• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Nuclear Engineering and Technology
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• v.25 no.1
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• pp.110-124
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• 1993

A Nonlinear model-based Hybrid Controller (NHC) is developed which consists of the adaptive proportional-integral-feedforward (PIF) gains and variable structure controller. The controller has the robustness against modeling uncertainty and is applied to the trajectory tracking control of single-input, single-output nonlinear systems. The essence of the scheme is to divide the control into four different terms. Namely, the adaptive P-I-F gains and variable structure controller are used to accomplish the specific control actions by each terms. The robustness of the controller is guaranteed by the feedback of estimated uncertainty and the performance specification given by the adaptation of PIF gains using the second method of Lyapunov. The variable structure controller is incorporated to regulate the initial peak of the tracking error during the parameter adaptation is not settled yet. The newly developed NHC method is applied to the power tracking control of a nuclear reactor and the simulation results show great improvement in tracking performance compared with the conventional model-based control methods.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.721-732
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• 2018

Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.

• Steel and Composite Structures
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• v.41 no.6
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• pp.873-886
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• 2021

In this paper, an analytical solution for thermodynamic response of functionally graded (FG) sandwich plates resting on variable elastic foundation is performed by using a quasi 3D shear deformation plate theory. The displacement field used in the present study contains undetermined integral terms and involves only four unknown functions with including stretching effect. The FG sandwich plate is considered to be subject to a time harmonic sinusoidal temperature field across its thickness with any combined boundary conditions. Equations of motion are derived from Hamilton's principle. The numerical results are compared with the existing results of quasi-3D shear deformation theories and an excellent agreement is observed. Several numerical examples for fundamental frequency, deflection, stress and variable elastic foundation parameter's analysis of FG sandwich plates are presented and discussed considering different material gradients, layer thickness ratios, thickness-to-length ratios and boundary conditions. The results of the present study reveal that the nature of the elastic foundation, the boundary conditions and the thermodynamic loading affect the response of the FG plate especially in the case of a thick plate.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.305-312
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• 2022

In this paper, we focused on training to create and optimize a basic data prediction model. And we proposed a gradient descent training method of machine learning that is widely used to optimize data prediction models. It visually shows the entire operation process of gradient descent used in the process of optimizing parameter values required for data prediction models by applying the differential method and teaches the effective use of mathematical differentiation in machine learning. In order to visually explain the entire operation process of gradient descent, we implement gradient descent SW in a spreadsheet. In this paper, first, a two-variable gradient descent training method is presented, and the accuracy of the two-variable data prediction model is verified by comparison with the error least squares method. Second, a three-variable gradient descent training method is presented and the accuracy of a three-variable data prediction model is verified. Afterwards, the direction of the optimization practice for gradient descent was presented, and the educational effect of the proposed gradient descent method was analyzed through the results of satisfaction with education for non-majors.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• Steel and Composite Structures
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• v.53 no.2
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• pp.209-226
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• 2024

In the present study, the third-order shear deformation theory (TSDT) is presented to investigate time-dependent thermo-elastic creep behavior and life assessment of rotating thick cylindrical shells with variable thickness made of 304L austenitic stainless steel (304L SS). The cylindrical shells are subjected to non-uniform internal pressure, distributed temperature field, and a centrifugal body force due to rotating speed. Norton's law is used to describe the material creep constitutive model. A system of differential equations in terms of displacement and boundary conditions is derived by employing the minimum total potential energy principle based on TSDT. Then, the resulting equations are solved as semi-analytically using the multilayered method (MLM), which leads to an accurate solution. Subsequently, an iterative procedure is also proposed to investigate the stresses and deformations at different creep times. Larson-Miller Parameter (LMP) and Robinson's linear life fraction damage rule are employed to estimate the creep damages and the remaining life of cylindrical shells. In this research, the creep model uses Norton's law, LMP, and Robinson's approach which is the most accurate and reasonable model. To the best of the researcher's knowledge, in the previous studies, there is no study carried out on third-order shear deformation theory for thermo-elastic creep analysis and life assessment of thick cylinders with variable thickness. The results obtained from the multilayered approach are compared and validated with those determined from the finite element method (FEM) to confirm the accuracy of the suggested method based on TSDT and very good agreement is found. The results indicate that the present analysis is accurate and computationally efficient.

• Journal of the Korean Institute of Telematics and Electronics
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• v.11 no.4
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• pp.1.1-11
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• 1974

A general and systematic method of organizing two-dimensional flexible cellular array which is capable of reclizing arbitrary combinational switching function is developed. A set of n functions of n variables is transformed to revalued functions of one variable. This set of functions form a semigroup under the normal operation which is defined in this paper. A systematic method of generating any functions using three base functions is presented. Three basic networks which are capable of realizing three base functions are designed using only one one-dimensional array. The algorithm is presented for lealizing arbitrary combinational switching functions by organizing this basic array in two.dimensional cellular array and by appropriately setting the parameters or the edge of the array.

• Steel and Composite Structures
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• v.36 no.6
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• pp.689-702
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• 2020

The current study deals with the size-dependent free vibration analysis of graphene nanoplatelets (GNPs) reinforced polymer nanocomposite plates resting on Pasternak elastic foundation containing different boundary conditions. Based on a four variable refined shear deformation plate theory, which considers shear deformation effect, in conjunction with the Eringen nonlocal elasticity theory, which contains size-dependency inside nanostructures, the equations of motion are established through Hamilton's principle. Moreover, the effective material properties are estimated via the Halpin-Tsai model as well as the rule of mixture. Galerkin's mathematical formulation is utilized to solve the equations of motion for the vibrational problem with different boundary conditions. Parametrical examples demonstrate the influences of nonlocal parameter, total number of layers, weight fraction and geometry of GNPs, elastic foundation parameter, and boundary conditions on the frequency characteristic of the GNPs reinforced nanoplates in detail.

• Journal of Power System Engineering
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• v.13 no.6
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• pp.43-50
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• 2009

If an internal combustion engine is operated by consolidated control, the minimum fuel consumption is achieved satisfying the demanded objectives. For this, it is necessary that the engine is operated on the ideal operating line which satisfies minimum fuel consumption. In this context of view, there are many tries to achieve given object. However, the parameter in the internal combustion engines are variable and depend on the operating points. Therefore, it is necessary to cope with the uncertainties such that the optimal operating may be possible. From this point of view, this paper gives a controller design method and a robust stability condition for engine torque control which satisfies the given control performance and robust stability in the presence of physical parameter perturbation. Exactly, the present paper considers a robust stability of this 2DOF servosystem with nonlinear type uncertainty in the engine system, and a robust stability condition for the servosystem is introduced. This result guarantees that if the plant uncertainty is in the permissible set defined by the given condition then a gain tuning can be carried out to suppress the influence of the plant uncertainties.