• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.651-659
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• 2001

Preference map is a widely used graphical method for the preference data set which is frequently encountered in the field of marketing research. This provides joint configuration usually in two dimensional space between "products" and their "attributes". Whereas the classical preference map adopts the ordinary least squares method in deriving map, the present article suggests the weighted least squares approach providing the better graphical display and interpretation compared to the classical one. Internet search engine data in Korea are analysed for illustration.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.482-496
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• 2008

Packet classification in the Internet routers requires multi-dimensional search for multiple header fields for every incoming packet in wire-speed, hence packet classification is one of the most important challenges in router design. Hierarchical packet classification is one of the most effective solutions since search space is remarkably reduced every time a field search is completed. However, hierarchical structures have two intrinsic issues; back-tracking and empty internal nodes. In this paper, we propose a new hierarchical packet classification algorithm which solves both problems. The back-tracking is avoided by using the set-pruning and the empty internal nodes are avoided by applying the binary search tree. Simulation result shows that the proposed algorithm provides significant improvement in search speed without increasing the amount of memory requirement. We also propose an optimization technique applying controlled rule copy in set-pruning.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2476-2481
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• 2007

An inverse radiation analysis is presented for the estimation of the wall emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. In this study, a repulsive particle swarm optimization(RPSO) algorithm which is a relatively recent heuristic search method is proposed as an effective method for improving the search efficiency for unknown parameters. To verify the performance of the proposed RPSO algorithm, it is compared with a basic particle swarm optimization(PSO) algorithm and a hybrid genetic algorithm(HGA) for the inverse radiation problem with estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. A finite-volume method is applied to solve the radiative transfer equation of a direct problem to obtain measured temperatures.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.3
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• pp.119-123
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• 2010

Since the two-way interactive broadcasting service began, remote controllers have been fitted with 4 color buttons, which enable interaction and convenience to grow between users and content. With Currently, diverse studies on IPTV are in progress. Particularly, as the mobile market rapidly grows, studies on mobile IPTV and on linkage with other media are constantly increasing. Yet, mobile IPTV has never been studied as of now. In that sense, the present study attempted to design a mobile-based IPTV UI that is fitted with more usability and functionality of 4 color buttons and multi-dimensional search based on consistent criteria for content search. The UI designed in this study was estimated using user interface design guideline. The guideline is comprised of consistency, user centered, ease of use, forgiveness, feedback, functionality, aesthetic integrity.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.297-304
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• 1997

In stereo vision, camera modeling is very important because the accuracy of the three dimensional locations depends considerably on it. In the existing stereo camera models, two camera planes are located in the same plane or on the optical axis. These camera models cannot be used in the active vision system where it is necessary to obtain two stereo images simultaneously. In this paper, we propose four kinds of stereo camera models for active stereo vision system where focal lengths of the two cameras are different and each camera is able to rotate independently. A single closed form solution is obtained for all models. The influence of the stereo camera model to the field of view, occlusion, and search area used for matching is shown in this paper. And errors due to inaccurate focal length are analyzed and simulation results are shown. It is expected that the three dimensional locations of objects are determined in real time by applying proposed stereo camera models to the active stereo vision system, such as a mobile robot.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.103-121
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• 2011

This study investigated how spatial dimension information and dimensional consistency between learning and testing phase would influence the target search performance. The participants learned spatial layouts of Lego blocks shown in either two- (2D) or three-dimension (3D) and were tested with the rotated stimuli ($0^{\circ}$, $90^{\circ}$, $180^{\circ}$, or $270^{\circ}$ from the initial view) in consistent or inconsistent dimension. Significantly better performance was observed when initial learning display appeared in 2D than in 3D. Particularly, the participants showed difficulties in flexible usage of spatial information presented in 3D especially if the dimensional information in the testing phase also was 3D and required mental rotation. The present study indicates that spatial map presented in 2D may be more useful than 3D in driving situations in which acquired spatial information from navigating device, such as GPS, and location of driver continuously changes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.971-980
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• 2010

In this paper, we present a navigation control algorithm for mobile robots that move in environments having static and moving obstacles. The algorithm includes a global and a local path-planning algorithm that uses $D^*$ search algorithm, a fuzzy logic for determining the immediate level of danger due to collision, and a fuzzy logic for evaluating the required wheel velocities of the mobile robot. To apply the $D^*$ search algorithm, the two-dimensional space that the robot moves in is decomposed into small rectangular cells. The algorithm is verified by performing simulations using the Python programming language as well as by using the dynamic equations for a two-wheeled mobile robot. The simulation results show that the algorithm can be used to move the robot successfully to reach the goal position, while avoiding moving and unknown static obstacles.

• Progress in Medical Physics
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• v.11 no.1
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• pp.49-57
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• 2000

New method about the dose optimization problem in radiation treatment was researched. Since all conditions are more complex and there are more relevant variables, the solution of three-dimensional treatment planning is much more complicate than that of current two-dimensional one. There(ore, in this study, as a method to solve three-dimensional dose optimization problem, the considered variables was minized and researched by reducing the domain that solutions can exist and pre-determining the important beam parameters. First, the dangerous beam range that passes critical organ was found by coordinate transformation between linear accelerator coordinate and patient coordinate. And the beam size and rotation angle for rectangular collimator that conform tumor at arbitrary beam position was also determined. As a result, the available beam position could be reduced and the dependency on beam size and rotation angle, that is very important parameter in treatment planning, totally removed. Therefore, the resultant combinations of relevant variables could be greatly reduced and the dose optimization by objective function can be done with minimum variables. From the above results, the dose optimization problem was solved for the two-dimensional radiation treatment planning useful in clinic. The objective function was made by combination of dose gradient, critical organ dose and dose homogeniety. And the optimum variables were determined by applying step search method to objective function. From the dose distributions by optimum variables, the merit of new dose optimization method was verified and it can be implemented on commercial radiation treatment planning system with further research.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.324-329
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• 2005

Dynamic Encoding Algorithm for Searches (DEAS) which is known as a fast and reliable non-gradient optimization method, was proposed [1]. DEAS reaches local or global optimum with binary strings (or binary matrices for multi-dimensional problem) by iterating the two operations; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., 0 or 1), while UDS performs increment or decrement of binary strings in the BSS' result direction with no change of string length. Because the interval of UDS exponentially decreases with increment of bit string length (BSL), DEAS is difficult to escape from local optimum when DEAS falls into local optimum. Therefore, this paper proposes hopping UDS (HUDS) which performs UDS by hopping as many as BSL in the final point of UDS process. HUDS helps to escape from local optimum and enhances a probability searching global optimization. The excellent performance of HUDS will be validated through the well-known benchmark functions.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.