• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.583-592
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• 2007

Given the moments of a symmetric random variable, its density and distribution functions can be accurately approximated by making use of the algorithm proposed in this paper. This algorithm is specially designed for approximating symmetric distributions and comprises of four phases. This approach is essentially based on the transformation of variable technique and moment-based density approximants expressed in terms of the product of an appropriate initial approximant and a polynomial adjustment. Probabilistic quantities such as percentage points and percentiles can also be accurately determined from approximation of the corresponding distribution functions. This algorithm is not only conceptually simple but also easy to implement. As illustrated by the first two numerical examples, the density functions so obtained are in good agreement with the exact values. Moreover, the proposed approximation algorithm can provide the more accurate quantities than direct approximation as shown in the last example.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.21-30
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• 2013

Powell-Miller theory is a good method to express or treat incorrect information. But it has limitation that requires too much time to apply to actual situation because computational complexity increases in exponential and functional way. Accordingly, there have been several attempts to reduce computational complexity but side effect followed - certainty factor fell. This study suggested expanded Approximation Algorithm. Expanded Approximation Algorithm is a method to consider both smallest supersets and largest subsets to expand basic space into a space including inverse set and to reduce Approximation error. By using expanded Approximation Algorithm suggested in the study, basic probability assignment function value of subsets was alloted and added to basic probability assignment function value of sets related to the subsets. This made subsets newly created become Approximation more efficiently. As a result, it could be known that certain function value which is based on basic probability assignment function is closely near actual optimal result. And certainty in correctness can be obtained while computational complexity could be reduced. by using Algorithm suggested in the study, exact information necessary for a system can be obtained.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.380-390
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• 1994

In this paper, we propose an equal-cost bifurcated routing algorithm which may be useful in practical computer network design problem. The performance of the routing algorithm is evaluated using the conventional Monte Carlo simulation and a transient queueing approximation. The relative errors between the closure approximation and the Monte Carlo simulation was fairly small. The closure approximation may be used to evaluate the performance of the load splitting algorithms, which results in considerable execution time reduction. The performance of the proposed algorithm is compared to that of the known algorithms based on average packet delay. For networks that have many non-disjoint equal-paths, the proposed algorithm performed better than other algorithms.

• Bulletin of the Korean Mathematical Society
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• v.38 no.2
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• pp.293-302
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• 2001

We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1\sqrt{\n}$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1231-1238
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• 2015

In this paper, we proposed an efficient algorithm using Node monitoring (NM) and Piecewise Linear Function Approximation(: NP) for reducing the complexity of LDPC code decoding. Proposed NM algorithm is based on a new node-threshold method together with message passing algorithm. Piecewise linear function approximation is used to reduce the complexity of the algorithm. This new algorithm was simulated in order to verify its efficiency. Complexity of our new NM algorithm is improved to about 20% compared with well-known methods according to simulation results.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.28-34
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• 2020

In this paper, we introduce motion artifact reduction algorithm for interleaved MRI using an advanced 3D approximation algorithm. The motion artifact framework of this paper is data corrected by post-processing with a new 3-D approximation algorithm which uses data structure for each voxel. In this study, we simulate and evaluate our algorithm using Shepp-Logan phantom and T1-MRI template for both scattered dataset and uniform dataset. We generated motion artifact using random generated motion parameters for the interleaved MRI. In simulation, we use image coregistration by SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) to estimate the motion parameters. The motion artifact correction is done with using full dataset with estimated motion parameters, as well as use only one half of the full data which is the case when the half volume is corrupted by severe movement. We evaluate using numerical metrics and visualize error images.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.443-450
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• 2016

This paper deals with a kind of packing problem of which the goal is to compose one or more templates which will be used to produce the items of different types. Each template consists of a fixed number of slots which are assigned to the different types of items and the production of the items is accomplished by printing the template repeatedly. The objective is to minimize the total number of produced items. This problem is known to be NP-hard. We present a polynomial time approximation algorithm which has a constant approximation ratio. The proposed algorithm is based on the well-known first-fit strategy.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.129-140
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• 2020

In this paper, we introduce an efficient algorithm for the non-convex penalized multinomial logistic regression that can be uniformly applied to a class of non-convex penalties. The class includes most non-convex penalties such as the smoothly clipped absolute deviation, minimax concave and bridge penalties. The algorithm is developed based on the concave-convex procedure and modified local quadratic approximation algorithm. However, usual quadratic approximation may slow down computational speed since the dimension of the Hessian matrix depends on the number of categories of the output variable. For this issue, we use a uniform bound of the Hessian matrix in the quadratic approximation. The algorithm is available from the R package ncpen developed by the authors. Numerical studies via simulations and real data sets are provided for illustration.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.20-26
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• 2011

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. Piecewise linear function approximation is used to reduce the complexity for more. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is reduced to about 20%, compared with thoes of well-known method.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.514-519
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• 2009

The projection approximation subspace tracking (PAST) is one of the attractive subspace tracking algorithms, because it estimates the signal subspace adaptively and continuously. Furthermore, the computational complexity is relatively low. However, the algorithm still has room for improvement in the subspace estimation accuracy. FE-PAST (Forward-Backward PAST) is one of the results from the improvement studies. In this paper, we propose a new algorithm to improve the orthogonality of the FB-PAST (Forward-Backward PAST).