• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.1-9
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• 2024

In this paper, we propose BRAIDS, a boot storm mitigation plan consisting of an AI-based VDI usage prediction system and a virtual machine boot scheduler system, to alleviate boot storms and improve service stability. Virtual Desktop Infrastructure (VDI) is an important technology for improving an organization's work productivity and increasing IT infrastructure efficiency. Boot storms that occur when multiple virtual desktops boot simultaneously cause poor performance and increased latency. Using the xgboost algorithm, existing VDI usage data is used to predict future VDI usage. In addition, it receives the predicted usage as input, defines a boot storm considering the hardware specifications of the VDI server and virtual machine, and provides a schedule to sequentially boot virtual machines to alleviate boot storms. Through the case study, the VDI usage prediction model showed high prediction accuracy and performance improvement, and it was confirmed that the boot storm phenomenon in the virtual desktop environment can be alleviated and IT infrastructure can be utilized efficiently through the virtual machine boot scheduler.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.178-183
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• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.76-83
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• 2016

In recent, the series of extreme storm events were occurred by those continuous typhoons and the severe flood damages due to the loss of life and the destruction of property were involved. In this study, we call Mega flood for the Extreme flood occurred by these successive storm events and so we can have a hypothetical Mega flood by assuming that a extreme event can be successively occurred with a certain time interval. Inter Event Time Definition (IETD) method was used to determine the time interval between continuous events in order to simulate Mega flood. Therefore, the continuous extreme rainfall events are determined with IETD then Mega flood is simulated by the consecutive events : (1) consecutive occurrence of two historical extreme events, (2) consecutive occurrence of two design events obtained by the frequency analysis based on the historical data. We have shown that Mega floods by continuous extreme rainfall events were increased by 6-17% when we compared to typical flood by a single event. We can expect that flood damage caused by Mega flood leads to much greater than damage driven by a single rainfall event. The second increase in the flood caused by heavy rain is not much compared to the first flood caused by heavy rain. But Continuous heavy rain brings the two times of flood damage. Therefore, flood damage caused by the virtual Mega flood of is judged to be very large. Here we used the hypothetical rainfall events which can occur Mega floods and this could be used for preparing for unexpected flood disaster by simulating Mega floods defined in this study.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1034-1046
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• 2011

In this paper, a design and fabrication of 40 W power amplifier for the X-band using load-pull measurement of GaN HEMT chip are presented. The adopted active device for power amplifier is GaN HEMT chip of TriQuint company, which is recently released. Pre-matched fixtures are designed in test jig, because the impedance range of load-pull tuner is limited at measuring frequency. Essentially required 2-port S-parameters of the fixtures for extraction optimal input and output impedances is obtained by the presented newly method. The method is verified in comparison of the extracted optimal impedances with data sheet. The impedance matching circuit for power amplifier is designed based on EM co-simulation using the optimal impedances. The fabricated power amplifier with 15${\times}$17.8 $mm^2$ shows the efficiency above 35 %, the power gain of 8.7~8.3 dB and the output power of 46.7~46.3 dBm at 9~9.5 GHz with pulsed-driving width of 10 usec and duty of 10 %.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.4
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• pp.63-70
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• 2011

In this paper, we proposed a method for estimating energy expenditure using the unfixed axis of the accelerometer. Most studies adopted waist-placement because of the fact that the waist is close to the center of mass of a whole human body. But we adopted pocket-placement, which is capable of using unfixed axis of sensor, that is more convenient than conventional methods. To evaluate the proposed method, 28 male subjects performed walking and running on a motor driven treadmill. All of subject put on the indirect calorimeter and fixed accelerometer, then data were simultaneously measured during exercise. The regression analysis was performed using the test group(n=20) and the regression equation was applied to the control group(n=8). A strong linear relationship between energy expenditure and unfixed accelerometer signal was found. Futhermore, the coefficient of determination was significantly reliable($R^2$=0.98) and showed zero of p-value. The error of energy expenditure estimation between indirect calorimeter and two types of accelerometer was 15.0%(fixed) and 17.0%(unfixed) respectively. These results show the possibilities that the unfixed accelerometer can be used in estimating the energy expenditure during exercise.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.531-539
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• 2011

It has been widely acknowledged that climate system associated with extreme rainfall events was difficult to understand and extreme rainfall simulation in climate model was more difficult. This study developed a new model for extracting rainfall filed associated with extreme events as a way to characterize large scale climate system. Main interests are to derive location, size and direction of the rainfall field and this study developed an algorithm to extract the above characteristics from global climate data set. This study mainly utilized specific humidity and wind vectors driven by NCEP reanalysis data to define the rainfall field. Geometric first and second moments have been extensively employed in defining the rainfall field in selected zone, and an ellipsoid based model were finally introduced. The proposed geometric moments based ellipsoid model works equally well with regularly and irregularly distributed synthetic grid data. Finally, the proposed model was applied to space-time real rainfall filed. It was found that location, size and direction of the rainfall field was successfully extracted.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.275-278
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• 2006

Google Earth enables people to easily find information linked to geographical locations. Google Earth consists of a collection of zoomable satellite images laid over a 3-D Earth model and any geographically referenced information can be uploaded to the Web and then downloaded directly into Google Earth. This can be achieved by encoding in Google's open file format, KML (Keyhole Markup Language), where it is visible as a new layer superimposed on the satellite images. We used KML to create and share fine resolution gridded temperature data projected to 3 climatological normal years between 2011-2100 to visualize the site-specific warming and the resultant earlier blooming of spring flowers over the Korean Peninsula. Gridded temperature and phonology data were initially prepared in ArcGIS GRID format and converted to image files (.png), which can be loaded as new layers on Google Earth. We used a high resolution LCD monitor with a 2,560 by 1,600 resolution driven by a dual link DVI card to facilitate visual effects during the demonstration.