• Journal of Wetlands Research
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• v.13 no.3
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• pp.623-631
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• 2011

In recent decade, the occurrences of typhoon and severe storm events are increasing trend due to the climate change. And the intensity of natural disaster is more and more stronger and the loss of life and damage of property are also increasing. Therefore, this study tried to understand the impact of climate change on urban drainage system for prevention and control of natural disaster and for this, we selected Gyeyang-gu, Incheon city as a study area. We investigated the climate models and scenarios for the selection of proper model and scenario, then we estimated frequency based rainfall in hourly unit considering climate change. The XP-SWMM model was used to estimate the future flood discharge on urban drainage system using the estimated frequency based rainfall. As a result, we have known that the study area will be overflown in the future and so we may need prepare proper measures for the flood prevention and control.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.23-32
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• 2024

NVS (Novel View Synthesis) is a field in computer vision that reconstructs new views of a scene from a set of input views. Real-time rendering and high performance are essential for NVS technology to be effectively utilized in various applications. Recently, 3D-GS (3D Gaussian Splatting) has gained popularity due to its faster training and inference times compared to those of NeRF (Neural Radiance Fields)-based methodologies. However, since 3D-GS reconstructs a 3D (Three-Dimensional) scene by splitting and cloning (Density Control) Gaussian points, the number of Gaussian points continuously increases, causing the model to become heavier as training progresses. To address this issue, we propose two methodologies: 1) Gaussian blending, an improved density control methodology that removes unnecessary Gaussian points, and 2) a performance enhancement methodology using a depth estimation model to minimize the loss in representation caused by the blending of Gaussian points. Experiments on the Tanks and Temples Dataset show that the proposed methodologies reduce the number of Gaussian points by up to 4% while maintaining performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.670-683
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• 2016

Network coding, as a new technique to improve the throughput, is studied combined with multi-relay model in this paper to address the challenges of long distance and power limit in deep space communication. First, an amplify-and-forward relaying approach based on analog network coding (AFNC) is proposed in multi-relay network to improve the capacity for deep space communication system, where multiple relays are introduced to overcome the long distance link loss. The design of amplification coefficients is mathematically formulated as the optimization problem of maximizing SNR under sum-power constraint over relays. Then for a dual-hop relay network with a single source, the optimal amplification coefficients are derived when the multiple relays introduce non-coherent noise. Through theoretic analysis and simulation, it is shown that our approach can achieve the maximum transmission rate and perform better over single link transmission for deep space communication.

• Journal of Life Science
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• v.9 no.3
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• pp.241-247
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• 1999

Antibiotics have been routinely used to control the disease of farm-raised animals in the aquaculture facilities without any criterion based on a pharmacokinetic study. This lack of information on the effective usage of antibiotics would have brought the farmers to use excessive and/or less dosages, causing the advent of drug-resistant bacteria as well as economic loss and possible contamination of the local farming area. Until recently, few studies on a detailed manual for the antibiotic usage including chemotherapy procedure, dosage, and treatment schedule of the aquatic antibiotics have been conducted throughout the world. To the worse, there is no available criterion for optimal usage of aquatic antibiotics to control diseases in aquatic farms in this country because every country has its own aquacultural system. Therefore, based on the previous studies on the usage of the various antibiotics, our studies are to focus on the development of optimal method for the detection of various antibiotics on the fate of antibiotics applied to the fish, including absorption, circulation, and secretion physiology. Pharmacokinetic study were to sep up the optimal detective condition against residual antibiotics of fish by HPLC. The grinding pefloxacin for 15 min is most effective in dissolution test and pharmacokinetic parameters. Pharmacokinetic parameters were satisfactory for 15 min-grinding products and they can be explained as one-compartment model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1295-1299
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• 2008

