• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.467-470
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• 2000

Vector quantization (VQ) is widely used in many high-quality and high-rate data compression applications such as speech coding, audio coding, image coding and video coding. When the size of a VQ codebook is large, the computational complexity for the full codeword search method is a significant problem for many applications. A number of complexity reduction algorithms have been proposed and investigated using such properties of the codebook as the triangle inequality. This paper proposes a new structured VQ search algorithm that is based on a multi-stage structure for searching for the best codeword. Even using only two stages, a significant complexity reduction can be obtained without any loss of quality.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.51-52
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• 2010

In order to simplify the offline identification of induction motor parameters, a method based on optimization using a Tabu Search algorithm is proposed. The Tabu Search algorithm is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.222-226
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• 2003

Information retrieval must be able to search the most suitable document that user need from document set. If foretell document adaptedness by similarity degree about QL(Query Language) of document, documents that search person does not require are searched. In this paper, showed that can search the most suitable document on user's request searching document of the whole space using genetic algorithm and used knowledge-base operator to solve various model's problem.

• Journal of Astronomy and Space Sciences
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• v.29 no.1
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• pp.41-45
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• 2012

This paper reports the progress of a search for exoplanets with S-type orbits in short-period binary star systems. The selected targets have stellar orbital periods of just a few days. These systems are eclipsing binaries so that exoplanet transits, if planets exist, will be highly likely. We report the results for seven binary star systems.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.673-679
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• 2012

This paper introduces robust tracking algorithm for fast and erratic moving object. CAMSHIFT algorithm has less computation and efficient performance for object tracking. However, the method fails to track a object if it moves out of search window by fast velocity and/or large movement. The size of the search window in CAMSHIFT algorithm should be selected manually also. To solve these problems, we propose an efficient prediction technique for fast movement of object using Kalman Filter with automatic initial setting and variable configuration technique for search window. The proposed method is compared to the traditional CAMSHIFT algorithm for searching and tracking performance of objects on test image frames.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.575-578
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• 2002

In this study, it enabled to perform keyword search on Geographic Information Systems (GIS) more flexibly by taking in the concept of geographic thesaurus in order to make the keyword search that was more efficiently. The search procedure depends on the history when user information is included. This study provides a system for keyword searching as well as check the validity of the system. Furthermore, by establishing this reference method, the search that took in the query of a user flexibly is attained rather than a chosen type reference system.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.100-110
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• 1999

The decision method for the selection of search engine and the number of returned documents for meta search engine proposed in this paper could provide a solution to reduce network traffic and to maintain the precision ratio. The experiments are performed to evaluate the proposed scheme using currently popular search engines and most frequently used queries.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.87-100
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• 2003

In managing organizational tacit knowledge, recent researches have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise. In this paper, we propose an intelligent expert search framework to provide search capabilities for experts in similar or related fields according to the user′s information needs. In enabling intelligent expert searches, Fuzzy Abstraction Hierarchy (FAH) framework has been adopted, through which finding experts with similar or related expertise is possible according to the subject field hierarchy defined in the system. To improve FAH, a text categorization approach called Vector Space Model is utilized. To test applicability and practicality of the proposed framework, the prototype system, "Knowledge Portal for Researchers in Science and Technology" sponsored by the Ministry of Science and Technology (MOST) of Korea, was developed.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.71-79
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• 1998

This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.