• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.55-56
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• 1994

KEPCO is now driving forward the raising of transmission-line-voltage from 345kV to 765kV for the stabilization of network and as the counter measure of the rapid-increasing of domestic electric power demands. 800kV GIS is one of the most important apparatuses in the 765kV substation and we are developing the Model $SF_6$ GCB for the GIS now. We'll explain the present condition of development of 800kV $SF_6$ GCB until now.

• Journal of the Korean Geotechnical Society
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• v.30 no.5
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• pp.47-54
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• 2014

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.94-106
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• 2018

The epidemiological model is useful for creating simulation and associated preventive measures for disease spread, and provides a detailed understanding of the spread of disease space through contact with individuals. In this study, propose an agent-based spatial model(ABM) integrated with spatial big data to simulate the spread of MERS-CoV infections in real time as a result of the interaction between individuals in space. The model described direct contact between individuals and hospitals, taking into account three factors : population, time, and space. The dynamic relationship of the population was based on the MERS-CoV case in Seoul Metropolitan Government in 2015. The model was used to predict the occurrence of MERS, compare the actual spread of MERS with the results of this model by time series, and verify the validity of the model by applying various scenarios. Testing various preventive measures using the measures proposed to select a quarantine strategy in the event of MERS-CoV outbreaks is expected to play an important role in controlling the spread of MERS-CoV.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.467-474
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• 2019

In this paper, we propose a novel method to improve FPS while maintaining the accuracy of YOLO v2 model in NVIDIA Jetson TX1. In general, in order to reduce the amount of computation, a conversion to an integer operation or reducing the depth of a network have been used. However, the accuracy of recognition can be deteriorated. So, we use methods to reduce computation and memory consumption through adjustment of the filter size and integrated computation of the network The first method is to replace the $3{\times}3$ filter with a $1{\times}1$ filter, which reduces the number of parameters to one-ninth. The second method is to reduce the amount of computation through CBR (Convolution-Add Bias-Relu) among the inference acceleration functions of TensorRT, and the last method is to reduce memory consumption by integrating repeated layers using TensorRT. For the simulation results, although the accuracy is decreased by 1% compared to the existing YOLO v2 model, the FPS has been improved from the existing 3.9 FPS to 11 FPS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.41-46
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• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.

• Journal of the Korean Geotechnical Society
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• v.26 no.9
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• pp.37-45
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• 2010

Sorption characteristics of arsenic on furnace slag were investigated to remove arsenic from groundwater using furnace slag, which is industrial waste generated from steel company. Adsorption isotherm experiments and kinetic sorption experiments were performed and the chemical characteristics of supernatants from these experiments were analyzed. Results showed that all supernatants were alkaline (above pH 9) and the highest ion concentration in the solution was found with calcium (30~50 mg/L). Results of adsorption isotherms were more adequately described by the Freundlich model than Langmuir model. From adsorption isotherms experiments, it was noted that the adsorption amount of As(V) was 87% higher than that of As(III). Results of kinetic sorption experiments were more properly fitted by pseudo second order (PSO) model than pseudo first order model. Equilibrium adsorption amount ($q_e$) and relaxation time ($t_r$) calculated from PSO model increased with initial concentration of arsenic. Equilibrium adsorption amount of As(V) was higher than that of As(III) and relaxation time of As(V) was shorter than that of As(III). Adsorption isotherm results could be predicted by kinetic adsorption results, since equilibrium adsorption amount calculated through PSO model generally agreed with equilibrium adsorption amount measured from adsorption isotherm.

• Journal of computational fluids engineering
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• v.10 no.4 s.31
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• pp.12-17
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• 2005

A computational study of evaluation of current turbulence models is performed for a better prediction of thermal stratification in an upper plenum of a liquid metal reactor. The turbulence models tested in the present study are the two-layer model, the shear stress transport (SST) model, the v2-f model and the elliptic blending mode(EBM). The performances of the turbulence models are evaluated by applying them to the thermal stratification experiment conducted at JNC (Japan Nuclear Corporation). The algebraic flux model is used for treating the turbulent heat flux for the two-layer model and the SST model, and there exist little differences between the two turbulence models in predicting the temporal variation of temperature. The v2-f model and the elliptic blending model better predict the steep gradient of temperature at the interface of thermal stratification, and the v2-f model and elliptic blending model predict properly the oscillation of the ensemble-averaged temperature. In general the overall performance of the elliptic blending model is better than the v2-f model in the prediction of the amplitude and frequency of the temperature oscillation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.10-18
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• 2012

This paper develops a decentralized advanced traveler information system (ATIS) under the connected vehicle environment, recently regarded as one of most promising tools in Intelligent Transportation Systems (ITS). The performance of the proposed ATIS is reinforced by introducing autonomous automatic incident detection (AAID) function. The proposed ATIS is implemented and tested using an off-the-shelf microscopic simulation model (VISSIM) on a simple traffic network under idealized communication conditions. A key attribute of this experiment is the inclusion of a non-recurrent traffic state (i.e., traffic incident). Simulation results indicate that the ATIS using V2V communication is efficient in saving drivers' travel time and AAID plays an important role in improving the effectiveness of the system.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.41-51
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• 2010

A control system has been being developed by korean engineers for 500MW korean standard type fossil power plant with the advent of retrofit of old control system. Simulators have been used for digital I&C system pre-tests and validation tests in nuclear power plants. In this paper, the power plant simulator for control system V&V was developed in order to verify the developed control system prior to application to a power plant. The control models were developed using plant control system data, translator programs, and vendor manuals. The developed simulator was verified by steady-state test, load swing test, transient test and so on.

• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.349-354
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• 2004

Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.