• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.697-709
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• 2011

For improvement of wireless Internet service quality at vehicle's moving speed, it is advised to reduce the service disruption time by reducing the handover frequency on vehicle's moving path. Particularly, it is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment. The experiment results confirmed that the proposed scheme was able to provide the improved wireless Internet service to vehicle that moved to some degree of consistency.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.114-122
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• 2015

Real estate development requires significant amount of capital investment. The project duration has been increased according to its enlarged size. For this reason, cost overrun and time delay are important risk factors that should be managed properly. As a method to hedge the risk, varoius real option methods have been presented. However, conventional project value assesment methods such as NPV(Net Present Value) have weakness to support decision making by reflecting dynamic situations in terms of variation of cost and time. Furthermore, the decision making process is serious of actions rather than discrete event. The purpose of this paper is to present a multiple real option valuation method to overcome the deterministic aspect of real option presented in previous research and practice. The method is developed as following: firstly, to select the model that can be applied in the real estate development project through a survey from previous literature on real options analysis; secondly, to apply data from office development case in order to verify the model by applying conventional real option and multiple real option valuation. According to analysis result, multiple real option provides enhanced values comparing to NPV and single real option.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.503-519
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• 2005

Cointegration test is usually performed under the assumption that the cointegrating vector is constant for the whole sample period. Most previous studies have used conventional cointegration methods in testing for a stable long-run equilibrium relation among related variables. However they have overlooked that the long-run equilibrium may not the unique and the stable relation may not be guaranteed. This study develops the additional statistical tests for the stability of the estimated cointegrating vector. Three tests for the parameter stability of a cointegrated regression model are utilized and applied to identify the types of variations in the long-run relation between the domestic unemployment and the rotated macroeconomic variables of interest. The present paper finds that, there exists a stable but, time-varying long-run relation between those. The observed variation in cointegrating relations is generally characterized by a discrete one-time shift, rather than a gradually evolving random walk process which is attributable to the IMF financial and economic crisis.

• Journal of the Korean Geotechnical Society
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• v.22 no.9
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• pp.45-59
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• 2006

The successful performance of any numerical geotechnical simulation depends on the accuracy and efficiency of the numerical implementation of constitutive model used to simulate the stress-strain (constitutive) response of the soil. The corner stone of the numerical implementation of constitutive models is the numerical integration of the incremental form of soil-plasticity constitutive equations over a discrete sequence of time steps. In this paper a well known two-surface soil plasticity model is implemented using a generalized implicit return mapping algorithm to arbitrary convex yield surfaces referred to as the Closest-Point-Projection method (CPPM). The two-surface model describes the nonlinear behavior of coarse-grained materials by incorporating a bounding surface concept together with isotropic and kinematic hardening as well as fabric formulation to account for the effect of fabric formation on the unloading response. In the course of investigating the performance of the CPPM integration method, it is proven that the algorithm is an accurate, robust, and efficient integration technique useful in finite element contexts. It is also shown that the algorithm produces a consistent tangent operator $\frac{d\sigma}{d\varepsilon}$ during the iterative process with quadratic convergence rate of the global iteration process.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.597-632
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• 2010

There is general agreement that global warming is occurring and that the main contributor to this probably is the buildup of green house gasses, GHG, in the atmosphere. Two main contributors are the utilization of fossil fuels and the deforestation of many regions of the world. The burning of fossil fuels increases atmospheric carbon while the burning of fuel-wood reducing fossil fuel consumption along with its forest source maintain an atmospheric carbon level. The standing timber in the forests is a carbon sink, as are wood buildings and structures, and fossil fuel in the ground. This paper is designed to examine a number of current issues related to mitigating the global warming problem through forestry. For this purpose, we develop a modeling approach by integrating timber market, fossil fuel market and carbon cycling model. We use discrete time optimal control theory to identify optimal time paths, the laws of motion, and stationary stats solutions of endogenous variables in the model. On the basis of these results, we identify the optimal amounts of subsidies to be provided or taxes to be imposed by the regulatory agency to mitigate atmospheric carbon accumulation. We also present a numerical example to help illustrate the characteristics of variables in the model when the social cost for atmospheric carbon incrementally shifts upward. A surprising result is that the social cost function for atmospheric carbon has a very smaller impact on the optimal rotation period than previous literature suggested.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.1
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• pp.54-62
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• 1995

This paper is a study on a speed control of an induction motor used the MRA-DSM(Mode1 Reference Adaptive-Discrete Sliding Mode) controller. In this paper, when controls motor speed, DSM algorithm is proposed for having Robustness against disturbance and parameter variation. and it is also proposed MRA-DSM including the additional load model reference algorithm, which can be compensated the discontinuous control imputs at sliding mode and followed the model Preference independent of parameter variation of control subjects. The control system is composed of the parallel processing control system using the microprocessor for maximizing the performance of control systems and the real time processing. Also it simplifies the hardware composed of controlling the system by software and improves the reliability of the system. And while MRA-DSM control, faster response characteristics of 27.2 % is obtained than DSM control.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.157-162
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• 2010

In this paper we propose a fractal analysis based on the discrete wavelet transform. It is well known that Fourier Transform is widely used for frequency analysis of random signal. However, the frequency domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. Maximum value in the wavelet modules can be expressed by the Lipschitz exponent, which is useful to represent the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only by using the few maximum points. The v possible image It iusing oil was acquired to interpret the maximum value. ufter that, it was applied to the v possible image of a ship model. In addition, the fractal dimens by by the conlapse process of the sediment particle was examined. In this paper, the fractal dimens by has been obtained by the maximum value and the experiment obtained from the visualized image also acquired the same result as existing methods.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.345-351
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• 2019

Avionics System Integration Laboratory is an integrated test environment for the integration and the verification of avionics systems. Recently, in order to fully consider the requirements verification of avionics system from the aspect of the entire system integration, the participation in the development of the SIL field is advanced from the requirement analysis of the aircraft. Efforts are being made to minimize the cost and the period of development of a SIL so that it does not affect the overall schedule of the aircraft development. We propose the avionics simulation model framework (ASMF) based on the modeling formalism applicable to SIL in order to reduce development period/cost and increase maintenance by standardizing the modeling methods of SIL.