• Nuclear Engineering and Technology
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• v.43 no.5
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• pp.405-412
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• 2011

Used nuclear fuel will likely be stored dry for extended periods of time in the USA. Until a final disposition pathway is chosen, the storage periods will almost definitely be longer than were originally intended. The ability of the important-tosafety structures, systems, and components (SSCs) to continue to meet storage and transport safety functions over extended times must be determined. It must be assured that there is no significant degradation of the fuel or dry cask storage systems. Also, it is projected that the maximum discharge burnups of the used nuclear fuel will increase. Thus, it is necessary to obtain data on high burnup fuel to demonstrate that the used nuclear fuel remains intact after extended storage. An evaluation was performed to determine the conditions that may lead to failure of dry storage SSCs. This paper documents the initial technical gap analysis performed to identify data and modeling needs to develop the desired technical bases to ensure the safety functions of dry stored fuel.

• Journal of Environmental Science International
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• v.15 no.10
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• pp.935-943
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• 2006

This study focuses on analyzing the inflow characteristic of contaminants of city water that flows into a main water system like a reservoir, and intends to provide basic data which can be efficiently reflected on water quality management policy and decision making of a reservoir. The conclusion obtained from the analysis of the inflow of a main water system by analyzing the inflow property of city water contaminants is as follows. In the case of Chungju-cheon stream which is the city water, pollution load from the basic outflow is low when it rains, and with high load of basic outflow during the dry season, due to the discharge of pollutants from the city, the quality of water becomes worse. In the case of Chungju-cheon stream, average BOD is $4.53mg/{\ell}$ when it rains, and the contaminants increase and flow in about 7.8% compared to the average BOD during the average droughty season. The average SS concentration in water is $798.67mg/{\ell}$ and increased 97.2% compared to the average droughty season.

• Annals of Clinical Neurophysiology
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• v.9 no.2
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• pp.69-74
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• 2007

Electroencephalogram (EEG) is a representative diagnostic tool in epilepsy. However, there are several points of debate on the role of EEG in diagnosis and management of epilepsy. We suggest that EEG has some limitations for differential diagnosis from nonepileptic episodic diseases, classification of epilepsy, prediction of recurrence, and evaluation of treatment response. Interictal EEG cannot diagnose or exclude epilepsy because interictal epileptic discharge (IED) is frequently absent in epilepsy and can appear in nonepileptic conditions. Although EEG is helpful in classification of epilepsy, focal spikes in generalized epilepsy and secondary bilateral synchrony in localization related epilepsy cause interrater disagreement. It is controversial whether EEG predicts recurrence after the first seizure in adults. The predictive value of EEG in antiepileptic drug (AED) withdrawal is not absolute. The prognosis after AED withdrawal depends on epilepsy syndrome. Many studies could not confirm the value of EEG in assessing the treatment response. After all, epilepsy is clinically diagnosed and assessed. Interictal EEG alone does not provide decisive information and routine follow-up of EEG is not recommended.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.264-268
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• 2016

Recently, various energy efficiency optimization activities are ongoing globally by integrating conventional grids with ICT (Information and Communication Technology). In this sense, various smart grid projects, which power suppliers and consumers exchange useful informations bilaterally in real time, have been being carried out. The electric vehicle diffusion program is one of the projects and it has been spotlighted because it could resolve green gas problem, fuel economy and tightening environmental regulations. In this paper, the economics of V2G system which consists of electric vehicles and the charging infrastructure is evaluated comparing electric vehicles for V2G with common electric vehicles. Additional benefits of V2G are analyzed in the viewpoint of load leveling, frequency regulation and operation reserve. To find this benefit, electricity sales is modeled mathematically considering depth of discharge, maximum capacity reduction, etc. Benefit and cost analysis methods with the modeling are proposed to decide whether the introduction of V2G systems. Additionally, the methods will contribute to derive the future production and the unit cost of electric vehicle and battery and to get the technical and economic analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.198-198
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• 2017

Climate change causes unpredictable and erratic climatic patterns which affects crop production in agriculture and threatens public health. To cope with the challenges of climate change, sustainable and sound growth environment for crop production should be secured. Recent attention has been given to the development of smart irrigation system using sensors and wireless network as a solution to achieve water conservation as well as improvement in crop yield and quality with less water and labor. This study developed the smart irrigation technique for farmlands by monitoring the soil moisture contents and real-time climate condition for decision-making support. Central to this design is micro-controller which monitors the farm condition and controls the distribution of water on the farm. In addition, a series of laboratory studies were conducted to determine the optimal irrigation pattern, one time versus plug time. This smart technique allows farmers to reduce water use, improve the efficiency of irrigation systems, produce more yields and better quality of crops, reduce fertilizer and pesticide application, improve crop uniformity, and prevent soil erosion which eventually reduce the nonpoint source pollution discharge into aquatic-environment.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.215-219
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• 2022

Bankruptcy is one of the major areas that have attracted the interest of many researchers in the American system, particularly in terms of the laws that oversee it. It provides a plan of reorganization that enables the debtor or the proprietor to discharge liabilities to the creditors through dividing the assets to settle debts. This activity is carried out under supervision to fairly protect the interests of the creditors. Bankruptcy protection systems are dynamic and complex in nature, in line with the economic sector, ensuring the protection of affected individuals from falling into huge losses. Some bankruptcy procedures give the debtor the opportunity to stay in operation or business activity and benefit from revenues until the debt is settled. This law allows some debtors to be relived from any financial burden after the distribution of assets, even if the debt is not paid in full. In light of the above information, this research paper seeks to explore the nature of the complexity of bankruptcy protection laws, their characteristics, and the justice system that regulate them. It also sheds more light on the decision-making powers on bankruptcy cases. There are specialized courts that cover bankruptcy cases located in district courts in every state.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.22-34
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• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• 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.