• 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.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.387-413
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• 1998

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(P<0.05). 3. There were significant differences of SNR values between 60kVp and 70kVp in seven interpolation methods. There were significant differences of SNR values between facet model method and those of the other methods at 60kVp(P<0.05), but there were not significant differences of SNR values among seven interpolation methods at 70kVp(P>0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods (P< 0.05). 5. The speed of computation time was the fastest with nearest -neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.

• Journal of Navigation and Port Research
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• v.34 no.3
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• pp.195-203
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• 2010

This paper concerns about total dynamic analysis of integrated mining system. This system consists of vertical steel pipe, intermediate buffer station, flexible pipe and self-propelled miner. The self-propelled miner and buffer are assumed as rigid-body of 6-dof. Discrete models of vertical steel pipe and flexible pipe are adopted, which are obtained by means of lumped-parameter method. The motion of mining vessel is not considered. Instead, the motion of mining vessel is taken into account in form of various boundary conditions (e.g. forced excitation in slow motion and/or fast oscillation and so on). A terramechanics model of extremely cohesive soft soil is applied to the self-propelled miner. Hinged and ball constraints are used to define the connections between sub-systems (vertical steel pipe, buffer, flexible pipe, self-propelled miner). Equations of motion of the coupled model are derived with respect to the each local coordinates system. Four Euler parameters are used to express the orientations of the sub-systems. To solve the equations of motion of the total dynamic model, an incremental-iterative formulation is employed. Newmark-${\beta}$ method is used for time-domain integration. The total dynamic responses of integrated mining system are investigated.

• 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.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.

• 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.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.456-467
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• 2019

In this study, the discrete element method was used to simulate the excavation of spoke-type shield TBM. The horizontal stress coefficient was used for the ground to simulate the increase of the horizontal stress according to the depth, and the driving conditions were set based on the torque generated from the cutterhead of the TBM to excavate within the operating range. That is, when the value of the torque generated at the cutterhead exceeds the given operating condition, the speed of excavation is constantly reduced, and conversely, the method of increasing the speed of excavation is considered. The change speed of the excavation was given the minimum change requirement in consideration of the driver's review time, and the change was possible according to the excavation conditions. In order to use these conditions, the user-subroutine was considered separately, and the results show that the DEM model were able to analyze the excavation within the considered operating range.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.6
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• pp.59-76
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• 2019

Most technology-based entrepreneurship(TBE) go through an process of decline or disappear without overcoming the valley of death(VoD). The purpose of this study is to identify the growth dimension of TBE and to test the influence of firms activities on firms growth over time. This study identified the two-dimensional growth dimension divided by size and profit through exploratory factor analysis(EFA) of a number of growth indicators. Then, we defined the discrete state of growth firm in four states, divided by size and profit, and five states, including the closure of business. Multi-nomial logit model is used to predict the effect of TBE activities on a discrete state of growth firm(size×profit, closure of business) based on multiple independent variables. The independent variables are based on five representative firms activities: employment, marketing, R&D, financial activities, and general management activities. The growth stage of TBE over time has been categorized into three stages: early stage, middle stage, and late stage of business, taking into account the main periods during which the survival rate of startups sharply decreases. The analytical data of this study was based on the secondary data of the start-up supporting companies of government and public institutions. The subjects of analysis were TBE within 10 years. As a result of the empirical analysis, the employment and marketing activities of TBE show that early and mid-term activities had an effect on the state of firms growth. However, if there is a difference, employment activities have both positive and negative effects, while marketing activities have only a positive effect on size and profit growth. And besides, R&D activities, financial activities, and general management activities throughout the entire process of firms growth were found to be firms activities that have both positive and negative effects on firms growth. In addition, the age of the founder, the firms' industry, and the geographic location of the firms, which are general characteristics of the company, were found to have a distinctive effect on the growth status of the firms according to the growth stage.

• Journal of the Korean Institute of Gas
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• v.7 no.4 s.21
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• pp.44-52
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• 2003

In this work dynamic heat transfer in a CPFS (cable penetration fire stop) system built in the firewall of nuclear power plants is three-dimensionally investigated to develop a test-simulator that can be used to verify effectiveness of the sealant. Dynamic heat transfer in the fire stop system is formulated in a parabolic PDE (partial differential equation) subjected to a set of initial and boundary conditions. First, the PDE model is divided into two parts; one corresponding to heat transfer in the axial direction and the other corresponding to heat transfer on the vertical planes. The first PDE is converted to a series of ODEs (ordinary differential equations) at finite discrete axial points for applying the numerical method of SOR (successive over-relaxation) to the problem. The ODEs are solved by using an ODE solver In such manner, the axial heat flux can be calculated at least at the finite discrete points. After that, all the planes are separated into finite elements, where the time and spatial functions are assumed to be of orthogonal collocation state at each element. The initial condition of each finite element can be obtained from the above solution. The heat fluxes on the vertical planes are calculated by the Galerkin FEM (finite element method). The CPFS system was modeled, simulated, and analyzed here. The simulation results were illustrated in three-dimensional graphics. Through simulation, it was shown clearly that the temperature distribution was influenced very much by the number, position, and temperature of the cable stream, and that dynamic heat transfer through the cable stream was one of the most dominant factors, and that the feature of heat conduction could be understood as an unsteady-state process.