• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.130-136
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• 1998

This paper develops an efficient algorithm for the minimum distance calculation between two general polyhedra(convex and/or concave) in three-dimensional space. The polyhedra approximate objects using flat polygons which composed of more than three vertices. The algorithm developed in this paper basically computes minimum distance between two polygons(one polygon per object) and finds a set of two polygons which makes a global minimum distance. The advantage of the algorithm is that the global minimum distance can be computed in any cases. But the big disadvantage is that the minimum distance computing time is rapidly increased with the number of polygons which used to approximate an object. This paper develops a method to eliminate sets of two polygons which have no possibility of minimum distance occurrence, and an efficient algorithm to compute a minimum distance between two polygons in order to compensate the inherent disadvantage of the algorithm. The correctness of the algorithm is verified not only comparing analytically calculated exact minimum distance with one calculated using the developed algorithm but also watching a line which connects two points making a global minimum distance of a convex object and/or a concave object. The algorithm efficiently finds minimum distance between two convex objects made of 224 polygons respectively with a computation time of about 0.1 second.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1876-1879
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• 1997

This paper developes an efficient algorithm for the minimum distance calculation between general polyhedra(convex and/or concave). The polyhedron approximates and object using flat polygons which composed of more than three veritices. The algorithm developed in this paper basically computes minimun distance betwen two convex polygons and finds a set of polygons whcih makes a global minimum distance. The advantage of the algorithm is that the global minimum distance can be computed in any cases. But the big disadvantage is that minimum distance computing time is repidly increased with the number of polygons which used to approximate an object. This paper developes a method to eliminate unnecessary sets of polygons, and an efficinet algorithm to compute a minimum distance between two polygons in order to compensate the inherent disadvantage of the algorithm. It takes only a few times iteration to find minimum distance for msot polygons. The correctness of the algortihm are visually tested with a line which connects two points making a global minimum distance of simple convex object(box) and concave object(pipe). The algorithm can find minimum distance between two convex objects made of about 200 polygons respectively less than a second computing time.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.989-995
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• 2009

Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the painful kernel density estimator. Their proposed class of density power divergences is indexed by a single parameter $\alpha$ which controls the trade-off between robustness and efficiency. In this article, (1) we introduce a new large class the minimum squared distance which includes from the minimum Hellinger distance to the minimum $L_2$ distance. We also show that under certain conditions both the minimum density power divergence estimator(MDPDE) and the minimum squared distance estimator(MSDE) are asymptotically equivalent and (2) in finite samples the MDPDE performs better than the MSDE in general but there are some cases where the MSDE performs better than the MDPDE when estimating a location parameter or a proportion of mixed distributions.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.687-696
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• 2003

This article presents a new family of the estimating functions related with minimum distance estimations, and discusses its relationship to the family of the minimum density power divergence estimating equations. Two representative minimum distance estimations; the minimum $L_2$ distance estimation and the minimum Hellinger distance estimation are studied in the light of the theory of estimating equations. Despite of the desirable properties of minimum distance estimations, they are not widely used by general researchers, because theories related with them are complex and are hard to be computationally implemented in real problems. Hopefully, this article would be a help for understanding the minimum distance estimations better.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.213-228
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• 2002

Asymptotic properties of minimum distance estimators for the parameter involved for a class of stochastic partial differential equations are investigated following the techniques in Kutoyants and Pilibossian (1994).

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.213-220
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• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.283-293
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• 2010

In this paper, inverse constrained minimum spanning tree problem under Hamming distance. Such an inverse problem is to modify the weights with bound constrains so that a given feasible solution becomes an optimal solution, and the deviation of the weights, measured by the weighted Hamming distance, is minimum. We present a strongly polynomial time algorithm to solve the inverse constrained minimum spanning tree problem under Hamming distance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10A
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• pp.1576-1581
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• 2000

We investigated into the minimum distance of a primitive binary cyclic code C with a generator polynomial g(x)=$m_1(x)m_{d}(x)$. It is known that the necessary and sufficient condition for C to have minimum distance five is the fact that \ulcorner is an APN power function. In this paper we derived the new proof of minimum distance for the primitive binary cyclic codes with minimum distance five.

• Journal of Broadcast Engineering
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• v.27 no.7
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• pp.974-984
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• 2022

In this paper, we introduce the minimum and maximum distance setting methods used in geometric distance attenuation processing, which is one of spatial sound reproduction methods. In general, sound attenuation by distance is inversely proportional to distance, that is 1/r law, but when the relative distance between the user and the audio object is very short or long, exceptional processing might be performed by setting the minimum distance or the maximum distance. While MPEG-I Immersive Audio's RM0 uses fixed values for the minimum and maximum distances, this study proposes effective methods for setting the distances considering the signal gain of an audio object. Proposed methods were verified through simulation of the proposed methods and experiments using RM0 renderer.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.507-514
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• 1998

The minimum distance estimation based on the L$_2$ distance between a model density and a density estimator is studied from M-estimation point of view. We will show that how a model density and a density estimator are incorporated in order to create an M-estimation function. This method enables us to create an M-estimating function reflecting the natures of both an assumed model density and a given set of data. Some new types of M-estimation functions for estimating a location and scale parameters are introduced.