• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Journal of Ginseng Research
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• v.43 no.1
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• pp.27-37
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• 2019

Background: The structural conversions in ginsenosides induced by steaming or heating or acidic condition could improve red ginseng bioactivities significantly. In this paper, the chemical transformations of red American ginseng from fresh Panax quinquefolium L. under steaming were investigated, and the possible mechanisms were discussed. Methods: A method with reversed-phase high-performance liquid chromatography coupled with linear ion trap mass spectrometry ($HPLC-MS^n$)-equipped electrospray ionization ion source was developed for structural analysis and quantitation of ginsenosides in dried and red American ginseng. Results: In total, 59 ginsenosides of protopanaxadiol, protopanaxatriol, oleanane, and ocotillol types were identified in American ginseng before and after steaming process by matching the molecular weight and/or comparing $MS^n$ fragmentation with that of standards and/or known published compounds, and some of them were determined to be disappeared or newly generated under different steaming time and temperature. The specific fragments of each aglycone-type ginsenosides were determined as well as aglycone hydrated and dehydrated ones. The mechanisms were deduced as hydrolysis, hydration, dehydration, and isomerization of neutral and acidic ginsenosides. Furthermore, the relative peak areas of detected compounds were calculated based on peak areas ratio. Conclusion: The multicomponent assessment of American ginseng was conducted by $HPLC-MS^n$. The result is expected to provide possibility for holistic evaluation of the processing procedures of red American ginseng and a scientific basis for the usage of American ginseng in prescription.

• Korean Chemical Engineering Research
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• v.59 no.1
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• pp.6-10
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• 2021

The shorting resistance (SR) of the PEMFC(Proton Exchange Membrane Fuel Cell) polymer membrane is an important indicator of the durability of the membrane. When SR decreases, shorting current (SC) increases, reducing durability and performance. When SR becomes less than about 0.1 kΩ·㎠, shorting occurs, the temperature rises rapidly, and MEA(Membrane Electrode Assembly) is burned to end stack operation. In order to prevent shorting, we need to control the SR, so the conditions affecting the SR were studied. There were differences in the SR measurement methods, and the SR measurement method, which improved the DOE(Department of Energy) and NEDO(New Energy and Industrial Technology Development Organization) method, was presented. It was confirmed that the SR decreases as the relative humidity, temperature and cell compression pressure increase. In the final stage of the accelerated durability evaluation process of the polymer membrane, SR rapidly decreased to less than 0.1 kΩ·㎠, and the hydrogen permeability became higher than 15 mA/㎠. After dismantling the MEA, SEM(Scanning Electron Microscope) analysis showed that a lot of platinum was distributed inside the membrane.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.51-60
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• 2021

Effective nonpoint source (NPS) pollution management requires frequent water quality monitoring, which is, however, often costly to be implemented in practice. Statistical techniques and machine learning methods allow us to identify and focus on fundamental environmental variables that have close relationships with NPS pollutants of interest. This study developed surrogate models to predict the concentrations of suspended sediment (SS) and total phosphorus (T-P) from turbidity and runoff discharge rates using multiple linear regression (MLR) and random forest (RF) methods. The RF models provided acceptable performance in predicting SS and T-P, especially when runoff discharge rates were high. The RF models outperformed the MLR models in all the cases. Such finding highlights the potential of RF techniques and models as a tool to identify fundamental environmental variables that are measured in relatively inexpensive ways or freely available but still able to provide information required to quantify the concentrations of NP S pollutants. The analysis of relative importance rates showed that the temporal variations of SS and T-P concentrations could be more effectively explained by that of turbidity than runoff discharge rate. This study demonstrated that the advanced statistical techniques such as machine learning could help to improve the efficiency of NPS pollutants monitoring.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.811-830
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• 2022

Purpose: In this paper, we will discuss 1) prioritizing databases to be integrated; 2) which data elements should be emphasized in federated database integration; and 3) the degree of efficiency in the integration. This paper aims to lay the groundwork for building data governance by presenting guidelines for database integration using metrics to identify and evaluate the capabilities of the UK's air quality databases. Methods: This paper intends to perform relative efficiency analysis using Data Envelope Analysis among the multi-criteria decision-making methods. In federated database integration, it is important to identify databases with high integration efficiency when prioritizing databases to be integrated. Results: The outcome of this paper aims not to present performance indicators for the implementation and evaluation of data governance, but rather to discuss what criteria should be used when performing 'federated integration'. Using Data Envelope Analysis in the process of implementing intelligent data governance, authors will establish and present practical strategies to discover databases with high integration efficiency. Conclusion: Through this study, it was possible to establish internal guidelines from an integrated point of view of data governance. The flexiblity of the federated database integration under the practice of the data governance, makes it possible to integrate databases quickly, easily, and effectively. By utilizing the guidelines presented in this study, authors anticipate that the process of integrating multiple databases, including the air quality databases, will evolve into the intelligent data governance based on the federated database integration when establishing the data governance practice in the railway industry.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.11-14
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• 2022

In this work, we evaluated the Al₂O₃ film, which was deposited by atomic layer deposition, degraded by exposure to harsh environments. The Al₂O₃ films deposited by atomic layer deposition have long been used as a gas diffusion barrier that satisfies barrier requirements for device reliability. To investigate the barrier and mechanical performance of the Al₂O₃ film with increasing temperature and relative humidity, the properties of the degraded Al₂O₃ film exposed to the harsh environment were evaluated using electrical calcium test and tensile test. As a result, the water vapor transmission rate of Al₂O₃ films stored in harsh environments has fallen to a level that is difficult to utilize as a barrier film. Through water vapor transmission rate measurements, it can be seen that the water vapor transmission rate changes can be significant, and the environment-induced degradation is fatal to the Al₂O₃ thin films. In addition, the surface roughness and porosity of the degraded Al₂O₃ are significantly increased as the environment becomes severer. the degradation of elongation is caused by the stress concentration at valleys of rough surface and pores generated by the harsh environment. Becaused the harsh envronment-induced degradation convert amorphous Al₂O₃ to crystalline structure, these encapsulation properties of the Al₂O₃ film was easily degraded.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.122-129
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• 2024

Though programming education has been stressed recently, the elementary, middle, and high school students are having trouble in programming education. Most programming environments for them are based on block coding, which hinders them from moving to text coding. The traditional PC environment has also troubles such as maintenance problems. In this situation, mobile applications can be considered as alternative programming environments. This paper addresses the design and implementation of coding applications for mobile devices. As a prototype, a Scheme interpreter mobile app is proposed, where Scheme is used for programming courses at MIT since it supports multi-paradigm programming. The implementation has the advantage of not consuming the network bandwidth since it is designed as a standalone application. According to the benchmark result, the execution time on Android devices, relative to that on a desktop, was 131% for the Derivative and 157% for the Tak. Further, the maximum execution times for the benchmark programs on the Android device were 19.8ms for the Derivative and 131.15ms for the Tak benchmark. This confirms that when selecting an Android device for programming education purposes, there are no significant constraints for training.