• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.381-385
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• 2003

We suggest a method which reduces the search space of a model-base on multiple-view approach for polyhedral object recognition using the ART-1 neural network. In this approach, the model-base is consisted of extracted features from two-dimensional projections observed at the predetermined viewpoints of a viewing sphere enclosing the object.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.500-503
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• 2003

The navigation problem of robot is one of the main themes to deal with conficts or interferences between obstacles and the robot itself In this case, while the robot avoids obstacles on the space, the passage route should be determined efficiently. In order to solve problems above, we have come up with the distance field space medel (DFM) and then, under known environment, we have presented the distance field A algorithm for passage route path search. In this research, the method of performing the 3-dimensional passage route path search of robot under unknown environment is proposed. It is shown that the authors can build the distance search model the does not need space division by taking into account of sensor information to a distance field space model, and constructing this information as virtual obstacle information.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.652-658
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• 1998

We introduce the concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-Ga and an exploiter sub-GA. The explorer sub-GA mainly performs global exploration of the search space, and incorporates a restart mechanism to help avoid being trapped at local optima. The exploiter sub-GA performs exploitation of fit local areas of the search space around the neighborhood of the best-so-far solution. Thus the search function of the algorithm is divided. the proposed technique exhibits performance significantly superior to standard GAs on two complex highly multimodal problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.750-755
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• 1998

Due to the temporal spatial correlation of the image sequence, the motion vector of a block is highly related to the motion vectors of its adjacent blocks in the same image frame. If we can obtain useful and enough information from the adjacent motion vectors, the total number of search points used to find the motion vector of the block may be reduced significantly. Using that idea, an efficient fuzzy prediction search (FPS) algorithm for block motion estimation is proposed in this paper. Based on the fuzzy inference process, the FPS can determine the motion vectors of image blocks quickly and correctly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.34-39
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• 2004

Huffman coding which has been used in many data compression algorithms is a popular data compression technique used to reduce statistical redundancy of a signal. It has been proposed that the Huffman algorithm can decode efficiently using characteristics of the Huffman tables and patterns of the Huffman codeword. We propose a new Huffman decoding algorithm which used a multi way tree search and present an efficient hardware implementation method. This algorithm has a small logic area and memory space and is optimized for high speed decoding. The proposed Huffman decoding algorithm can be applied for many multimedia systems such as MPEG audio decoder.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.1093-1102
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• 2003

The dynamic nature of the World Wide Web challenges Search engines to find relevant and recent pages. Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. In this paper we study way spiders that should visit the URLs in order to obtain more “important” pages first. We define and apply several metrics, ranking formula for improving crawling results. The comparison between our result and Breadth-first Search (BFS) method shows the efficiency of our experiment system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.299-304
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• 2004

With the development of mobile and wireless environments, the Ubiquitous era has com on the base of technologies of Semantic Web and Web Services. To accelerate proliferation of the E-Commerce in the Ubiquitous era, the importance of information search is emphasized more and more and its time to need more intelligent search for product and service which can consider location and other context-aware related information. As a starting point to meet these requirements, we proposed more effective product information search service framework through web services under semantic web environment. Over the wireless internet and we also examined its technical validity with the prototype system, implemented prototype and investigated the technological possibility.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.192-195
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• 2008

People have tendency of forgetting music title, though they easily remember particular part of music. If a music search system can find the title through a part of melody, this will provide very convenient interface to users. In this paper, we propose an algorithm that enables this type of search using feature transformation function. The original music is transformed to new feature information with sequential melodies. When a melody that is a part of search music is given to the system, the music retrieval system searches the music similar to the feature information of the melody. Moreover, this transformation function can be easily extended to various music recognition systems.