• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.12
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• pp.1624-1634
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• 1997

A numerical study on two-dimensional unsteady transonic cascade flow has been performed by adopting dual time stepping and the k-.omega. turbulence model. An explicit 4 stage Runge-Kutta scheme for the compressible Navier-Stokes equations and an implicit Gauss-Seidel iteration scheme for the k-.omega. turbulence model are proposed for fictitious time stepping. This mixed time stepping scheme ensures the stability of numerical computation and exhibits a good convergence property with less computation time. Typical steady-state convergence accelerating schemes such as local time stepping, residual smoothing and multigrid combined with dual time stepping shows good convergence properties. Numerical results are presented for unsteady laminar flow past a cylinder and turbulent shock buffeting problem for bicircular arc cascade flow is discussed.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.219-232
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• 2022

The aim of this study is to provide the dynamic convergence index that reflected the inherent characteristics of the convergence phenomenon and utilized the nationally-funded R&D projects data, thereby suggesting useful information about the direction of the national convergence R&D strategy. The dynamic convergence index that we suggested was made of two indicators: persistency and diversity. From a time-series perspective, the persistency index, which measures the degree of continuous convergence of multidisciplinary nationally-funded R&D projects, and the diversity index, which measures the degree of binding with heterogeneous research areas. We conducted the empirical experiment with 151,248 convergence R&D projects during the 2015~2021 time period. The results showed that convergence R&D projects in both public health and life sciences appeared the highest degree of persistency. It was presumed that the degree of persistency has increased again due to the COVID-19 pandemic. Meanwhile, the degree of diversity has risen with combining with disciplinary such as materials, chemical engineering, and brain science areas to solve social problems including mental health, depression, and aging. This study not only provides implications for improving the concept and definition of dynamic convergence in terms of persistency and diversity for national convergence R&D strategy but also presented dynamic convergence index and analysis methods that can be practically applied for directing public R&D programs.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.187-192
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• 2004

본 논문에서는 시간지연 제어기와 관측기를 사용하여 산업용 로봇을 위한 고속 제어 방법을 설계하였다. 설계된 방법은 로봇의 매개변수 변화나 비선형이 존재하는 상황에서도 강인한 제어성능을 보이게끔 개발되었으며, 실제구현을 하였을 때도 계산량이 적으면서 동시에 구현이 쉬운 방법이다. 평면 2 자유도 스카라 로봇의 적용을 통하여 실험을 하였는데, 그 결과 실제 시스템에 효과적으로 적용될 수 있음 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.67-72
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• 2017

Deep learning neural network becomes very popular nowadays due to the reason that it can learn a very complex dataset such as the image dataset. Although deep learning neural network can produce high accuracy on the image dataset, it needs a lot of time to reach the convergence stage. To solve the issue, we have proposed a scaling method to improve the neural network to achieve the convergence stage in a shorter time than the original method. From the result, we can observe that our algorithm has higher performance than the other previous work.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.73-76
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• 2001

It is well-known that the convergence rate gets worse when an input signal to an adaptive filter is correlated. In this paper we propose a new adaptive filtering algorithm that makes the convergence rate highly improved even for highly correlated input signals. By introducing an orthogonal constraint between successive input signal vectors, we overcome the slow convergence problem caused by the correlated input signal. Simulation results show that the proposed algorithm yields highly improved convergence speed and excellent tracking capability under both time-invariant and time varying environments, while keeping both computation and implementation simple.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.1
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• pp.135-140
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• 2003

The convergence of finite volume method (FVM) or discrete ordinate method (DOM) is known to degrade for optical thickness greater than unity and large scattering albedo. The present article presents a convergence acceleration procedure for the FVM based on a full approximation storage (FAS) multigrid method. Among a variety of multigrid cycles, the V-cycle is used and the full multigrid algorithm (FMG) is applied to an analysis of radiation in irregular two-dimensional geometry. Solution convergence is discussed for the several cases of various optical thickness and scattering albedo. At small scattering albedo and optical thickness, there is no advantage to using the multigrid method for calculation CPU time. For large scattering albedo greater than 0.5 and optical thickness greater than unity, however, the multigrid method improves the convergence and the solution is rapidly obtained.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.80-95
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• 1993

Convergence measurements play very important role in the assessment of stability of a tunnel and of the economics of rock reinforcements. The characteristics of convergences are both due to the face advance effect and the time-dependent behaviour of rocks. As the convergence law can be modeled as a specific function of two variables of distance and time, we can determine the type of function and the related parameters from the field measurements. By using the regression method based on the Levengberg-Marquardt algorithm, an analysis of convergence of two different tunnels and one numerical example is described. It is shown that the convergence can be modeled as following function, C(x)=a{1-exp(-bx)} or C(t)=a{1-exp(-bt)} in case of a tunnel excavated in elastic rocks, in case of elasto-plastic or over stressed rocks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.117-120
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• 2008

This paper investigates the convergence condition of ADILC(iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.7-14
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• 2004

It is well known that preconditioning methods are efficient for convergence acceleration at compressible low Mach number flows. In this study, the original Euler equations and three preconditioners nondimensionalized differently are implemented in two dimensional inviscid bump flows using the 3rd order MUSCL and DADI schemes as flux discretization and time integration respectively. The multigrid and local time stepping methods are also used to accelerate the convergence. The test case indicates that a properly modified local preconditioning technique involving concepts of a global preconditioning one produces Mach number independent convergence. Besides, an asymptotic analysis for properties of preconditioning methods is added.