• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.352-356
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• 2002

In this paper, an automated design system of suspension and steering parts is developed. The system automates the processes of 3-D modeling and 2-D drafting of the parts. In addition, the BOM and dimension data of the designed part is also automatically transferred to the database of ERP system. The system is developed by using the functions of parametric design and API(application Programming Interface) of the a commercial solid modeler.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.154-161
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• 2009

In order to implement continuous-path motion on a robot, it is necessary to blend one joint motion to another joint motion near a via point in a trapezoidal form of joint velocity. First, the velocity superposition using parametric interpolation is proposed. Hybrid motion blending is defined as the blending of different two type's motions such as blending of joint motion with linear motion, in the neighborhood of a via point. Second, hybrid motion blending algorithm is proposed based on velocity superposition using parametric interpolation. By using a 3-axis SCARA (Selective Compliance Assembly Robot Arm) robot with $LabVIEW^{(R)}$ $controller^{(1)}$, the velocity superposition algorithm using parametric interpolation is shown to result in less vibration, compared with PTP(Point- To-Point) motion and Kim's algorithm. Moreover, the hybrid motion $algorithm^{(2)}$ is implemented on the robot using $LabVIEW^{(R)(1)}$ programming, which is confirmed by showing the end-effector path of joint-linear hybrid motion.

• Korean Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.168-176
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• 1998

This paper deals with an application on the mould machining of air intake hose product by using 3-dimensional parametric modeling techniques. The detailed domain is the 3-dimensional product with similar shapes and different sizes which needs too much working time for preparation of modeling or machining due to making a trial and errors repeatedly. Decision making rules for selection of modeling order and technique, and for calculation of cutting conditions, and for determination of sequence and method concerning machining operations are required by interview of expert engineers in the field. The developed expert system of modeling and machining is programmed by using a user programming language under the CAD/CAM software of the Personal Designer. The developed system that aids a mould engineer who is working in the modeling and machining section which deal with air intake hose product provides strong and useful capabilities.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.641-653
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• 2022

Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.1
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• pp.92-99
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• 2004

With the advent of computers, CAD systems are widely used for various design practice. Complexity of CAD systems and difficulty of exchanging data among different CAD systems, however, do not allow efficient use as desired. In addition, to follow variety of designers' need, CAD activities should be customized. This article proposed a methodology fer developing an intelligent CAD system and the sate-of-the-art technologies described fur customizing CAD systems using API (Application Programming Interface). A basic platform is proposed and a useful application system is implemented to enable a parametric design by directly inputting numerical values on a CAD model. Based on this application, we developed a system that makes it possible to share part family data between SolidEdge and Pro/Engineer. The proposed concept on intelligent CAD systems facilitates integration of external systems such as CAE tools and promotes the use of CAD for both engineering designers and analysts.

• Structural Engineering and Mechanics
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• v.36 no.2
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• pp.225-241
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• 2010

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.

• Computers and Concrete
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• v.26 no.6
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• pp.497-504
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• 2020

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using Gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.173-181
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• 1996

In this paper, we studied the modeling methods of initial ship structure design results using ship structure product model for concurrent engineering in shipbuilding industry. We suggested two modeling methods which are programming method using CAD commands and Macros for primary structure members. Also, we investigated three modeling procedures which are modeling procedure in unit concept, modeling procedure using version control, and modeling procedure for computer aided process planning. We demonstrated how to use the structure product modeling results in some application fields. Finally, we proposed some ideas for future research.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Journal of the Korean Mathematical Society
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• v.41 no.5
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• pp.821-864
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• 2004

Both parametric and parameter-free necessary and sufficient optimality conditions are established for a class of nondiffer-entiable nonconvex optimal control problems with generalized fractional objective functions, linear dynamics, and nonlinear inequality constraints on both the state and control variables. Based on these optimality results, ten Wolfe-type parametric and parameter-free duality models are formulated and weak, strong, and strict converse duality theorems are proved. These duality results contain, as special cases, similar results for minmax fractional optimal control problems involving square roots of positive semi definite quadratic forms, and for optimal control problems with fractional, discrete max, and conventional objective functions, which are particular cases of the main problem considered in this paper. The duality models presented here contain various extensions of a number of existing duality formulations for convex control problems, and subsume continuous-time generalizations of a great variety of similar dual problems investigated previously in the area of finite-dimensional nonlinear programming.