• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.81-92
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• 2019

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models (GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.39-43
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• 2019

Hydrogen energy has positive effects as an alternative energy source to overcome the energy shortage issues. On the other hand, since stability is very important in use, sensor technology that enables accurate and rapid detection of hydrogen gas is highly required. In this study, hydrogen sensor was developed on AlGaN/GaN heterostructure platform using Pd catalyst where a recess structure was employed to improve the sensitivity. Temperature and bias voltage dependencies on sensitivity were carefully investigated using a hydrogen concentration of 4% that is the safety threshold concentration. Due to the excellent properties of AlGaN/GaN heterostructure in conjunction with the recess structure, a very high sensitivity of 56% was achieved with a fast response speed of 0.75 sec.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• Journal of Veterinary Science
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• v.24 no.3
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• pp.48.1-48.13
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• 2023

Background: Senecavirus A (SVA), a member of the family Picornaviridae, is newly discovered, which causes vesicular lesions, lameness in swine, and even death in neonatal piglets. SVA has rapidly spread worldwide in recent years, especially in Asia. Objectives: We conducted a global meta-analysis and systematic review to determine the status of SVA infection in pigs. Methods: Through PubMed, VIP Chinese Journals Database, China National Knowledge Infrastructure, and Wanfang Data search data from 2014 to July 26, 2020, a total of 34 articles were included in this analysis based on our inclusion criteria. We estimated the pooled prevalence of SVA in pigs by the random effects model. A risk of bias assessment of the studies and subgroup analysis to explain heterogeneity was undertaken. Results: We estimated the SVA prevalence to be 15.90% (1,564/9,839; 95% confidence interval [CI], 44.75-65.89) globally. The prevalence decreased to 11.06% (945/8,542; 95% CI, 28.25-50.64) after 2016. The highest SVA prevalence with the VP1-based RT-PCR and immunohistochemistry assay was 58.52% (594/1,015; 95% CI, 59.90-83.96) and 85.54% (71/83; 95% CI, 76.68-100.00), respectively. Besides, the SVA prevalence in piglet herds was the highest at 71.69% (119/166; 95% CI, 68.61-98.43) (p < 0.05). Moreover, our analysis confirmed that the subgroups, including country, sampling year, sampling position, detected gene, detection method, season, age, and climate, could be the heterogeneous factors associated with SVA prevalence. Conclusions: The results indicated that SVA widely exists in various countries currently. Therefore, more prevention and control policies should be proposed to enhance the management of pig farms and improve breeding conditions and the environment to reduce the spread of SVA.

• Analytical Science and Technology
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• v.21 no.5
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• pp.424-431
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• 2008

Streptomycin, which is one of aminoglycoside antibiotics, has been widely used in the rearing of food-producing animals to prevent and treat diseases in cattle, pigs and poultry. Although not licensed in South Korea, streptomycin has also been used for the treatment of bacterial honeybee disease, such as European foulbrood in Third World countries. A reliable and effective method using liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed for the determination of streptomycin in honey. A established method was optimized the clean-up and extraction procedure for the trace determination, good precision and accuracy. And the chromatographic and tandem mass spectrometric parameters were also optimized. The precision (RSD) and accuracy (bias) in the concentration range of 5.0~50.0 ug/kg were 5.5~14% and -10.0~8.0%, respectively. Limit of detection was 0.75 ug/kg and recovery of streptomycin spiked at level of 10 ug/kg in honey was 74%. The established and validated method was applied to determine streptomycin in honey which was on the market.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.232-239
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• 2024

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

• Journal of Environmental Health Sciences
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• v.44 no.5
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• pp.491-501
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• 2018

