• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.5
• /
• pp.848-860
• /
• 1994

Generally, to get the minimum distance between two arbitrary shapes that are composed of circular arcs and lines, we must calculate distances for all the possible pairs of the components from two given shapes. In this paper, we propose an efficient method for the minimum distance problem between two shapes by using their structural features after extracting the reduced component lists which are essential to calculate the minimum distance considering the relationship of shape location. Even though the reduced component lists may contain all the components of the shapes in the worst case, in the average we can reduce the required computation much by using the reduced component lists. This method may be efectively applied to calculating the minimum distance between two shapes which are generated by the CAD tool, like in the nesting system.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.11
• /
• pp.130-136
• /
• 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
• /
• 1997.10a
• /
• pp.1876-1879
• /
• 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
• /
• v.16 no.6
• /
• pp.989-995
• /
• 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.

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.52-59
• /
• 2011

In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.

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

• Steel and Composite Structures
• /
• v.37 no.4
• /
• pp.463-479
• /
• 2020

Currently, there are many lateral resisting systems utilized in resisting lateral loads being produced in an earthquake. Such systems can significantly reduce the roof's displacement when placed at an optimum location. Since in the design of tall buildings, the minimum distance between adjacent buildings is important. In this paper, the critical excitation method is used to determine the best location of the belt truss system while calculating the minimum required distance between two adjacent buildings. For this purpose, the belt truss system is placed at a specific story. Then the critical earthquakes are computed so that the considered constraints are satisfied, and the value of roof displacement is maximized. This procedure is repeated for all stories; i.e., for each, a critical acceleration is computed. From this set of computed roof displacement values, the story with the least displacement is selected as the best location for the belt truss system. Numerical studies demonstrate that absolute roof displacements induced through critical accelerations range between 5.36 to 1.95 times of the San Fernando earthquake for the first example and 7.67 to 1.22 times of the San Fernando earthquake for the second example. This method can also be used to determine the minimum required distance between two adjacent buildings to eliminate the pounding effects. For this purpose, this value is computed based on different standard codes and compared with the results of the critical excitation method to show the ability of the proposed method.

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

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.967-976
• /
• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

• Journal of Ocean Engineering and Technology
• /
• v.20 no.6 s.73
• /
• pp.82-87
• /
• 2006

Numerical analysis is performed on the wave transmission coefficient of various crown widths of the double-submerged breakwater and the triple-submerged breakwater, varying the distance between submerged breakwaters. The finite element method is used, and the fluid motion is considered as linearized two-dimensional potential flow. In case of the double- and triple-submerged breakwaters, as the width of submerged breakwater increases, the minimum wave transmission coefficient decreases and the wave period at which the minimum wave transmission coefficient occurs moves to a longer wave period the distance between submerged breakwaters at which the minimum wave transmission coefficient occurs becomes larger.