• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.2
• /
• pp.171-177
• /
• 2016

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this study, reliability software cost model considering shape parameter based on life distribution from the process of software product testing was studied. The cost comparison problem of the Lomax distribution reliability growth model that is widely used in the field of reliability presented. The software failure model was used the infinite failure non-homogeneous Poisson process model. The parameters estimation using maximum likelihood estimation was conducted. For analysis of software cost model considering shape parameter. In the process of change and large software fix this situation can scarcely avoid the occurrence of defects is reality. The conditions that meet the reliability requirements and to minimize the total cost of the optimal release time. Studies comparing emissions when analyzing the problem to help kurtosis So why Kappa efficient distribution, exponential distribution, etc. updated in terms of the case is considered as also worthwhile. In this research, software developers to identify software development cost some extent be able to help is considered.

• Journal of Energy Engineering
• /
• v.22 no.4
• /
• pp.370-375
• /
• 2013

This paper attempts to estimate the city gas demand function in Korea over the period 1981-2012. As the city gas demand function provides us information on the pattern of consumer's city gas consumption, it can be usefully utilized in predicting the impact of policy variables such as city gas price and forecasting the demand for city gas. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the city gas demand function. The results show that short-run price and income elasticities of the city gas demand are estimated to be -0.522 and 0.874, respectively. They are statistically significant at the 1% level. The short-run price and income elasticities portray that demand for city gas is price- and income-inelastic. This implies that the city gas is indispensable goods to human-being's life, thus the city gas demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for city gas is price- and income-elastic in the long-run.

• KDI Journal of Economic Policy
• /
• v.13 no.4
• /
• pp.117-140
• /
• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.25 no.6
• /
• pp.422-432
• /
• 2013

A System Identification method to develop parametric models linking free surface elevation and wave pressure is presented and two models are built allowing for either wave pressure or free surface elevation simulation. Linear, time invariant model structures with static nonlinearities are assumed and solutions are sought in a form of autoregressive model with extra input (ARX). An arbitrary chosen free-surface elevation and wave pressure dataset is used for estimation of the models, which are subsequently verified against datasets with similar pressure gauge depth but different free-surface elevation spectra due to different meteorological conditions. It is shown that free-surface simulation using System Identification methods can perform better than traditional linear transfer function derived from linear wave theory (LTF), while wave pressure simulation quality using presented methods is generally similar to that obtained with corrected LTF.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.3
• /
• pp.306-311
• /
• 2016

Software reliability in the software development process is an important issue. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, reliability software cost model considering logarithmic and exponential fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software reliability model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. The logarithmic and exponential fault detection model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, the software developers have to consider life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• Korean Journal of Remote Sensing
• /
• v.29 no.5
• /
• pp.555-568
• /
• 2013

Atmospheric optical thickness during nighttime was estimated in this study using analysis on the images of the moon taken from commercial digital camera. Basically the Langely Regression method was applied to the observations of the moon for the cloudless and optically stable sky conditions. The spectral response functions for the red(R), green(G), and blue(B) channels were employed to derive effective wavelength centers of each channel for the observations of the moon, and the correspondent Rayleigh optical thickness were also calculated. Aerosol optical thickness (AOT) was calculated by subtracting Rayleigh optical thickness from the atmospheric optical thickness derived from the Langley regression method. As there are only handful of nighttime AOT observations, the AOT from the moon observations was compared with the AOT from sun-photometers and the MODIS satellite sensor, which was taken several hours before the moon observations of this study. As a result, the values of AOT from moon observations agree with those from sun-photometers and MODIS within 0.1 for the R, G, B channels of the digital camera. On the other hand, ${\AA}$ngstr$\ddot{o}$m Exponent seems to be subject to larger errors due to its sensitiveness to the spectral errors of AOT. Nevertheless, the results of this study indicate that the method reported in this study is promising as it can provide nighttime AOT relatively easily with a low cost instrument like digital camera. More observations and analyses are warranted to attain improved nighttime AOT observations in the future.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.641-648
• /
• 2022

In this study, the performance of the NHPP software reliability model with exponential distribution (Exponential Basic, Inverse Exponential, Lindley, Rayleigh) characteristics was comparatively analyzed, and based on this, the optimal reliability model was also presented. To analyze the software failure phenomenon, the failure time data collected during system operation was used, and the parameter estimation was solved by applying the maximum likelihood estimation method (MLE). Through various comparative analysis (mean square error analysis, true value predictive power analysis of average value function, strength function evaluation, and reliability evaluation applied with mission time), it was found that the Lindley model was an efficient model with the best performance. Through this study, the reliability performance of the distribution with the characteristic of the exponential form, which has no existing research case, was newly identified, and through this, basic design data that software developers could use in the initial stage can be presented.

• The KIPS Transactions:PartB
• /
• v.14B no.4
• /
• pp.311-320
• /
• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.4
• /
• pp.351-358
• /
• 2016

Science and technology is developing rapidly as more powerful software with the rapid development of software testing and reliability assessment by the difficulty increases with the complexity of the software features of the larger increases NHPP software reliability models for failure analysis can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, finite failure NHPP models that assuming the expected value of the defect and infinite failures NHPP models that repairing software failure point in time reflects the situation, were presented for comparing property. Commonly used in the field of software reliability based on Erlang distribution software reliability model finite failures and infinite failures were presented for performance comparative evaluation problem. As a result, finite failure model is better than infinite failure model effectively. The parameters estimation using maximum likelihood estimation in the course of this study was conducted. As the results of this research, software developers to identify software failure property be able to help is concluded.