• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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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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• 제4권2호
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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.

• 한국경영과학회지
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• 제24권1호
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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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• 제5권3호
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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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• 제27권4호
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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.

• 분석과학
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• 제25권2호
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• pp.102-113
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• 2012

현행 환경오염공정시험기준 중 먹는 물 수질공정시험기준과 실내공기질 공정시험기준에 대해 국제규격에 부합화된 KS ISO규격과의 일원화에 관해 연구하였다. KS규격 23,000여종을 대상으로 먹는 물 및 실내공기질관련 KS 규격을 조사하였다. 환경분야관련 KS규격은 KS I로 분류되며, 총 653종으로 조사되었으며, 이중 국제규격에 부합화된 KS I 규격은 583종으로 조사되었다. 먹는 물 수질공정 시험기준은 총 100개 기준을 대상으로 ISO/TC 147 "Water Quality" 관련 KS 규격 총 232종을 비교 검토하였으며, 실내공기질 공정시험기준은 "ISO/TC 146 "Air Quality" 관련 KS 규격 총 95종 규격을 비교 검토하였다. 문헌검토와 비교시험을 통해 먹는 물 및 실내공기질 공정시험기준과 일원화 가능한 국제규격에 부합화된 KS규격 연구결과 먹는물수질공정시험 100개 기준 중 사전일원화 완료 23개 기준, 일원화 가능 29개 기준, 일원화 불가 12개 기준, 대응 규격 없는 기준이 36개로 평가되었으며, 실내공기질 공정시험기준 17개 기준 중 사전일원화 완료 1개 기준, 일원화 가능 3개 기준, 일원화 불가 3개 기준, 대응규격 없는 기준이 10개 기준으로 평가되었다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권3호
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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.

• 한국수학사학회지
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• 제11권1호
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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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• 제6권1호
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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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• 제30권2호
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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.