• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.261-265
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• 2014

This paper studies the problem of the sampled-data control for Lur'e system with nonlinearities. The nonlinearities are expressed as convex combinations of sector and slope bounds. It is assumed that the sampling periods are arbitrarily varying but bounded. By constructing a new augmented Lyapunov-Krasovskii functional which have an augmented quadratic form with states as well as the nonlinear function, the stabilizing sampled-data controller gains are obtained by solving a set of linear matrix inequalities. The effectiveness of the developed method is demonstrated by numerical simulations.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.210-219
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• 2010

Consumers differ in both horizontally and vertically. Market segmentation aims to divide horizontally different (or heterogeneous) consumers into more similar (or homogeneous) small segments. A specific consumer, however, may differ in vertically. He (or she) may belong to a different market segment from another one where he (or she) belonged to before. In consumer panel data, the vertical difference can be observed by his (or her) choice among brand alternatives are changing over time. The consumer's vertical difference has been defined as 'dynamics'. In this research, we have developed a binary probit model with random-walk coefficients to capture the consumer's dynamics. With an application to a consumer panel data, we have examined how have the random-walk coefficients changed over time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8A
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• pp.664-670
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• 2005

OFDM is a very attractive technique for achieving high-bit-rate data transmission and high spectrum efficiency in fading environment. However, the reliable detection of an OFDM signal in time-varying multipath fading channels is a challenging problem. Accordingly, various channel estimation methods have been proposed for performance improvement. But, conventional pilot patterns for channel estimation in OFDM systems have not robust characteristics relating to various mobile speed. To solve this drawback in conventional patterns, we propose the pilot patterns modified from conventional patterns to have a good error performance in time-varying fading channel. Simulation results show that the performance of the proposed pilot patterns is better than conventional patterns in fast time-varying channel.

• Environmental and Resource Economics Review
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• v.13 no.3
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• pp.417-440
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• 2004

For the behavior of the wholesale spot price, a regime switching model with time-varying transition probabilities was estimated using the data from the PJM (Pennsylvania-New Jersey-Maryland) market. By including the temperature as an explanatory variable in the transition probability equations, the threshold effect of changing regime is clearly enhanced. And hence the predictability of the price spikes was improved. This means that the model showed a very clear threshold effect, with a low probability of switching for low loads and low temperatures and a high probability for high loads and high temperatures. And temperature showed a clearer threshold effect than load does. This implies that weather-related contracts may help to hedge against the risk in the cost of buying electricity during a summer.

• Journal of Biomedical Engineering Research
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• v.36 no.6
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• pp.264-270
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• 2015

A mock circulatory loop system has been developed to construct a simulator for trainees in cardiopulmonary bypass systems or to simulate a test environment for cardiac-assist devices. This paper proposes a computerized mock circulatory loop system whose node is modularized by using a servo control flow regulator to simulate dynamic change of the hemodynamic status. To observe the effect of time-varying resistance, one with hemodynamic properties, the proposed system replicates the planned cross-sectional areas of the outlet of a ventricular assist device in terms of voltage input of a servo valve. The experiment is performed (1) for steady-input commands of selected area sizes and (2) for dynamic commands such as monotonous increase and decrease, and oscillatory functions of the voltage input, and a computer program based on LabVIEW (National Instruments, Austin, USA) processes every measured data and control command to the servo valve. The results show that the pressure and flow at the target points with respect to time-varying resistance match intuitive estimation: the pressure at the outlet and the pressure drop between both sides of the valve increased and the flow at the outlet decreased for increased resistance.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.691-700
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• 2023

Purpose: This study aims to analyze the diffusion pattern of the Mongolian mobile phone market. In particular, we used a generalized diffusion model to explore the factors affecting market potenial. Methods: We used three diffusion models to estimate the number of mobile subscribers in Mongolia. Based on the Logistic model with the best fitness, we introduced time-varying market potential and explored the influence of various independent variables such as GDP and inflation. Results: Among the basic diffusion models, the Logistic model was the best in terms of estimation performance and statistical significance. The estimation results of the Generalized Logistic model confirm that investment in the telecommunication sector has a significant positive effect on market potential. The estimation of the Generalized Logistic model effectively describes the continuous growth of the Mongolian telecommunications market until recently. Conclusion: We have analyzed the diffusion pattern of the Mongolian telecommunications market and found that the amount of investment in the sector leads to the growth of the market size. This study is original in terms of its subject - Mongolian telecommunications market and methodology - time-varying market potential.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• Economic Analysis
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• v.17 no.2
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• pp.27-55
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• 2011

Although many theoretical studies have tried to explain the volatility in financial markets using models of herd behavior, there have been few empirical studies on dynamic herding due to the technical difficulty of detecting herd behavior with time-series data. Thus, this paper theoretically extends a continuous beliefs system belonging to an agent based economic model by introducing a term representing agents'mutual dependence into each agent's utility function and derives a SV(stochastic volatility)-type econometric model. From this model the time-varying herding parameters are efficiently estimated by a Markov chain Monte Carlo method. Using monthly data of KOSPI and DOW, this paper provides some empirical evidences for stronger herding in the Korean stock market than in the U.S. stock market, and further stronger herding after the global financial crisis than before it. More interesting finding is that time-varying herd behavior has weak autocorrelation and the global financial crisis may increase its volatility significantly.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.511-518
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• 2015

As the cost-per-byte of SSDs dramatically decreases, the introduction of SSDs to Hadoop becomes an attractive choice for high performance data processing. In this paper the cost-per-performance of SSD-based Hadoop cluster (SSD-Hadoop) and HDD-based Hadoop cluster (HDD-Hadoop) are evaluated. For this, we propose a MapReduce performance model using queuing network to simulate the execution time of MapReduce job with varying cluster size. To achieve an accurate model, the execution time distribution of MapReduce job is carefully profiled. The developed model can precisely predict the execution time of MapReduce jobs with less than 7% difference for most cases. It is also found that SSD-Hadoop is 20% more cost efficient than HDD-Hadoop because SSD-Hadoop needs a smaller number of nodes than HDD-Hadoop to achieve a comparable performance, according to the results of simulation with varying the number of cluster nodes.