• FLOWER RESEARCH JOURNAL
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• v.16 no.1
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• pp.36-43
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• 2008

This purpose of this study was to examine the effect of planting density, growing medium and strength of a nutrient solution (National Horticultural Research Institute's nutrient solution: HRI's) on the growth and development of Oriental hybrid lily 'Le Reve' in a box cultivation. The planting density with 14, 18 and 22 bulbs had sprouting one day earlier than other treatments. Planting density of 22 bulbs flowered first, while six bulbs flowered the last, indicating that higher planting densities led earlier flowering. The increasing planting density increased stem length of cut flowers. On the other hand, cut flower quality was improved when the planting density was lower. The incidence of physiological disorders such as blasting was more frequent in planting density of 22, 18, and 14, indicating that higher planting densities caused higher incidences of physiological disorders. All planting densities except 22 bulbs displayed superior results in width, weight, number, and scale weight of the bulbs. Greater planting densities led to inferior bulb enlargement and an increased decomposition rate. pH decreased in all treatments after the bulb enlargement and decreased more as the planting density increased. Contents of P, K, Ca, and Mg increased, while contents of K and Ca decreased, as the planting density increased. The rice hull+coir (1:1, v/v) treatment was better than others, but did not show that much of a difference. Moreover, in bulbs enlargement after cut flower harvest, lily medium and perlite+peat moss treatments showed superior results, and decomposition rate was the greatest in the rice hull+coir (1:1, v/v) treatment. In the HRI's solution strength treatment from the period of flower bud emergence to flower harvest, higher solution strengths gave better cut flower quality in terns of length, weight, and number of flowers. The non-treated control and one third strength of a HRI's solution hastened flowering, indicating that lower strengths led to earlier flowering. According to the results of leaf analysis as affected by solution strength during the flower harvest, absorption rates of N and K were greater when the strength was higher, and Ca and Mg showed the same tendency. On the other hand, the absorption rate of P was the lowest in all treatments.

• Journal of the korean academy of Pediatric Dentistry
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• v.48 no.2
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• pp.221-228
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• 2021

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human. A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 - 7 years were used for this study. The model used for machine learning was Google's teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group. As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69. This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.27-34
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• 2017

The author investigated interventional radiology patient doses in several other countries, assessed accuracy of DAP meters embedded in intervention equipments in domestic country, conducted measurement of patient doses for 13 major interventional procedures with use of Dose Area Product(DAP) meters from 23 hospitals in Korea, and referred to 8,415 cases of domestic data related to interventional procedures by radiation exposure after evaluation the actual effectives of dose reduction variables through phantom test. Finally, dose reference level for major interventional procedures was suggested. In this study, guidelines for patient doses were $237.7Gy{\cdot}cm^2$ in TACE, $17.3Gy{\cdot}cm^2$ in AVF, $114.1Gy{\cdot}cm^2$ in LE PTA & STENT, $188.5Gy{\cdot}cm^2$ in TFCA, $383.5Gy{\cdot}cm^2$ in Aneurysm Coil, $64.6Gy{\cdot}cm^2$ in PTBD, $64.6Gy{\cdot}cm^2$ in Biliary Stent, $22.4Gy{\cdot}cm^2$ in PCN, $4.3Gy{\cdot}cm^2$ in Hickman, $2.8Gy{\cdot}cm^2$ in Chemo-port, $4.4Gy{\cdot}cm^2$ in Perm-Cather, $17.1Gy{\cdot}cm^2$ in PCD, and $357.9Gy{\cdot}cm^2$ in Vis, EMB. Dose referenece level acquired in this study is considered to be able to use as minimal guidelines for reducing patient dose in the interventional radiology procedures. For the changes and advances of materials and development of equipments and procedures in the interventional radiology procedures, further studies and monitorings are needed on dose reference level Korean DAP dose conversion factor for the domestic procedures.

• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.151-174
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• 2022

According to the "Special Act on the Restoration and Maintenance of the Core Relics of the Shilla Kingdom" enacted in 2019, the Shilla Kingdom refers to the capital of Shilla and Unified Shilla period, and refers to Gyeongju, where the king lived, and the nearby area. Shilla Wanggyeong is a heritage registered on the UNESCO World Heritage List in 2000 under the name of Gyeongju Historic Site and belongs to Wolseong District, Hwangnyongsa District, and Daeneungwon District among the five districts registered as Gyeongju Historic Site. Unlike the Namsan and Sanseong districts, the Shilla Kingdom is a heritage consisting mostly of archaeological sites without physical substance. Gyeongju City sought to promote local tourism while providing more direct experiences to visitors by restoring the heritage that constitutes the Shilla Kingdom. Starting with the restoration of Woljeonggyo Bridge in 2005, the Shilla Wanggyeong restoration project began in earnest. Gyeongju City tried to restore the building site on the west side of Donggung Palace and Wolji after Woljeonggyo Bridge, but it was canceled due to opposition from the UNESCO World Heritage Committee. The World Heritage Committee opposed the restoration and recommended a heritage impact assessment for similar projects in the future. During the miscarriage impact assessment procedure, there is an OUV attribute analysis process of the heritage to be evaluated. This study intends to preemptively derive OUV attributes for the Silla Kingdom through literature and overseas case analysis. In the case of literature research, domestic and foreign research data related to the UNESCO World Heritage Convention and World Heritage Management were examined, and in overseas cases, the architectural works of Krakow Historical District, Stonehenge and Abbury Geoseok Ruins in England, and Le Corbusier were analyzed. Through this, the outstanding universal value attributes of the Silla Kingdom were derived. This study is expected to be used as a reference in the process of restoration projects of other heritage constituting the Shilla Kingdom or construction plans in nearby areas in the future and serve as an indicator to improve the management system of the Shilla Kingdom more efficiently from the perspective of world heritage.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.356-365
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• 2022

