• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.7
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• pp.703-714
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• 2010

This paper describes the design optimization of a rotating rectangular channel with staggered arrays of pin-fins by Kriging metamodeling technique. Two non-dimensional variables, the ratio of the height to the diameter of the pin-fins and the ratio of the spacing between the pin-fins to the diameter of the pin-fins are chosen as the design variables. The objective function that is a linear combination of heat transfer and friction loss related terms with a weighting factor is selected for the optimization. To construct the Kriging model, objective function values at 20 training points generated by Latin hypercube sampling are evaluated by a three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis method with the SST turbulence model. The Kriging model predicts the objective function value that agrees well with the value calculated by the RANS analysis at the optimum point. The objective function is reduced by 11% by the optimization of the channel.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2933-2940
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• 2018

It is known that fruit is more affected by the weather than other crops. Therefore, in order to create high value for farmers, it is necessary to develop a wholesale price model considering the weather. Peaches produced under relatively limited conditions were chosen as subjects of study. The data were collected from 2015 to 2017 provided by okdab 4.0. The meteorological data used for the analysis were generated by weighting the cultivation area and the variables with high correlation among the weather data were selected from the day before to 7 days before. Randomforest, gradient boosting machine, and XGboost were used for the analysis. As a result of analysis, XGboost showed the best performance in the sense of RMSE and correlation, and price prediction was comparatively well predicted, but the accuracy of the trading volume prediction was not so good enough. The top three weather variables affecting to the peach were minimum temperature, average maximum temperature, and precipitation.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.593-604
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• 2020

This study analyzes human development convergence and the impact of funds transfer to the regions using σ and β-convergence analysis method. Observations were made in all Indonesia's provinces in the period 2010-2019. The coefficient of variation calculation shows a dispersion in the inequality of human development, which means that convergence occurred. This is also documented by the clustering analysis results developed in the study. The results are in line with the hypothesis of neoclassical theory, which shows the tendency for provinces with lower human development levels to grow relatively faster. The dynamic panel data approach with the GMM model shows that a model built with explanatory variables for transfer of funds to regions may lead to the process of convergence of human development - 2.21% per year or 31 years to cover the half-life of convergence. This is a consequence of the Special Allocation Fund and the Village Fund, which positively impact the convergence process, and the General Allocation Fund and the Revenue Sharing Fund with negative signs slowing the convergence process. This evidence opens opportunities to review the justification of the weighting component in determining the amount of funds transferred to the region to accelerate the convergence process of human development.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.797-802
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• 2010

In this paper, a systematic procedure to design a nonlinear controller for nonlinear active magnetic bearing (AMB) systems is presented. To do this, we effectively convert the AMB system into a polytopic quasi-linear parameter varying (LPV) system, which is a representation of nonlinear state-space models and is described by the convex combination of a set of precisely known vertices. Unlike the existing quasi-LPV systems, the nonlinear weighting functions, which construct the polytopic quasi-LPV model of the AMB system by connecting the vertices, include not only state variables but also the input ones. This allows us to treat the input nonlinearity effectively. By means of the derived polytopic quasi-LPV model and linear matrix inequality (LMI) conditions, nonlinear controller that stabilizes the AMB system is obtained. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.10 s.241
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• pp.1163-1171
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• 2005

The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, front leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the . reduction of the drag furce, improves its drag to $13\%$ and lift-drag ratio to $2\%$. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to $61\%$, while sustaining its drag force, compared to those of the baseline model.

• The KSFM Journal of Fluid Machinery
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• v.14 no.1
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• pp.66-76
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• 2011

The heat transfer and pressure loss characteristics of a rotating two-pass channel with a guide vane in the turning region have been studied using three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis, and the shape of the guide vane has been optimized using surrogate modeling optimization technique. For the optimization, thickness, location and angle of the guide vanes have been selected as design variables. The objective function has been defined as a linear combination of the heat transfer and the friction loss related terms with a weighting factor. Latin hypercube sampling has been applied to determine the design points as design of experiments. A weighted-average surrogate model, PBA has been used as the surrogate model. The guide vane in the turning region does not influence the heat transfer in the first passage upstream of the turning region, but enhances largely the heat transfer in the turning region and the second passage. In an example of the optimization, the objective function has been increased by 13.6%.

• Structural Engineering and Mechanics
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• v.56 no.1
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• pp.49-56
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• 2015

Dynamic compaction (DC) is a useful method for improvement of granular soils. The method is based on falling a tamper (weighting 5 to 40 ton) from the height of 15 to 30 meters on loose soil that results in stress distribution, vibration of soil particles and desirable compaction of the soil. Propagation of the waves during tamping affects adjacent structures and causes structural damage or loss of performance. Therefore, determination of the safe or critical distance from tamping point to prevent structural hazards is necessary. According to FHWA, the critical distance is defined as the limit of a particle velocity of 76 mm/s. In present study, the ABAQUS software was used for numerical modeling of DC process and determination of the safe distance based on particle velocity criterion. Different variables like alluvium depth, relative density, and impact energy were considered in finite element modeling. It was concluded that for alluvium depths less than 10 m, reflection of the body waves from lower boundaries back to the soil and resonance phenomenon increases the critical distance. However, the critical distance decreases for alluvium depths more than 10 m. Moreover, it was observed that relative density of the alluvium does not significantly influence the critical distance value.