Objectives: The aims of this study were to compare concentrations and the correspondence of human blood cadmium by using graphite furnace atomic absorption spectrometry (GF-AAS) and inductively coupled plasma-mass spectrometry (ICP-MS), which are representative methods of heavy metal analysis. Methods: We randomly selected 79 people who agreed to participate in the research project. After confirming the linearity of the calibration curves for GF-AAS and ICP-MS, the concentrations of cadmium in a quality control standard material and blood samples were measured, and the correlation and the degree of agreement were compared. Results: The detection limit of ICP-MS (IDL: $0.000{\mu}g/L$, MDL: $0.06{\mu}g/L$) was lower than that of GF-AAS (IDL: $0.085{\mu}g/L$, MDL: $0.327{\mu}g/L$). The coefficient of variation of the quality control standard material showed stable values for both ICP-MS (clinchek-1: 5.35%, clinchek-2: 6.22%) and GF-AAS (clinchek-1: 7.92%, clinchek-2: 5.22%). Recovery was relatively high for both ICP-MS (clinchek-1: 95.1%, clinchek-2: 92.8%) and GF-AAS (clinchek-1: 91.4%, clinchek-2: 98.8%), with more than 90%. The geometric mean, median, and percentile of blood samples were all similar. The agreement of the two instruments compared with the bias of the analytical values found that about 81% of the analytical values were within ${\pm}30%$ of the deviation from the ideal reference line (y=0). As a result of the agreement limit, the value included in the confidence interval was about 94%, which shows high agreement. Conclusion: In this study, we confirmed there was no significant difference in concentrations of a quality control standard material and blood samples. Since ICP-MS showed lower concentrations than GF-AAS at concentrations below the method detection limit of GF-AAS, it is expected that more precise results will be obtained by analyzing blood cadmium with ICP-MS.

• Journal of the Korean Magnetics Society
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• v.23 no.6
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• pp.200-204
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• 2013

In this study, we have fabricated magnetoresistive sensors of $50{\mu}m{\times}50{\mu}m$ cross type by trilayer structure of antiferromagnetic/nonmagnetic/ferromagnetic. The magnetic signal and magnetic domain of this sensor is measured. The sensor hysteresis loop is not in symmetrical at 0 Oe. This is may be due to the exchange coupling between ferromagnetic layer and anti ferromagnetic layer. This exchange bias value is 20 Oe. The sensor signal is measured at between the applied magnetic field and current. The sensor signal is measured between the applied magnetic field and current at $20^{\circ}$ and $90^{\circ}$ angles. The sensitivity of sensor signals is $20{\mu}V/Oe$ and $7{\mu}V/Oe$ at $20^{\circ}$ and $90^{\circ}$ angles, respectively. In addition, this sensor is also applied for the detection of magnetic bacteria at $20^{\circ}$ angle. From these results, we calculate the stray field of single bacteria is to be $5{\times}10^{-5}$Oe.

• Journal of Plant Biotechnology
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• v.40 no.1
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• pp.18-26
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• 2013

Genetically modified (GM) rice Agb0101, which expresses the insecticidal toxin modified cry1Ac (mcry1Ac1) gene, was developed by the Rural Development Administration in Korea. To monitor the probable release of Agb0101 in the future, it is necessary to develop a reliable detection method. Here, we developed the PCR detection method for monitoring and tracing of GM rice. The primer pair (RBEgh-1/-2) from a starch branching enzyme (RBE4) gene was designed as an endogenous reference, giving rise to an expected PCR amplicon of 101 bp. For the qualitative PCR detection, construct- and event-specific primers were designed on the basis of integration sequence of T-DNA. Event-specific PCRs amplified specifically 5'- or 3'-junction region spanning the native genome DNA and the integrated gene construct, while none of amplified product was shown on crops, rice varieties, and other insect-resistant transgenic rice lines. The event-specific real-time PCR method was performed using TaqMan probe and plasmid pRBECrR containing both rice endogenous gene RBE4 sequence and 5'-junction sequence as the reference molecule. The absolute limit of quantification (LOQ) of real-time PCR was established with around 10 copies for one plasmid molecule pRBECrR. Thereafter, the different amounts of transgenic rice (1, 3, 5, and 10%, respectively) were quantified by using the established real-time PCR method, with a range below 19.55% of the accuracy expressed as bias, 0.06-0.40 of standard deviation (SD) and 3.80-7.01% of relative standard deviations (RSD), respectively. These results indicate that the qualitative and quantitative PCR methods could be used effectively to detect the event Agb0101 in monitoring and traceability.