• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• The Journal of Korea Robotics Society
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• v.6 no.4
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• pp.334-343
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• 2011

This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system's precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time, the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn-table, we setup a carouseling system to decrease the substantial bias effect on gyroscope's heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system's accuracy, usefulness and adaptability are shown.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.27-32
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• 2005

This paper presents the optimization of expanding velocity for tube expanding process in the manufacturing of a heat exchanger. In specific, the expanding velocity has a great influence on the performance of a heat exchanger because it is a key variable determining the quantity of tube expending at assembly stage as well as a key Parameter determining overall production rate. The simulation showed that the genetic algorithm used in this paper resulted in the optimal tube expanding velocity by performing the following series of iteration; the generation of arbitrary population for tube expanding parameters, consequently the generation of tube expanding velocities, the evaluation of tube expanding quantity using the pre-trained data of plastic deformation by means of a neural network and finally the generation of next population using a penalty faction and a Roulette wheel method.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.1-8
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• 2012

This study is to assess the reduction of non-point source pollution loads for rice straw surface covering of upland crop cultivation at a watershed scale. For Byulmi-cheon watershed ($1.21km^2$) located in the upstream of Gyeongancheon, the HSPF (Hydrological Simulation Program-Fortran), a physically based distributed hydrological model was applied. Before evaluation, the model was calibrated and validated using 9 rainfall events. The Nash-Sutcliffe model efficiency (NSE) for streamflow was 0.62~0.78 and the NSE for water quality (Sediment, T-N, and T-P) were 0.68, 0.60, and 0.58 respectively. From the field experiment of 16 rainfall events, the rice straw covering reduced surface runoff average 10 % compared to normal surface condition. By handling infiltration parameter (INFILT) in the model, the value of 16.0 mm/hr was found to reduce about 10 % reduction of surface runoff. For this condition, the reduction effect of Sediment, T-N, and T-P loads were 87.2, 28.5, and 85.1 % respectively. The rice straw surface covering was effective for removing surface runoff dependent loads such as Sediment and T-P.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.2
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• pp.95-101
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• 2018

Diagonally reinforced concrete coupling beams (DRCB) play an important role in coupled shear wall systems since these elements dissipate most of seismic input energy under earthquake loading. For reliable seismic performance evaluation using nonlinear response history analysis, it is important to use an accurate analytical model for DRCBs. In this study, the Pinching4 model is used as a base model to simulate the cyclic behavior of DRCBs. For simulating the cyclic behavior of DRCBs using the Pinching4 model, the analytical parameters for backbone curve, pinching and cyclic deterioration in strength and stiffness should be computed. To determine the proper values of the constituent analytical parameters efficiently and accurately, this study proposes the empirical equations for the analytical parameters using regression analyses. It is shown that the hysteretic behavior of coupling beams can be simulated efficiently and accurately using the proposed numerical model with the proposed empirical equations of model parameters.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.1
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• pp.101-107
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• 2011

Analytical solution of micro-scale milling process is presented in order to suggest available machining conditions. The size effect should be considered to determine cutting characteristics in micro-scale cutting. The feed per tooth is the most dominant cutting parameter related to the size effect in micro-scale milling process. In order to determine the feed per tooth at which chips can be formed, the finite element method is used. The finite element method is employed by utilizing the Johnson-Cook (JC) model as a constitutive model of work material flow stress. Machining experiments are performed to validate the simulation results by using a micro-machining stage. The validation is conducted by observing cutting force signals from a cutting tool and the conditions of the machined surface of the workpiece.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1051-1055
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• 1997

Hot forging is widely used in the manufacturing of automotive component. The mechanical, thermal load and thermal softening which is happened by the high temperature die in hot forging. Tool life of hot forging decreases considerably due to the softening of the surface layer of a tool caused by a high thermal load and long contact time between the tool and workpieces. The service life of tools in hot forging process is to a large extent limited by wear, heat crack, plastic deformation. These are one of the main factors affecting die accuracy and tool life. It is desired to predict tool life by developing life prediction method by FE-simulation. Lots of researches have been done into the life prediction of cold forming die, and the results of those researches were trustworthy, but there have been little applications of hot forming die. That is because hot forming process has many factors influencing tool life, and there was not accurate in-process data. In this research, life prediction of hot forming die by wear analysis and plastic deformation has been carried out. To predict tool life, by experiment of tempering of die, tempering curve was obtained and hardness express a function of main tempering curve.

• Advances in concrete construction
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• v.9 no.5
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• pp.469-478
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• 2020

This study aims to establish a new methodological framework for the evaluation of the evolution of the reliability of plain concrete for pavement vs number of cycles under flexural fatigue loading. According to the framework, a new method calculating the reliability was proposed through probability simulation in order to describe a random accumulation of fatigue damage, which combines reliability theory, one-to-one probability density functions transformation technique, cumulative fatigue damage theory and Weibull distribution theory. Then the statistical analysis of flexural fatigue performance of cement concrete tested was carried out utilizing Weibull distribution. Ultimately, the reliability for the tested cement concrete was obtained by the proposed method. Results indicate that the stochastic evolution behavior of concrete materials under fatigue loading can be captured by the established framework. The flexural fatigue life data of concrete at different stress levels is well described utilizing the two-parameter Weibull distribution. The evolution of reliability for concrete materials tested in this study develops by three stages and may corresponds to develop stages of cracking. The proposed method may also be available for the analysis of degradation behaviors under non-fatigue conditions.