• Journal of Information Processing Systems
• /
• v.5 no.4
• /
• pp.237-242
• /
• 2009

The classical vehicle routing problem (VRP) can be extended by including customers who want to send goods to the depot. This type of VRP is called the vehicle routing problem with pickups and deliveries (VRPPD). This study proposes a novel way to solve VRPPD by introducing a two-phase heuristic routing algorithm which consists of a clustering phase and uses the geometrical center of a cluster and route establishment phase by applying a two-way search of each route after applying the TSP algorithm on each route. Experimental results show that the suggested algorithm can generate better initial solutions for more computer-intensive meta-heuristics than other existing methods such as the giant-tour-based partitioning method or the insertion-based method.

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.32-39
• /
• 2007

An important application domain for online services is an interactive, multi-player game. In recent, many increase of users that use on-line services through networks have caused a heavy load to the server. In this paper, we propose a MMORPG(Massively Multi-player Online Role Playing Game) distributed game server using flayer-Cell. Our method provides efficient solution of a MMORPG distributed game server for large numbers of users. It is shown through the experiments that our method outperforms existing methods in terms of memory utilization rate and processing speed.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.421-426
• /
• 2006

We propose a new algorithm for K-medoids clustering which runs like the K-means clustering algorithm and test several methods for selecting initial medoids. The proposed algorithm calculates similarity matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm we use real and artificial data and compare with the clustering results of other algorithms in terms of three performance measures. Experimental results show that the proposed algorithm takes the reduced time in computation with comparable performance as compared to the Partitioning Around Medoids.

• Journal of Korea Multimedia Society
• /
• v.4 no.5
• /
• pp.465-475
• /
• 2001

Recent1y, there is growing attention on the researches of inducing association rules from large volume of database. One of them is the method that can be applied to quantitative attribute data. This paper presents a new method for generating large-interval itemsets, which uses locality for partitioning the range of data. This method can minimize the loss of data-inherent characteristics by generating denser large-interval items than other methods. Performance evaluation results show that our new approach is more efficient than previously proposed techniques.

• The Korean Journal of Applied Statistics
• /
• v.2 no.1
• /
• pp.35-47
• /
• 1989

This article deals with the method of ML among the methods of estimating m gene frequenecies in the Generalized ABO-like Blood Group Systems and with the statistical testing about the differencies of gene frequencies by using these estimators. Especially, the generalization about the Homogeneity testing problem is tried and thus it enables us to test of Homogeneity of m gene frequencies. Finally, in the example, ML estimator is compared with other estimators suggested by Bernstein method, by adjusted Bernstein method and by modified Bernstein method, and statistical testing in the above is carried out by using orthogonal partitioning.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.14-19
• /
• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.518-526
• /
• 1986

This paper gives the detail of the work carried out to develop real-time control software for Flexible Manufacturing Systems. A basic design philosophy to implement such software is proposed. The major features are the partitioning of complicated control actions into simplified ones, structured programming and multi-threaded transaction-based tasks. The software operates on the basis of passing task-to-task messages via mailboxes, causing appropriate actions to be taken by each task. Each task represents a separate subprocess so that the subprocesses can be run simultaneously. The task-to-task message could be easily replaced by computer-to-computer communication, using LAN, demonstrating that the software methods developed produce a flexible designs for control software of an FMS. A method of linking such software to simulation software is suggested as a potentially powerful additional design-tool.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2395-2398
• /
• 2003

This paper focuses on lossless medical image compression methods for medical images that operate on two-dimensional(2D) reversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm [1][3][9] to medical images, using a 2D wavelet decomposition and a 2D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling and truncations keep the integer precision small and the transform unitary. We have tested our encoder on medical images using different integer filters. Results show that our algorithm with certain filters performs as well and sometimes better in lossless coding than previous coding systems using 2D integer wavelet transforms on medical images.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.11
• /
• pp.128-139
• /
• 1996

In this paper, we propose an algorithm to solve the problem of placement of rectangular blocks whose sizes and shpaes are pre-determined. The proposed method solves the placement of many retangular blocks of different sizes and shapes in a hierarchical manner, so as to minimize the chip area. The placement problem is divided into several sub-problems: hierarchical partioning, hierarchical area/shape estimation, hierarchical pattern pacement, overlap removal, and module rotation. After the circuit is recursively partitioned to build a hierarchy tree, the necessary wiring area and module shpaes are estimated using the resutls of the partitioning and the pin information before the placement is performed. The placement templaes are defined to represent the relative positions of the modules. The area and the connectivity are optimized separately at each level of hierachy using the placement templates, so the minimization of chip area and wire length can be achieved in a short execution time. Experiments are made on the MCNC building block benchmark circuits and the results are compared with those of other published methods. The proposed technique is shown to produce good figures in tems of execution time and chip area.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.366-366
• /
• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.