• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.183-185
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• 2021

Detecting bacteria in blood could be an important research area in medicine and computer vision. In this paper, we propose a method for detecting bacteria in blood from 366 darkfield microscopy images acquired at Kaggle. Generate a training dataset through preprocessing and data augmentation using image processing techniques, and define a deep learning model for learning it. As a result of the experiment, it was confirmed that the proposed deep learning model effectively detects red blood cells and bacteria in darkfield microscopy images. In this paper, we learned using a relatively simple model, but it seems that more accurate results can be obtained by using a deeper model.

• Journal of the Korean Society of Food Culture
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• v.24 no.1
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• pp.23-32
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• 2009

This study was conducted to generate fundamental data required by food coordinators and food space creators for planning and directing table settings. The results of this study were then used to suggest an ideal model of table settings for Korean-style food equipped with simple, sophisticated, and practical characteristics. Specifically, this study evaluated the importance of hygiene (safety, cleanness, arrangement), decoration (dignity, form, stylishness, presentation of food on plates), naturalness (seasonal beauty, comfortableness, natural beauty), and modernity (modern style, chic style, urban style). These factors were evaluated according to the preference of the table setting and the characteristics of the meeting, which fit various meal cultures, times, places, and objectives. The results of this study indicate that people prefer hygiene and decoration for family meetings (bansang setting), hygiene and modernity for friendly meetings (simple buffet setting), hygiene and decoration for company meetings (simple buffet setting), and hygiene and decoration for academic meetings (tea party). Hygiene and decoration were highly evaluated in most cases, which indicates that individuals at meetings for special purposes give weight to the meeting's atmosphere, but also consider the hygiene and cleanliness of the food.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1D
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• pp.45-51
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• 2010

Traffic volume is essential data for traffic control or maintenance and rehabilitation planning. The volume especially with respect to the type of vehicles can facilitate to those road operations. In this research, a method for vehicle classification was developed using skewed sensors which can generate traffic signatures. In order to characterize vehicle types, the method investigates whether the second axle of each vehicle consists of dual tires. The presence of dual tire is determined by the discriminate function obtained from discriminant analysis. The validation using 1,878 vehicles recorded from a highway using a CCTV camera indicated significantly accurate results: 96.92% for class 1, 82.91% for class 3 and 79.13% for class 4.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.229-230
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• 2022

Smart AtoN(Aids to Navigation) that meet the future environment will generate variety of information and be provided in variety. In order to provide a customized service to marine users, the managers of AtoN should be able to check the any time and data in the desired format. In addition, in order to properly manage the AtoN in the future, it is necessary to identify the cause of the AtoN accidents and make efforts to prevent accident. In this study, 7 types of causes and 11 types of accidents were presented for AtoN accidents.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.17-17
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• 2022

Leaves are an important organism for photosynthesis and transpiration. The shape of leaf is crucial factor affecting plant architecture. V-shape leaf rolling is enhancing canopy photosynthesis by increasing the CO₂ penetration and the light capture by reducing the shadow between the leaves. Therefore, moderate leaf rolling is thought to more high grain yield per area than flat leaf. We investigated 278 KRICE_CORE accession's Adaxial Leaf Rolling Index (LRI) in first heading using the following equation. For each accession, genomic DNA was used for sequencing. We sequenced the genomics with ~8 X coverage to detect SNPS. Raw reads were aligned against the rice reference (IRGSP 1.0) for SNP identification and genotype calling. To generate genotype data for GWAS, SNPs were filtered with minor allele frequency 0.05. Finally, 841,134 high-quality SNPs were used for our GWAS. The significant threshold was -log10(P)>7.23. From the results, 2 significance SNP were detected. Considering the LD block of 250kbp, 60 candidate gene were selected including Hypothetical gene and Conserved gene. In this poster, we analyzed candidate gene affecting adaxial Leaf Rolling through single-trait GWAS.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.243-243
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• 2022

Leaves are an important organism for photosynthesis and transpiration. The shape of leaf is crucial factor affecting plant architecture. V-shape leaf rolling is enhancing canopy photosynthesis by increasing the CO₂ penetration and the light capture by reducing the shadow between the leaves. Therefore, moderate leaf rolling is thought to more high grain yield per area than flat leaf. We investigated 278 KRICE CORE accession's Adaxial Leaf Rolling Index (LRI) in first heading using the following equation. For each accession, genomic DNA was used for sequencing. We sequenced the genomics with ~8 X coverage to detect SNPS. Raw reads were aligned against the rice reference (IRGSP 1.0) for SNP identification and genotype calling. To generate genotype data for GWAS, SNPs were filtered with minor allele frequency 0.05. Finally, 841,134 high-quality SNPs were used for our GWAS. The significant threshold was -log10(P) >7.23. From the results, 2 significance SNP were detected. Considering the LD block of 250kbp, 60 candidate gene were selected including Hypothetical gene and Conserved gene. In this poster, we analyzed candidate gene affecting adaxial Leaf Rolling through single-trait GWAS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.