• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.25-32
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• 1990

This paper presents an approximate algorithm for computing Fault-tree probabilities. The method is essentially composed of three steps. In the first step, a Fault-tree is converted into a network form. In the second step, We change the network into a parallelized diagram. In the third step, the approximate fault-tree probability is calculated from the parallelized diagram. In this paper, in order to verify the method two hypothetical Fault-tree is used by examples. The results show that the method is very useful, even though it is an approximate technique, since it needs not to search the minimal cut sets and has the simple computing rontines.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.647-652
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• 2000

This paper presents a study on the position tracking control of robot system on the uncertain elastic base. The elastic base is modeled as a virtual robot which has passive joints and the control strategy is using approximate Jacobian operators. Jacobian operators represent the overall robot system including base movement. However, because we don't know the base movement we can't estimate the jacobian operators directly. The control algorithm is proposed which uses only Jacobian operators of a real robot as approximate Jacobian operators. The measured errors from external sensor are compensated by approximate Jacobian operators. The simulation results of a single-axis robot system show that the control strategy can be used for position tracking.

• Structural Engineering and Mechanics
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• v.8 no.1
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• pp.1-18
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• 1999

Procedures are investigated by which nonlinear finite element shell analysis algorithms can be simplified to provide more cost effective approximate analyses of orthogonally-reinforced concrete flat plate structures. Two alternative effective stiffness formulations, and an unbalanced force formulation, are described. These are then implemented into a nonlinear shell analysis algorithm. Nonlinear geometry, three-dimensional layered stress analyses, and other general formulations are bypassed to reduce the computational burden. In application to standard patch test problems, these simplified approximate analysis procedures are shown to provide reasonable accuracy while significantly reducing the computational effort. Corroboration studies using various simple and complex test specimens provide an indication of the relative accuracy of the constitutive models utilized. The studies also point to the limitations of the approximate formulations, and identify situations where one should revert back to full nonlinear shell analyses.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.437-447
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• 2003

In order to reduce the increasing packet loss rates caused by an exponential increase in network traffic, the IETF(Internet Engineering Task Force) is considering the deployment of active queue management techniques such as RED(Random Early Detection) algorithm. However, RED algorithm simple but does not protect traffic from high-bandwidth flows, which include not only flows that fail to use end-to-end congestion control such as UDP flow, but also short round-trip time TCP flows. In this paper, in order to solve this problem, we propose a simple fairness queue management scheme, called AFQM(Approximate Fair Queue Management) algorithm, that discriminate against the flows which submit more packets/sec than is allowed by their fair share. By doing this, the scheme aims to approximate the fair queueing policy Since it is a small overhead and easy to implement, AFQM algorithm controls unresponsive or misbehaving flows with a minimum overhead.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.103-118
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• 2024

The horseshoe prior is notably one of the most popular priors in sparse regression models, where only a small fraction of coefficients are nonzero. The parameter space of the horseshoe prior is much smaller than that of the spike and slab prior, so it enables us to efficiently explore the parameter space even in high-dimensions. However, on the other hand, the horseshoe prior has a high computational cost for each iteration in the Gibbs sampler. To overcome this issue, various MCMC algorithms for the horseshoe prior have been proposed to reduce the computational burden. Especially, Johndrow et al. (2020) recently proposes an approximate algorithm that can significantly improve the mixing and speed of the MCMC algorithm. In this paper, we compare (1) the traditional MCMC algorithm, (2) the approximate MCMC algorithm proposed by Johndrow et al. (2020) and (3) its variant in terms of computing times, estimation and variable selection performance. For the variable selection, we adopt the sequential clustering-based method suggested by Li and Pati (2017). Practical performances of the MCMC methods are demonstrated via numerical studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.105-108
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• 2001

In this paper, the learning algorithm called advanced expanded and truncate algorithm(AETLA) is proposed to training multilayer binary neural network to approximate binary to binary mapping. AETLA used merit of ETL and MTGA learning algorithm. We proposed to new learning algorithm to decrease number of hidden layer. Therefore, learning speed of the proposed AETLA learning algorithm is much faster than other learning algorithm.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.195-205
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• 2004

To the unconstrained programme of non-convex function, this article give a modified BFGS algorithm. The idea of the algorithm is to modify the approximate Hessian matrix for obtaining the descent direction and guaranteeing the efficacious of the quasi-Newton iteration pattern. We prove the global convergence properties of the algorithm associating with the general form of line search, and prove the quadratic convergence rate of the algorithm under some conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.21-28
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• 1999

The TSP(Traveling Salesman Problem) is one of the most widely studied problems in combinatorial optimization. The most common interpretation of TSP is finding a shortest Hamiltonian tour of all cities. The objective of this paper proposes a new heuristic algorithm MCH(Multi-Convex hulls Heuristic). MCH is a algorithm for finding good approximate solutions to practical TSP. The MCH algorithm is using the characteristics of the optimal tour. The performance results of MCH algorithm are superior to others algorithms (NNH, CCA) in CPU time.

• The Journal of Korea Robotics Society
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• v.11 no.2
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• pp.92-99
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• 2016

An electro-hydraulic actuator (EHA) is widely used in industrial motion systems and the increasing bandwidth of EHA position control is important issue. The model-inverse feedforward controller is known to extend the bandwidth of system. When the system has non-minimum phase (NMP) zeros, direct model inversion makes system unstable. To overcome this problem, an approximate model-inverse method is used. A representative approximate model inversion method is zero phase error tracking control (ZPETC). However, if zeros locate right half plane of z-plane, the approximate inverse model amplifies the high-frequency response. In this paper, to solve the problem of ZPETC, an adaptive model-inverse control is proposed. The adaptive algorithm updates feedforward term in real-time. The effectiveness of the proposed adaptive model-inverse position control strategy is verified by comparison with typical proportional-integral (PI) control and feedforward control by experiments. As a result, the proposed adaptive controller extends the bandwidth of EHA position control.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.137-146
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• 2017

For the competitive business environment under purchase dependence, this paper proposes a new approximate calculation of order fill rate which is a probability of satisfying a customer order immediately using the existing inventory. Purchase dependence is different to demand dependence. Purchase dependence treats the purchase behavior of customers, while demand dependence considers demand correlation between items, between regions, or over time. Purchase dependence can be observed in such areas as marketing, manufacturing systems, and distribution systems. Traditional computational methods have a difficulty of the curse of dimensionality for the large cases, when deriving the stationary joint distribution which is utilized to calculate the order fill rate. In order to escape the curse of dimensionality and protect the solution from diverging for the large cases, we develop a greedy iterative search algorithm based on the Gauss-Seidel method. We show that the greedy iterative search algorithm is a dependable algorithm to derive the stationary joint distribution of on-hand inventories in the retailer system by conducting a comparison analysis of a greedy iterative search algorithm with the simulation. In addition, we present some managerial insights such as : (1) The upper bound of order fill rate can be calculated by the one-item pure system, while the lower bound can be provided by the pure system that consists of all items; (2) As the degree of purchase dependence declines while other conditions remain same, it is observed that the difference between the lower and upper bounds reduces, the order fill rate increases, and the order fill rate gets closer to the upper bound.