• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1339-1348
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• 2020

Phytoplankton controls marine ecosystems in terms of nutrients, photosynthetic rate, carbon cycle, etc. and the degree of its influence on the marine environment depends on their physical size. Many studies have been attempted to identify marine phytoplankton size classes using the remote sensing techniques. One of successful approach was the three-component model which estimates the chlorophyll concentrations of three phytoplankton size classes (micro-phytoplankton; >20 ㎛, nano-; 2-20 ㎛ and pico-; <2 ㎛) as a function of total chlorophyll. Here, we examined the applicability of Geostationary Ocean Colour Imager (GOCI) to the mapping of the phytoplankton size class distribution in the East Sea. A fit of the three-component model to a biomarker pigment dataset collected in the study area for some years including a large harmful algal bloom period has been carried out to derive size-fractioned chlorophyll concentration (CHL). The tuned three-component model was applied to the hourly GOCI images to identify the fractions of each phytoplankton size class for the entire CHL. Then, we investigated the distribution of phytoplankton community in terms of the size structure in the East Sea during the harmful Cochlodinium polykrikoides blooms in the summer of 2013.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Conservation Science in Museum
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• v.28
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• pp.51-64
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• 2022

This study aims to identify the impact of optical energy on cultural properties when the light energy irradiates cultural assets during augmented reality (AR) or media façade performances as activities designed to garner public interest. The 10-story Gyeongcheonsa Pagoda was used for this study, and the impact was evaluated by comparing the optical energy irradiated during a media façade performance with the energy irradiated under normal conditions. For comparison, this study measured the illuminance in lux for each light source that irradiated the ten-story stone pagoda and used the data to calculate illuminance in lux-hours. The results showed that the pagoda receives 786.4 lux per hour when both sunlight and artificial light are present, while 13.2 lux of energy is irradicated by the media façade for each performance. The result indicates that the pagoda receives about 29.8 times more optical energy from sunlight and artificial light sources than during media façade performances on an hourly basis, when the performance is carried out twice a week. This study therefore concludes that the optical energy of media façade performances inflicted trivial damage to the ten-story stone pagoda.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.913-922
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• 2022

The purpose of this research is to propose the measurement of improving DEM by using the water surface range of SAR image analysis for river corridors and to suggest the construction of satellite-based 3D river spatial information of inaccessible regions such as North Korea. For this research, it has been progressed from the accessible area, watershed of Namgang river, the branch of Nakdonggang river. The satellite image was collected from SAR satellite image data for a year in 2021 which was provided by ESA from Sentinel-1A/B data and extracted from the seasonal water surface area. Ground gauge water level was collected from hourly intervals data by WAMIS. The DEM was improved by analysis of the river altitude of water surface area change by the combination of the ground water level of minimum to maximum water surface area data extracted from SAR image analysis. After the improvement of DEM, the altitude of the river varied that it is defined to comprise more natural form of river DEM than the existing DEM. The correction validation of improvement DEM was necessary in field survey elevation data; however, the correction validation was not progressed due to the absence of the data. Although, the purpose of this research is to provide the improvement of DEM by using the analyzed water surface by existing DEM data and SAR image analysis. After the progression of additional correction validation research, we would plan to examine the application in other places and to progress the additional methodological research to apply in inaccessible and unmeasured area including the North Korea.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.205-232
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• 2022

This study analyzes the effect of the 2^nd pilot test of Tiime of Use (TOU) pricing for Korean households using a two-level electricity demand model. The test, implemented from May to September 2021, was conducted to compare the effects of two TOU pricing rates and the standard rates for households living in apartment and detached house in 7 provinces of Korea. Based on the data on electricity consumption during the test period and during the same period last year of the 1,292 participants and their socio-economic characteristics, this study analyzes (1) whether the relative demand across periods has changed in response to hourly price changes and (2) whether the price responsiveness of daily consumption has changed after the introduction of TOU pricing. The results show that both types of TOU pricing affect neither the relative demand across periods nor the price responsiveness of daily consumption. The reason behind the results could be related to the level of TOU pricing rates and the periodical classification, which were not sufficient to induce changes in the participants' electricity demand patterns.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Atmosphere
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• v.33 no.4
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• pp.367-385
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• 2023

