• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.599-607
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• 2015

The purpose of this study is to reproduce long-term wave fields in coastal waters of Korea based on wave hind-casting modelling and discuss its applications. To validate wind data(NCEP, ECMWF, JMA-MSM), comparison of wind data was done with wave buoy data. JMA-MSM predicted wind data with high accuracy. But due to relatively longer period of ECMWF wind data as compared to that of JMA-MSM, wind data set of ECMWF(2001~2014) was used to perform wave hind-casting modelling. Results from numerical modelling were verified with the observed data of wave buoys installed by Korea Meteorological Administration(KMA) and Korea Hydrographic and Oceanographic Agency(KHOA) on offshore waters. The results agree well with observations at buoy stations, especially during the event periods such as a typhoon. Consequently, the wave data reproduced by wave hind-casting modelling was used to obtain missing data in wave observation buoys. The obtained missing data indicated underestimation of maximum wave height during the event period at some points of buoys. Reasons for such underestimation may be due to larger time interval and resolution of the input wind data, water depth and grid size etc. The methodology used in present study can be used to analyze coastal erosion data in conjunction with a wave characteristic of the event period in coastal areas. Additionally, the method can be used in the coastal disaster vulnerability assessment to generate wave points of interest.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.211-217
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• 2004

The instrument used in this study consists of a lkg capacity loadcell and a Imm tipping bucket rain gauge. There are two signals: one is the weight of the water in the tipping bucket and the other is the pulse from the reversing mechanism of the tipping bucket. The loadcell measures the weight of water with a 0.0lmm resolution up to 1mm rainfall and the bucket reverses beyond 1mm. From this point, a pulse signal generates and the loadcell starts measuring the weight again. A field test was carried out with the range of rainfall intensity from 42mm/h to 250mm/h. The result shows an error range from -2.2% to + 2.6% in 12 measurement cases with a rainfall of l00mm or more. This result satisfies the WMO recommendation for rainfall intensity instrumentation which allows a 5% range. In a field experiment during 17 to 19 August, 2004, more than 100mm/h rainfall intensity was observed by this instrument, confirming that our instrument has a sufficient capacity of rainfall intensity measurement under extreme conditions like Jangma (Bai-u season). Compared with existing commercial models which employ a water drop measurement method, our method can give a practical solution for diagnostic check of remote rain gauges using two independent signals.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.47-56
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• 2021

This study a secondary data analysis study attempted to identify the factors influencing the successful aging of the elderly in Korea. Using the data of the 7th Aging Research Panel in 2018, 4,106 people over 65 years of age who had at least one non-living child and no missing values in the study variables were enrolled. Data were analyzed by frequency analysis, crossover analysis, independent sample t-test, and binary logistic regression analysis. The results of this study revealed that the factors affecting successful aging among elderly included age, the presence or absence of a spouse, education level, housing type, subjective health, exercise, alcohol drinking, and non-face-to-face contact frequency with non-cohabiting children, and the explanatory power of the variables was 24.1%. In order for the elderly to achieve successful aging, centering on child ties, the frequency of non-face-to-face contact, which can comfort the elderly's life and increase the satisfaction of life in a continuous relationship, is more important than having children live close and meet frequently. Based on this study, various strategies are needed for the successful aging of elderly people who are socially isolated due to concerns about COVID-19 infection.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.300-314
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• 2023

Purpose: This study sought to predict the reasons for skipping breakfast by adolescents aged 13-18 years using the 7^th Korea National Health and Nutrition Examination Survey (KNHANES). Methods: The participants included 1,024 adolescents. The data were analyzed using a complex-sample t-test, the Rao Scott χ²-test, and the classification and regression tree (CART) algorithm for decision tree analysis with SPSS v. 27.0. The participants were divided into two groups, one regularly eating breakfast and the other skipping it. Results: A total of 579 and 445 study participants were found to be breakfast consumers and breakfast skippers respectively. Breakfast consumers were significantly younger than those who skipped breakfast. In addition, breakfast consumers had a significantly higher frequency of eating dinner, had been taught about nutrition, and had a lower frequency of eating out. The breakfast skippers did so to lose weight. Children who skipped breakfast consumed less energy, carbohydrates, proteins, fats, fiber, cholesterol, vitamin C, vitamin A, calcium, vitamin B1, vitamin B2, phosphorus, sodium, iron, potassium, and niacin than those who consumed breakfast. The best predictor of skipping breakfast was identifying adolescents who sought to control their weight by not eating meals. Other participants who had low and middle-low household incomes, ate dinner 3-4 times a week, were more than 14.5 years old, and ate out once a day showed a higher frequency of skipping breakfast. Conclusion: Based on these results, nutrition education targeted at losing weight correctly and emphasizing the importance of breakfast, especially for adolescents, is required. Moreover, nutrition educators should consider designing and implementing specific action plans to encourage adolescents to improve their breakfast-eating practices by also eating dinner regularly and reducing eating out.