• The Journal of Engineering Geology
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• v.19 no.2
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• pp.177-189
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• 2009

The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle (${\phi}$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.4
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• pp.45-54
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• 2011

A simple model for reinforced concrete shear walls with boundary elements is proposed, which is a macro-model composed of spring elements representing flexure and shear behaviors. The flexural behaviour is represented by vertical springs at the wall ends, where the moment strength and rotational capacity of the wall are based on section analysis. The shear behaviour is represented by a horizontal spring at the wall center, where the key parameters for the shear behavior are based on the flexural behaviour since the shear walls with boundary elements are governed by the flexure. The proposed model was prepared with the results of hysteretic tests of the shear walls, and then the reliability of the hysteretic rule and variables was investigated by nonlinear dynamic analyses. Using parametric study with nonlinear dynamic analyses, the effect of the variables on demand and capacity, which are major parameters in seismic performance evaluation, are investigated. Results show that the measured and calculated shear forces versus the shear distortion relationships are slightly different, but the global response is well simulated. Furthermore, the demand and capacity are also changed in a similar way to the change in the major parameters so that the proposed model may be appropriate for reinforced concrete shear walls with boundary elements.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.1
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• pp.59-65
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• 2011

The aerodynamic power and torque of wind turbines are extremely nonlinear. Therefore, the overall dynamic behavior of a wind turbine exhibits nonlinear characteristics that are dependent on the magnitude of the wind speed. The nonlinear aerodynamic characteristics of the wind turbine also affect the characteristics of the control system of the wind turbine. Therefore, the analysis of the nonlinear aerodynamic characteristics of wind turbine is essential in designing the wind-turbine controller. In this study, the nonlinear aerodynamic characteristics and the effects of these characteristics on the closed-loop pitch system with PI controller for an 1-mass model of the wind turbine are investigated above rated power.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.261-264
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• 2008

When the concrete is confined to width direction, stress-strain curve of concrete are different from the uniaxial behavior. In case of normal strength concrete, Mander model are used with concrete material model which considers confining effect. Sakino-Sun model showed experimental result of specimen-level and the highest accuracy. Therefore, Normal strength concrete used Mander model. and High strength concrete used Sakino-Sun model. But there are significant differences from actual data when medium strength concrete used Mander or Sakino-Sun model. and Limit scope of maximum or minimum compressive strength of concrete is not clear when applied to two models. Therefore, In this research, material nonlinear model of confined concrete is suggested when concrete which has 30-40MPa's strength is confined to width direction.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.193-204
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• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.70-75
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• 2014

Super-resolution near-field structure (super-RENS) read-out samples are affected by a nonlinear and noncausal channel, which results in inter-symbol interference (ISI). In this study, we investigate asymmetry or domain bloom in super-RENS in terms of equalization. Domain bloom is caused by writing process in optical recording. We assume in this work that the asymmetry symbol conversion scheme is to generate asymmetric symbols, and then a linear finite impulse response filter can model the read-out channel. For equalizing this overall nonlinear channel, the read-out signals are deconvolved with the finite impulse response filter and its output is decided based on the decision rule table that is developed from the asymmetry symbol conversion scheme. The proposed equalizer is investigated with the simulations and the real super-RENS samples in terms of raw bit error rate.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.65-76
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• 2010

Orthogonal frequency division multiplexing (OFDM) is an attractive technique for achieving high-bit-rate wireless data transmission. However, multicarrier systems such as OFDM show great sensitivity to nonlinear distortion. The OFDM structure requires a summation of a large number of subcarriers for multicarrier modulation, and as a result of this summation large signal envelope fluctuations occur. These fluctuations make OFDM systems to be very sensitive to nonlinear distortion introduced by the high power amplifier (HPA) at the transmitter. In this paper, we propose a canonical piecewise-linear (CPWL) model based digital predistorter to compensate for nonlinear distortion introduced by the high peak-to-average power ratio (PAPR) and the HPA in OFDM systems. The performance of the new predistortion scheme for OFDM systems is evaluated in terms of total degradation (TD) and bit error rate (BER). The simulation results demonstrated that the proposed predistorter achieves significant performance improvement by effectively compensating for the nonlinear distortion introduced by the HPA.