (40.7%, 22/54) vs. stereotactic-vacuum-assisted biopsy (S-VAB) (16.0%, 12/75) vs. USguided VAB (US-VAB) (7.7%, 1/13) (p = 0.002). Multivariable analysis showed that only US-CNB (odds ratio = 5.19, 95% confidence interval: 2.16-13.95, p < 0.001) was an independent predictor for pathologic upgrade. There was no upgrade when a sonographic mass was biopsied by US-VAB (n = 7) Conclusion The incidence of ADH was relatively low (1.7%) and the upgrade rate was 24.6%. Surgical excision should be considered because of the considerable upgrade rate, except in the case of US-VAB.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.1
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• pp.49-60
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• 2024

A region can be divided into cultivation zones based on homogeneity in weather variables that have the greatest influence on crop growth and yield. This study classified the cultivation zone of soybean using weather indices as a prior study to classify the agroclimatic zone of soybean. Meteorological factors affecting soybeans were determined through correlation analysis over a 10 year period (from 2013 to 2022) using data from the Miryang and Suwon regions collected from the soybean yield trial database of the Rural Development Administration, Korea and the meteorological database of the Korea Meteorological Administration. The correlation between growth characteristics and the minimum temperature, daily temperature range, and precipitation were high during the vegetative growth stages. Moreover, the correlation between yield components and the maximum temperature, daily temperature range, and precipitation were high during the reproductive growth stages. As a result of k-means clustering, soybean cultivation zones were divided into three zones. Zone 1 was the central inland region and southern Gyeonggi-do; Zone 2 was the southern part of the west coast, the southern part of the east coast, and the South Sea; and Zone 3 included parts of eastern Gyeonggi-do, Gangwon-do, and areas with high altitudes. Zone 1, which has a wide latitude range, was further subdivided into three cultivation zones. The results of this study may provide useful information for estimating agrometeorological characteristics and predicting the success of soybean cultivation in South Korea.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.366-376
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• 2023

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37' N 128°32' E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

• Tuberculosis and Respiratory Diseases
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• v.46 no.5
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• pp.685-696
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• 1999

Background : Idiopathic pulmonary fibrosis (IPF) is a diffuse inflammatory and fibrosing process that occurs within the interstitium and alveolus of the lung with invariably poor prognosis. The major problem in management of IPF results from the variable rate of disease progression and the difficulties in predicting the response to therapy. The purpose of this retrospective study was to evaluate the short-term efficacy of steroid and immunosuppressive therapy for IPF and to identify the pre-treatment determinants of favorable response. Method : Twenty patients of IPF were included. Diagnosis of IPF was proven by thoracoscopic lung biopsy and they were presumed to have active progressive disease. The baseline evaluation in these patients included clinical history, pulmonary function test, bronchoalveolar lavage (BAL), and chest high resolution computed tomography (HRCT). Fourteen patients received oral prednisolone treatment with initial dose of 1mg/kg/day for 8 to 12 weeks and then tapering to low-dose prednisolone (0.25mg/kg/day). Six patients who previously had experienced significant side effects to steroid received 2mg/kg/day of oral cyclophosphamide with or without low-dose prednisolone. Follow-up evaluation was performed after 6 months of therapy. If patients met more than one of followings, they were considered to be responders : (1) improvement of more than one grade in dyspnea index, (2) improvement in FVC or TLC more than 10% or improvement in DLco more than 20% (3) decreased extent of disease in chest HRCT findings. Result : One patient died of extrapulmonary cause after 3 month of therapy, and another patient gave up any further medical therapy due to side effect of steroid. Eventually medical records of 18 patients were analyzed. Nine of 18 patients were classified into responders and the other nine patients into nonresponders. The histopathologic diagnosis of the responders were all nonspecific interstitial pneumonia (NSIP) and that of nonresponders were all usual interstitial pneumonia (UIP) (p<0.001). The other significant differences between the two groups were female predominance (p<0.01), smoking history (p<0.001), severe grade of dyspnea (p<0.05), lymphocytosis in BAL fluid ($23.8{\pm}16.3%$ vs $7.8{\pm}3.6%$, p<0.05), and less honeycombing in chest HRCT findings (0% vs $9.2{\pm}2.3%$, p<0.001). Conclusion : Our results suggest that patients with histopathologic diagnosis of NSIP or lymphocytosis in BAL fluid are more likely to respond to steroid or immunosuppressive therapy. Clinical results in large numbers of IPF patients will be required to identify the independent variables.

• Journal of dental hygiene science
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• v.4 no.3
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• pp.141-148
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• 2004

This study was examed in order to determine influential factors of health promoting behavior on Dental Hygiene students the health promoting behavior. So examed students' health promoting behavior, self-efficacy, perceived benefit, perceived barrier, a health locus of control, self-esteem. A the result of this study were as follows: (1) Performance mean score in health promoting behavior was 2.60, self achievement score was 2.89, health responsibility score was 2.12, exercise score was 1.89, nutrition score was 2.45, interpersonal support score was 2.97, stress management score was 2.63. Performance mean score in self-efficacy was 2.56, perceived benefit was 3.45, perceived barrier was 2.32, a health locus of control score was 3.04, self-esteem score was 2.81. (2) Performance in health promoting behavior was significant differences in year, religion, economical level, experience of disease on family, perceived health status(p<0.05), perceived oral health status(p<0.001). Performance in self achievement was significant differences in year, economical level, perceived health status(p<0.05), religion, perceived oral health status(p<0.01). Performance in health responsibility was significant differences in year, religion, economical level, BMI(p<0.05) and experience of disease on myself, perceived oral health status(p<0.001). Performance in excercise was significant differences in mother's educational level, experience of disease on family, perceived oral health status(p<0.05) and nutrient was economical level, perceived oral health status(p<0.01), perceived health status(p<0.05). Performance in interpersonal relations was only significant differences perceived oral health status(p<0.05) and in stress management was year, perceived oral health status(p<0.05). (3) Performance in self-efficacy was significant differences in economical level, health status(P<0.05) and perceived health status, perceived oral health status(p<0.01). Performance in perceived benefit was significant differences in religion(p<0.05). Performance in perceived barrier was significant differences economical level, perceived oral health status(p<0.05), experience of disease on myself(p<0.01). Performance in a health locus of control was significant differences year(p<0.05), performance in a perceived oral health status(p<0.01). (4) Performance in health promoting behavior was significantly correlated with self-efficacy(r=0.376), perceived benefit(r=0.188), perceived barrier(r=-0.155), a health locus of control (r=0.064), self-esteem(r=0.318). (5) Self-efficacy was the highest factor predicting health promoting behavior.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.