• The Journal of Korean Obstetrics and Gynecology
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• v.24 no.4
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• pp.186-193
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• 2011

Purpose: This study was performed to find correlations between Edema Index and Heart Rate Variability(HRV) conducted in early postpartum period. Methods: 17 postpartum women were participated in this study which was conducted with Inbody 720(Biospace Co. Ltd.) and SA-6000(Medicore Co. Ltd.). Edema Index(Extra Cellular Water/Total Body Water) was calculated after body composition analysis and HRV result(TP, VLF, LF, HF, HF norm, LF norm, LF/HF ratio) was analysed within a week after delivery. Results: There was no significant correlations between Edema Index and HRV results in time domain analysis. also Edema Index and TP, LF, HF, LF/HF ratio have no significant correlations in frequency domain analysis. but only VLF data has significance compared with Edema Index. Conclusions: It can be suggested that VLF results analysed by HRV can be useful reference estimating postpartum edema especially occurs in early postpartum period.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.1
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• pp.19-30
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• 2019

In this paper, we develop an accurate explicit finite difference method for the two-dimensional Black-Scholes equation with a hybrid boundary condition. In general, the correlation term in multi-asset options is problematic in numerical treatments partially due to cross derivatives and numerical boundary conditions at the far field domain corners. In the proposed hybrid boundary condition, we use a linear boundary condition at the boundaries where at least one asset is zero. After updating the numerical solution by one time step, we reduce the computational domain so that we do not need boundary conditions. To demonstrate the accuracy and efficiency of the proposed algorithm, we calculate option prices and their Greeks for the two-asset European call and cash-or-nothing options. Computational results show that the proposed method is accurate and is very useful for nonlinear boundary conditions.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.70-71
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• 2021

The unit quantity that affects the workability, shrinkage cracking, and durability of concrete is an important factor. Methods for measuring the unit quantity include a high frequency heating method, a capacitance method, a unit volume mass method, and a simple method. However, these methods have the disadvantage of poor measurement method, time required, and precision. To solve this problem, a relatively simple and fast measurement method was adopted to compensate for the shortcomings through a Frequency Domain Reflection (FDR) sensor, and the unit quantity was used. In addition, the measurement data was analyzed by deep learning to evaluate the unit quantity of concrete.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.86-94
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• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

• The Journal of Korean Medicine
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• v.22 no.4
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• pp.131-141
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• 2001

Objectives : Asthma is a chronic inflammatory disorder in which the airway smooth muscle undergoes exaggerated constriction and is abnormally responsive to external stimuli and clinically manifests dyspnea, cough, and wheezing. Recently in Korea, the number of asthma patients has tended to increase as air pollution increases and new allergens appear. This study aimed to identify the therapeutic effects of Sochongryong-tang, which has been used on asthmatic patients in Oriental Medicine for a long time. Methods : We studied 10 asthmatic patients who had visited the Division of Respiratory System, Department of Internal Medicine of Kyunghee Oriental Medical Center from February 1, 2001 to June 30, 2001, which examined the patients by the pulmonary function test (PFT) and Quality of Life Questionnaire for adult Korean Asthmatics (QLQAKA) before and after taking Sochongryong-tang for two weeks. The data was analyzed using paired t-test. Results : After treatment with Sochongryong-tang for two weeks, FEV 1.0 showed a significant increase of 14.2%, FVC a significant increase of 9.3% and PEFR a significant increase of 16.2% in results of patients examined asthmatic by PFT. In the results of QLQAKA, the mean of scores increased significantly over 0.5 points in total score, symptom domain, emotion domain, and environmental domain. Conclusions : This study shows that Sochongryong-tang has the effect of improvements of pulmonary function and quality of life in asthmatic patients.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.317-331
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• 2012

In this paper, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel non-negative matrix factorization (NMF) method. The NMF algorithm is a famous algorithm which is used to solve the source separation problems. In this paper, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010) for evaluation.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.227-234
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• 2007

This paper describes a transmission method based on a single carrier with frequency domain equalization (SC-FDE) scheme with cyclic prefix(CP). The SC-FDE has similar features with orthogonal frequency division multiplexing(OFDM). Similar to OFDM, a SC-FDE system is computationally efficient since equalization is reformed on a block of data in the frequency domain. Especially, it has the advantage of low sensitivity to nonlinear distortion compared to OFDM. In this paper, we design a SC-FDE receiver using decision-directed method, and present simulation results.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1255-1263
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• 2015

In the previous work, the authors studied the problem of robust discretization of linear time-invariant systems with polytopic uncertainties, where linear matrix inequality (LMI) conditions were developed to find an approximate discrete-time (DT) model of a continuous-time (CT) system with uncertainties in polytopic domain. The system matrices of obtained DT model preserved the polytopic structures of the original CT system. In this paper, we extend the previous approach to solve the problem of robust discretization of polytopic uncertain systems with aperiodic sampling. In contrast with the previous work, the sampling period is assumed to be unknown, time-varying, but contained within a known interval. The solution procedures are presented in terms of unidimensional optimizations subject to LMI constraints which are numerically tractable via LMI solvers. Finally, an example is given to show the validity of the proposed techniques.