Agrivoltaic systems, also called solar sharing, stated from an idea that utilizes sunlight above the light saturation point of crops for power generation using solar panels. The agrivoltaic systems are expected to reduce the incident solar radiation, the consequent surface cooling effect, and evapotranspiration, and bring additional income to farms through solar power generation by combining crops with solar photovoltaics. In this study, to evaluate if agrivoltaic systems are suitable for viticulture, we investigated the microclimatic change, the growth of vines and the characteristics of grape grown under solar panels set by planting lines compared with ones in open vineyards. There was high reduction of wind speed during over-wintering season, and low soil temperature under solar panel compared to those in the open field. There was not significant difference in total carbohydrates and bud burst in bearing mother branches between plots. Despite high content of chlorophyll in vines grown under panels, there is no significant difference in shoot growth of vines, berry weight, cluster weight, total soluble solid content and acidity of berries, and anthocyanin content of berry skins in harvested grapes in vineyards under panels and open vineyards. It was observed that harvesting season was delayed by 7-10 days due to late skin coloration in grapes grown in vineyards under panels compared to ones grown in open vineyards. The results from this study would be used as data required in development of viticulture system under panel in the future and further study for evaluating the influence of agrivoltaic system on production of crops including grapes.

• Korean Journal of Culture and Social Issue
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• v.22 no.4
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• pp.487-522
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• 2016

The main purpose of this research is to investigate Korean adolescents' perception of stress experiences, and related social support. To this end, adolescents were asked about stress events, as well as stress symptoms, in their lives. Also, the adolescents were asked about the people that provided social support and the types of social support provided. The participants were 952 Korean adolescents (Primary 219; Middle 280; High 212; University 241). Among the four measures (stress events, stress symptoms, social supporters, and types of social support), the measure of stress symptoms yielded a reliability of Cronbach α=.88, while the remaining three measures yielded an inter-judger reliability of 89.6%, Kappa=.87. The results were as follows. First, for stress events, the most frequent responses were related to Academic Achievement, followed by Career/Job, Family Relations, Friend Relations, Lack of Capacity, and Financial Difficulties. For high-school students the most frequent responses were related to Academic Achievement, while for university students Career/Job. Second, for stress symptoms there were significant differences among the groups, in that the high-school students showed the highest level of symptoms, while primary school students the lowest. Third, for social supporters, the most frequent responses were related to Friends, followed by Myself, Parents, Teacher, Siblings, and Seniors/Juniors. As the groups aged (from primary to university), support from Friends and Seniors/ Juniors increased, while support from Parents decreased. Fourth, for the types of social support, the most frequent responses were related to Emotional Support, followed by None, Advice, Supporter Directly Solved Problem, and Talked with Me. The highest frequencies of responses were found for Emotional Support among all groups. As the groups aged (from primary to university), Advice increased while Supporter Directly Solved Problem decreased.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of the Korean Geotechnical Society
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• v.23 no.9
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• pp.5-16
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• 2007

The simple linear elastic-perfectly plastic model with soil parameters $s_u,\;E_u$ and n of undrained condition is usually applied to predict the displacement of a constructed diaphragm wall(DW) on soft soils during excavation. However, the application of this soil model for finite element analysis could not interpret the continued increment of the lateral displacement of the DW for the large and deep excavation area both during the elapsed time without activity of excavation and after finishing excavation. To study the characteristic behaviors of soil behind the DW during the periods without excavation, a series of tests on soft Bangkok clay samples are simulated in the same manner as stress condition of soil elements happening behind diaphragm wall by triaxial tests. Three kinds of triaxial tests are carried out in this research: $K_0$ consolidated undrained compression($CK_0U_C$) and $K_0$ consolidated drained/undrained unloading compression with periodic decrement of horizontal pressure($CK_0DUC$ and $CK_0UUC$). The study shows that the shear strength of series $CK_0DUC$ tests is equal to the residual strength of $CK_0UC$ tests. The Young's modulus determined at each decrement step of the horizontal pressure of soil specimen on $CK_0DUC$ tests decreases with increase in the deviator stress. In addition, the slope of Critical State Line of both $CK_0UC$ and $CK_0DUC$ tests is equal. Moreover, the axial and radial strain rates of each decrement of horizontal pressure step of $CK_0DUC$ tests are established with the function of time, a slope of critical state line and a ratio of deviator and mean effective stress. This study shows that the results of the unloading compression triaxial tests can be used to predict the diaphragm wall deflection during excavation.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.