• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2439-2449
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• 2011

Existing grade crossings between railway and roadway area gradually changed to grade separation systems by the law. In the case of new roadway construction which crosses railways, it shall be grade separation system in principle. With the railway underground crossing method, many practices have been developed which can minimize rail displacements and avoid rail release. With these methods, the effects to the train can be reduced. The underground crossing methods can be identified as open-cut methods and non open-cut methods. The open-cut methods include temporary support methods and special rail construction methods. Also the non open-cut methods includes pipe roof methods, front jacking methods, messer shield methods, NTR methods and JES methods. Among these, the most suitable method is applied considering safety, economy, class of each rail system (train passing frequency and velocity), etc. In the non open-cut methods, the cost and duration shall be increased to keep existing rail system during construction. In the open-cut methods which use plate girders, the rail speed shall be restricted due to the displacement and vibration of the girder. In this study new grade separation methods were developed. With this method, the safety during construction can be increased. This method refines temporary support methods, but pc slab girder with huge stiffness is applied instead of plate girders. With this method, the rail displacement can be reduced and higher safety can be obtained during construction. Also construction cost and duration can be minimized because the temporary work and the overburden soil depth can be reduced.

• Journal of applied mathematics & informatics
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• v.4 no.2
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• pp.341-378
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• 1997

We consider three classes of numerical methods for solv-ing the semi-explicit differential-algebraic equations of index 1 and higher. These methods use implicit multistep fixed stepsize methods and several iterative processes including simple iteration, full a2nd modified Newton iteration. For these methods we prove convergence theorems and derive error estimates. We consider different ways of choosing initial approximations for these iterative methods and in-vestigate their efficiency in theory and practice.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.1
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• pp.1-11
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• 1999

We classified preprocessing methods into (1) analytic methods, (2) methods for removing implied free variables, (3) methods using pivot or elementary row operations, (4) methods for removing linearly dependent rows and columns and (5) methods for dense columns. We noted some considerations to which should be paid attention when preprocessing methods are applied to interior point methods for linear programming. We proposed an efficient order of preprocessing methods and data structures. We also noted the recovery process for dual solutions. We implemented the proposed preprocessing methods. and tested it with 28 large scale problems of NETLIB. We compared the results of it with those of preprocessing routines of HOPDM, BPDPM and CPLEX.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.669-690
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• 1998

In this paper we develop a new theory of adjoint and symmetric method in the class of general implicit one-step fixed-stepsize methods. These methods arise from simple and natral def-initions of the concepts of symmetry and adjointness that provide a fruitful basis for analysis. We prove a number of theorems for meth-ods having these properties and show in particular that only the symmetric methods possess a quadratic asymptotic expansion of the global error. In addition we give a very simple test to identify the symmetric methods in practice.

• Nonlinear Functional Analysis and Applications
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• v.27 no.4
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• pp.869-879
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• 2022

In this paper, we consider matrix optimization problems. We investigate augmented Lagrangian methods of multipliers and alternating direction methods of multipliers for the problems. Following the proofs of Eckstein [3], and Eckstein and Yao [5], we prove convergence theorems for augmented Lagrangian methods of multipliers and alternating direction methods of multipliers for the problems.

• Analytical Science and Technology
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• v.25 no.2
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• pp.102-113
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• 2012

Our study researched on unification of KS I ISO standard harmonized with ISO and Official Test Method enacted by Korean Ministry of Environment-drinking water and indoor air quality. We reviewed KS methods related to drinking water and indoor air quality for about 23,000 KS methods. KS methods related environmental field are classified as KS I, total 635 methods and 583 KS I methods were harmonized with ISO. For Environmental Standard methods for drinking water, 100 methods were reviewed according to 232 KS methods related to ISO/ TC 147 "Water Quality". Environmental Standard methods for indoor air quality were reviewed according to 95 KS standard methods related to ISO/TC 146 "Air Quality". By reviews and comparison tests for unifiable ES for drinking water and indoor air quality with KS methods harmonized with ISO, it was evaluated that for 100 ES methods for drinking water, 23 ES methods were unification complete, 29 ES methods were unification possible, 12 ES methods were unification impossible, no corresponding methods were found in KS I ISO for 36 ES methods and for 17 ES methods for indoor air quality,1 ES methods were unification complete, 3 ES methods were unification possible, 3 ES methods were impossible, no corresponding methods were found in KS I ISO for 10 ES methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.3
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• pp.243-291
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• 2020

In this paper, multi-block generalized backward differentiation methods for numerical solutions of ordinary differential and differential algebraic equations are introduced. This class of linear multi-block methods is implemented as multi-block boundary value methods (MB₂ VMs). The root distribution of the stability polynomial of the new class of methods are determined using the Wiener-Hopf factorization of a matrix polynomial for the purpose of their correct implementation. Numerical tests, showing the potential of such methods for output of multi-block of solutions of the ordinary differential equations in the new approach are also reported herein. The methods which output multi-block of solutions of the ordinary differential equations on application, are unlike the conventional linear multistep methods which output a solution at a point or the conventional boundary value methods and multi-block methods which output only a block of solutions per step. The MB₂ VMs introduced herein is a novel approach at developing very large scale integration methods (VLSIM) in the numerical solution of differential equations.

• Journal for History of Mathematics
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• v.11 no.1
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• pp.68-77
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• 1998

There are two approach methods widely in statistical inferences. First is sampling theory methods and the other is Bayesian methods. In this paper, we will introduce the most basic differences of the two approach methods. Especially, we investigate and introduce the historical origin of Bayesian methods in Statistical inferences which is currently used. Also, we introduce the some characteristics of sampling theory method and Bayesian methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.6 no.1
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• pp.1-15
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• 2002

Advection-dominated transport problems possess difficulties in the design of numerical methods for solving them. Because of the hyperbolic nature of advective transport, many characteristic numerical methods have been developed such as the classical characteristic method, the Eulerian-Lagrangian method, the transport diffusion method, the modified method of characteristics, the operator splitting method, the Eulerian-Lagrangian localized adjoint method, the characteristic mixed method, and the Eulerian-Lagrangian mixed discontinuous method. In this paper relationships among these characteristic methods are examined. In particular, we show that these sometimes diverse methods can be given a unified formulation. This paper focuses on characteristic finite element methods. Similar examination can be presented for characteristic finite difference methods.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.