Soil erosion is a natural process and has been occurring in most areas in the watershed. However, accelerated soil erosion rates have been causing numerous environmental impacts in recent years. To reduce soil erosion and sediment inflow into the water bodies, site-specific soil erosion best management practices (BMPs) need to be established and implemented. The most commonly used soil erosion model is the Universal Soil Loss Equation (USLE), which have been used in many countries over 30 years. The Sediment Assessment Tool for Effective Erosion Control (SATEEC) ArcView GIS system has been developed and enhanced to estimate the soil erosion and sediment yield from the watershed using the USLE input data. In the last decade, the Soil and Water Assessment Tool (SWAT) model also has been widely used to estimate soil erosion and sediment yield at a watershed scale. The SATEEC system estimates the LS factor using the equation suggested by Moore and Burch, while the SWAT model estimates the LS factor based on the relationship between sub watershed average slope and slope length. Thus the SATEEC and SWAT estimated soil erosion values were compared in this study. The differences in LS factor estimation methods in the SATEEC and SWAT caused significant difference in estimated soil erosion. In this study, the difference was -51.9%(default threshold)$\sim$-54.5%(min. threshold) between SATEEC and non-patched SWAT, and -7.8%(default threshold)$\sim$+3.8%(min. threshold) between SATEEC and patched SWAT estimated soil erosion.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.1
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• pp.3-12
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• 2008

Soil erosion is a natural process and has been occurring in most areas in the watershed. However, accelerated soil erosion rates have been causing numerous environmental impacts in recent years. To reduce soil erosion and sediment inflow into the water bodies, site-specific soil erosion best management practices(BMPs) need to be established and implemented. The most commonly used soil erosion model is the Universal Soil Loss Equation(USLE), which have been used in many countries over 30 years. The Sediment Assessment Tool for Effective Erosion Control(SATEEC) ArcView GIS system has been developed and enhanced to estimate the soil erosion and sediment yield trom the watershed using the USLE input data. In the last decade, the Soil and Water Assessment Tool(SWAT) model also has been widely used to estimate soil erosion and sediment yield at a watershed scale. The SATEEC system estimates the LS factor using the equation suggested by Moore and Burch, while the SWAT model estimates the LS factor based on the relationship between sub watershed average slope and slope length. Thus the SATEEC and SWAT estimated soil erosion values were compared in this study. The differences in LS factor estimation methods in the SATEEC and SWAT caused significant difference in estimated soil erosion. In this study, the difference was -51.9%(default threshold)${\sim}-54.5%$(min. threshold) between SATEEC and non-patched SWAT, and -7.8%(default threshold)${\sim}+3.8%$(min. threshold) between SATEEC and patched SWAT estimated soil erosion.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.361-375
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• 2013

While the mobile application market is growing fast, the revenues of the majority apps are declining. Moreover, paid mobile application prices and purchases have been slow because of intensifying competition. However, the conventional studies focused only on the acceptance factor of mobile applications, so they cannot explain the phenomenon such as acceptance resistance for paid mobile applications. Therefore, our study tries to analyze the factors for the acceptance resistance of users for paid mobile applications. The research model in this paper, which is based on S-O-R model, verified through surveys how social influence and app characteristics affect user perception and how user perception affects to app resistance. The results of our study showed that paid mobile applications happened to be destroyed in front of the chasm because of the perceived loss. Consequently, the results implicate that the developers should lower the initial price and actively react to the negative reviews in order to lower the perceived loss. Moreover, the results verified that a sense of self-efficacy can lower application acceptance resistance by including personal properties to our research model as control variables.

• Journal of Wetlands Research
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• v.23 no.2
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• pp.173-182
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• 2021

It is important to extract and assess low-flow recession characteristics for water resources management in the upper reaches of a stream. It is difficult to express the groundwater flow recession characteristics for streamflow synthetically. The linear recession model has been widely used by baseflow recession analysis for reason of simplicity and convenience, but recent studies show that nonlinear recession models fit well, and the relationship between the reservoir storage of shallow unconfined aquifers and the groundwater discharge was to be identified as nonlinear in the literature based on the analysis of numerous streamflow recession curves. The objective of the study is to decode these nonlinear characteristics, including evaporation loss, storage, and recharge of groundwater using streamflow. By analyzing the observed time series of streamflow from the study area, which is the Pyeongchang River basin in Korea, the main components of the underlying groundwater balance, namely, discharge, evaporation loss, storage, and recharge, can be identified and quantified. As a result of the study, depletion of groundwater by evapotranspiration losses through the water uptake of tree roots was found to bias the recession curves and the estimated reservoir parameters. The seasonality of both rainfall and potential evaporation, analysis of the recession curves, stratified according to time of the year, allowed the quantification of evapotranspiration loss as a function of a calendar month and stored groundwater storage.