The variabilities of precipitation and particulate matters (i.e., PM₁₀ and PM_2.5) and the scavenging efficiency of PMs by precipitation were quantified using long-term measurements in Seoul, Korea. The 21 years (2001~2021) measurements of precipitation and PM₁₀ mass concentrations, and the 7 years (2015~2021) of PM_2.5 mass concentrations were used. Statistical analysis was performed for each period (i.e., year, season, and month) to identify the long-term variabilities of PMs and precipitation. PM₁₀ and PM_2.5 decreased annually and the decreasing rate of PM₁₀ was greater than PM_2.5. The precipitation intensity did not show notable variation, whereas the annual precipitation amount showed a decreasing trend. The summer precipitation amount contributed 61.10% to the annual precipitation amount. The scavenging efficiency by precipitation was analyzed based on precipitation events separated by 2-hour time intervals between hourly precipitation data for 7 years. The scavenging efficiencies of PM₁₀ and PM_2.5 were quantified as a function of precipitation characteristics (i.e., precipitation intensity, amount, and duration). The calculated average scavenging efficiency of PM₁₀ (PM_2.5) was 39.59% (35.51%). PM₁₀ and PM_2.5 were not always simultaneously scavenged due to precipitation events. Precipitation events that simultaneously scavenged PM₁₀ and PM_2.5 contributed 42.24% of all events, with average scavenging efficiency of 42.93% and 43.39%. The precipitation characteristics (i.e., precipitation intensity, precipitation amount, and precipitation duration) quantified in these events were 2.42 mm hr^-1, 15.44 mm, and 5.51 hours. This result corresponds to 145% (349%; 224%) of precipitation intensity (amount; duration) for the precipitation events that do not simultaneously scavenge PM₁₀ and PM_2.5.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.620-629
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• 2022

By varying various experimental conditions such as heating rate, molar hourly space velocity (MHSV), and nitridation reaction temperature, vanadium oxynitride was prepared through temperature programmed reduction/nitridation reaction (TPRN) of vanadium pentoxide and ammonia, and characterization were performed. In order to investigate the physico-chemical properties of the prepared catalyst, N₂ adsorption-desorption analysis, X-ray diffraction analysis (XRD), hydrogen temperature programmed reduction (H₂-TPR), temperature programmed oxidation (TPO), ammonia temperature programmed desorption (NH₃-TPD), transmission electron microscopy (TEM) was performed. Transformation of V₂O₅ with 5 m² g^-1 low specific surface area by reduction at 340 ℃ to V₂O₃ showed a high specific surface area value of 115 m² g^-1 by micropore formation. As the nitridation temperature increased beyond that, the specific surface area continued to decrease due to sintering. The nitridation reaction variable that had the greatest influence on the specific surface area was the reaction temperature, and the x + y value of VN_xO_y of a single phase approached from 1.5 to 1.0 as the nitridation reaction temperature increased. At a high reaction temperature of 680 ℃, the cubic lattice constant a was VN. close to the value. At 680 ℃, the highest nitridation temperature among the experimental conditions, the ammonia conversion rate was 93%, and no deactivation was observed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.839-848
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• 2006

Korea has become the 5th country to own and operate the high speed railroad in 2004. However, there were many difficulties until Koreans enjoy the first bullet train service with the average hourly speed of 300km. The high speed railroad requires elevated quality standards differently from the traditional railways. In addition to the technical difficulties, the construction project itself was an unpleasant case with huge delays and cost overruns mainly due to the lack of experiences, deficiency of owner$^{\circ}{\O}$s role, and increase of public resistances triggered by environmental concerns. This paper analyzes the reasons for delays on this mega-project. With respect to the characteristics of the whole project level, it is very complicated/linear project, whose total length is around 412 km with the composition of various sections in the route of the railway which have basically different conditions. For that reason, the analysis is performed in both macro and micro level. First, macroscopic analysis is performed to find critical subdivisions in the railway route that induces the significant delay in the opening due date. Then, microscopic analysis is followed to quantify the causes and effects of delays focused on these critical subdivisions in more detailed way. Finally, this paper provides lessons learned from this project to avoid the decisive delays in performing the similar large-scaled projects.