• The Transactions of the Korea Information Processing Society
• /
• v.7 no.6
• /
• pp.1867-1873
• /
• 2000

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Support vector machine (SVM) is a new and very promising classification, regression and function approximation technique developed by Vapnik and his group at AT&TG Bell Laboratories. However, it has failed to establish itself as common machine learning tool. This is partly due to the fact that this is not easy to implement, and its standard implementation requires the use of optimization package for quadratic programming (QP). In this appear we present simple iterative Kernel Adatron (KA) algorithm for function approximation and compare it with standard SVM algorithm using QP.

• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.7_8
• /
• pp.387-396
• /
• 2003

This paper provides an approximation algorithm for STP-MSP(Steiner Tree Problem with minimum number of Steiner Points).Because it seems to be impossible to have a PTAS(Polynomial Time Approximation Schemes), which gives the near optimal solutions, for the problem, the algorithm of this paper is an alternative that has the approximation ratio 2 with $n^{O(1)}$ run time. The importance of this paper is the potential to solve other related unsolved problems. The idea of this paper is to distribute the error allowance over the problem instance so that we may reduce the run time to polynomial bound out of infinitely many cases. There are earlier works[1,2] that show the approximations that have practical run times with the ratio of bigger than 2, but this paper shows the existence of a poly time approximation algorithm with the ratio 2.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.270-273
• /
• 2017

Productivity through reduced defects in plastic injection molding and reduced cycle times is a long-standing need in the injection industry. In particular, productivity is very urgent for the domestic injection industry, which is caught between the pursuit of latecomers such as China and technological gap with Germany and Japan which will not be narrowed down. Through 30 years of research and experience in the domestic injection industry, we have found that controlling the surface temperature of injection molds is the key of quality control. There have been various attempts to utilize advanced control techniques such as PID control, but the productivity against leading companies in Germany and Japan is still insufficient. Using Approximation Algorithm - "Knapsack" and "Minimum Makespan Scheduling", We want to show how to efficiently control objects with periodic repetitive data patterns that are difficult to solve with PID control. In addition, We want to propose that the control by Approximation Algorithm is effective enough to improve the productivity of the product by analyzing the data extracted from actual injection site.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.37-39
• /
• 2005

본 논문에서는 기존의 밀도 기반 전지 클러스터링 알고리즘의 성능을 개선한 밀도 기반 클러스터링의 근사적 접근법을 제안한다. 기존의 밀도 기반 전지 알고리즘은 다차원 색인의 많은 검색 공간을 빠르게 전지하면서도 원하는 클러스터를 정확히 찾아내는 특징을 가지고 있다. 그러나 기존 알고리즘은 전지를 위한 한계 값 설정을 위하여 단말 영역들의 밀도 값을 사용함으로써, 내부 영역에 속한 단말 영역들 간의 밀도 편차가 큰 경우 전지 여부에 대한 판별이 빨리 이루어지지 않는다. 또한, 최악의 경우에는 모든 단말 페이지를 검색하여야 하고, 이에 따라 성능이 저하될 수 있다. 반면에 제안하는 근사적 접근법에서는 한계 값 설정을 위해 단말 영역이 아닌 내부 영역의 밀도 값을 사용한다. 일반적으로, 내부 영역들 간의 밀도 편차는 단말 영역들 간의 밀도 편차보다 크지 않으므로, 근사 밀도 기반 전지 알고리즘에서는 더욱 많은 검색 공간의 전지 여부의 빨리 판별할 수 있게 된다. 성능 평가 실험을 수행한 결과, 제안한 알고리즘은 기존의 알고리즘과 비교하여 정확성 측면에서는 큰 차이가 없는 반면 수행 시간 측면에서는 최대 $17\%$의 성능 향상 효과가 있는 것으로 나타났다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37A no.9
• /
• pp.738-745
• /
• 2012

This paper proposes an approximated soft decision demapping algorithm with low computational complexity for coded 4+12+16 amplitude phase shift keying (APSK) in an additive white Gaussian noise (AWGN) channel. To derive the proposed algorithm, we approximate the decision boundaries for 4+12+16 APSK symbols, and then decide the log likelihood ratio (LLR) value for each bit from the approximated decision boundaries. Although the proposed algorithm shows about 0.6~1.1dB degradation on the error performance compared with the conventional max-log algorithm, it gives a significant result in terms of the computational complexity.

• Proceedings of the Korean Information Science Society Conference
• /
• 1998.10b
• /
• pp.670-672
• /
• 1998

반복되는 형태의 문자열은 많은 분야에서 연구되고 있다. 반복적인 문자열 형태의 한 종류인 주기(period)는 주어진 문자열을 x라 할 때, x가 pn의 접두어(prefix)가 되는 p를 말한다. 본 논문에서는 근사주기(approximate period)의 개념을 제시하고, 길이가 각각 n, m인 두 문자열 x, p가 주어졌을 때, p가 x의 근사주기가 되는 최소의 오차를 O(m2n) 시간에 찾는 알고리즘을 제시한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10a
• /
• pp.718-720
• /
• 2004

n개의 점과 이를 포함하는 직사각형 B가 주어졌을 때 B내에서 어떤 점도 포함하지 않는 가장 큰 직사각형을 찾는 선형 근사 알고리즘을 제시한다. 찾게 되는 직사각형은 좁고 긴 모양의 것은 피하며 직사각형 B의 변에 평행해야 할 필요는 없다. 모든 방향을 고려하여 가장 큰 직사각형을 근사율 (1+$\alpha$)로 O(n + (area(B)/($\alpha$l)$^2$) log$^2$($\alpha$/$\varepsilon$)) 시간 내에 구한다.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.05c
• /
• pp.320-324
• /
• 2002

본 연구에서는 모멘트와 뉴우턴법 및 모멘트와 할선법에 각각 기초한 고정점 알고리즘의 신경망 기반 독립성분분석 기법을 제안하였다. 여기서 뉴우턴법과 할선법은 각각 엔트로피에 기초한 목적함수의 근을 구하는 근사화 방법으로 빠른 경신을 위함이고, 모멘트는 근사화에 의한 역혼합행렬의 경신과정에서 발생하는 발진을 줄여 좀 더 빠른 학습을 위함이다. 제안된 기법을 256×256 픽셀(pixel)의 8개 지문영상으로부터 임의의 혼합행렬에 따라 발생되는 영상들을 각각 대상으로 시뮬레이션 한 결과, 모멘트와 할선법에 기초한 알고리즘이 모멘트와 뉴우턴에 기초한 알고리즘보다 우수한 분리성능과 빠른 학습속도가 있음을 확인하였다.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.3
• /
• pp.747-753
• /
• 1997

Function approximation from a set of input-output parirs has numerous applications in scientiffc and engineer-ing areas.Multiayer feedforward neural networks have been proposed as a good approximator of noninear function.The back propagation (BP) algorithm allows muktiayer feedforward neural networks oro learn input-output mappongs from training samples.However, the mapping acquired through the BP algorithm nay be cor-rupt when errorneous trauning data are employed.In this paper we propose a robust BP learning algorithm that is resistant to the errormeous data and is capable of rejecting gross errors during the approximation process.