• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.145-154
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• 2012

Recently, the cardiovascular disease has increased by causes such as westernization dietary life, smoking, and obesity. In particular, the acute myocardial infarction (AMI) occupies 50% death rate in cardiovascular disease. Following this trend, the AMI has been carried out a research for discovery of risk factors based on national data. However, there is a lack of diagnosis minor suitable for Korean. The objective of this paper is to develop a classifier for short-term relapse mortality prediction of cardiovascular disease patient based on prognosis data which is supported by KAMIR(Korea Acute Myocardial Infarction). Through this study, we came to a conclusion that ANN is the most suitable method for predicting the short-term relapse mortality of patients who have ST-segment elevation myocardial infarction. Also, data set obtained by logistic regression analysis performed highly efficient performance than existing data set. So, it is expect to contribute to prognosis estimation through proper classification of high-risk patients.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.68-77
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• 2008

We have developed an inversion algorithm for loop-loop electromagnetic (EM) data, based on the localised non-linear or extended Born approximation to the solution of the 2.5D integral equation describing an EM scattering problem. Source and receiver configuration may be horizontal co-planar (HCP) or vertical co-planar (VCP). Both multi-frequency and multi-separation data can be incorporated. Our inversion code runs on a PC platform without heavy computational load. For the sake of stable and high-resolution performance of the inversion, we implemented an algorithm determining an optimum spatially varying Lagrangian multiplier as a function of sensitivity distribution, through parameter resolution matrix and Backus-Gilbert spread function analysis. Considering that the different source-receiver orientation characteristics cause inconsistent sensitivities to the resistivity structure in simultaneous inversion of HCP and VCP data, which affects the stability and resolution of the inversion result, we adapted a weighting scheme based on the variances of misfits between the measured and calculated datasets. The accuracy of the modelling code that we have developed has been proven over the frequency, conductivity, and geometric ranges typically used in a loop-loop EM system through comparison with 2.5D finite-element modelling results. We first applied the inversion to synthetic data, from a model with resistive as well as conductive inhomogeneities embedded in a homogeneous half-space, to validate its performance. Applying the inversion to field data and comparing the result with that of dc resistivity data, we conclude that the newly developed algorithm provides a reasonable image of the subsurface.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.69-78
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• 2004

In this study, the back analysis program was developed by applying the genetic algorithm, one of artificial intelligence fields, to the direct method. The optimization process which has influence on the efficiency of the direct method was modulated with genetic algorithm. On conditions that the displacement computed by forward analysis for a certain rock mass model was the same as the displacement measured at the tunnel section, back analysis was executed to verify the validity of the program. Usefulness of the program was confirmed by comparing relative errors calculated by back analysis, which is carried out under the same rock mass conditions as analysis model of Gens et at (1987), one of back analysis case in the past. We estimated the total displacement occurring by tunnelling with the crown settlement and convergence measured at the working faces in three tunnel sites of Kyungbu Express railway. Those data measured at the working face are used for back analysis as the input data after confidence test. As the results of the back analysis, we comprehended the tendency of tunnel behaviors with comparing the respective deformation characteristics obtained by the measurement at the working face and by back analysis. Also the usefulness and applicability of the back analysis program developed in this study were verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.817-822
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• 2017

The braking system is one of the most important components in vehicles and machines. It must exert a reliable braking force when they are brought to a halt. Generally, frictional heat is generated by converting kinetic energy into heat energy through friction. As the kinetic energy is converted into heat energy, high temperature heat is generated which affects the mechanical behavior of the braking system. Frictional heat affects the thermal expansion and friction coefficient of the brake system. If the temperature is not controlled, the brake performance will be decreased. Therefore, it is important to predict and control the heat generation of the brake. Various numerical analysis studies have been carried out to predict the frictional heat, but they assumed the existence of boundary conditions in the numerical analysis to simulate the frictional heat, because the simulation of frictional heat is difficult and time consuming. The results were based on the assumption that the frictional heat is different from the actual temperature distribution in a rotating brake system. Therefore, the reliability of the cooling effect or thermal stress using the results of these studies is insufficient. In order to overcome these limitations and establish a simulation procedure to predict the frictional heat, this study directly simulates the frictional heat generation by using a thermal-structure coupling element. In this study, we analyzed the thermo-mechanical behavior of a brake model, in order to investigate the thermal characteristics of brake systems by using the Finite Element method (FEM). This study suggests the necessity to directly simulate the frictional heating and it is hoped that it can provide the necessary information for simulations.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.684-690
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• 2010

Recently, the NG(Next Generation) system is studied for supporting convergence of various services and multi mode of single terminal. And a demand of user for taking the various services is getting increased, for supporting these services, many systems being able to transmit a large message have been appeared. In the NG system, it has to be supporting the CDMA and WCDMA besides the tele communication systems using OFDM method with single terminal An intergrated system can be improved with adopting of SoC technique. For adopting SoC technique on the intergrated terminal, we have to solve the non linear problem of HPA(High Power Amplifier). Nonlinear characteristic of HPA distorts both amplitude and phase of transmit signal, this distortion cause deep adjacent channel interference. We adopt a polynomial pre-distortion technique for this problem. In this paper, a noble modem design for NG mobile communication service and a method using polynomial pre-distorter with PAPR technique for counterbalancing nonlinear characteristic of the HPA are proposed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.3
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• pp.127-138
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• 2016

Diesel vehicles should be equipped with urea-selective catalytic reduction(SCR) system as a high-performance catalyst, in order to reduce harmful nitrogen oxide emissions. In this study, a three-dimensional Eulerian-Lagrangian CFD analysis was used to numerically predict the multiphase flow characteristics of the urea-SCR system, coupled with the chemical reactions of the system's transport phenomena. Then, the numerical spray structure was modified by comparing the results with the measured values from spray visualization, such as the injection velocity, penentration length, spray radius, and sauter mean diameter. In addition, the analysis results were verified by comparison with the removal efficiency of the nitrogen oxide emissions during engine and chassis tests, resulting in accuracy of the relative error of less than 5%. Finally, a verified CFD analysis was used to calculate the interanl flow of the urea-SCR system, thereby analyzing the characteristics of pressure drop and velocity increase, and predicting the uniformity index and overdistribution positions of ammonia.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.361-372
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• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.629-635
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• 2019

In this study, we analyzed the effect of FAN and oil cooler application on APU. MIL-STD-810 was applied to the atmospheric environment and radiation dose in order to perform thermal flow analysis. The heat flow was analyzed for the case in which the inlet / outlet fan was applied (Case 1), the case in which the inlet fan and the oil cooler were applied (Case 2), and the case in which the inlet / outlet fan and the oil cooler were applied (Case 3). As a result, it was confirmed that the cylinder head temperature of Case 3 was 21.4 times lower than that of Case 1 and 8.0 times lower than that of Case 2. Experiments were conducted under the same ambient conditions in order to examine the validity of the results. The numerical values and experimental results showed a difference of less than 7%. Through this, we were able to confirm that the APU heat flow optimization model satisfies the design conditions. The results of this study are expected to be used as basic data for optimizing heat flow of APU.