• 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 Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.185-191
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• 1999

Mathematical morphology based on set theory has been applied to various areas in image processing. Pitas proposed a object recognition algorithm using Morphological Shape Decomposition(MSD), and a new representation scheme called Morphological Shape Representation(MSR). The Pitas's algorithm is a simple and adequate approach to recognize objects that are rotated 45 degree-units with respect to the model object. However, this recognition scheme fails in case of random rotation. This disadvantage may be compensated by defining small angle increments. However, this solution may greatly increase computational complexity because the smaller the step makes more number of rotations to be necessary. In this paper, we propose a new method for object recognition based on MSD. The first step of our method decomposes a binary shape into a union of simple binary shapes, and then a new tree structure is constructed which ran represent the relations of binary shapes in an object. finally, we obtain the feature informations invariant to the rotation, translation, and scaling from the tree and calculate matching scores using efficient matching measure. Because our method does not need to rotate the object to be tested, it could be more efficient than Pitas's one. MSR has an intricate structure so that it might be difficult to calculate matching scores even for a little complex object. But our tree has simpler structure than MSR, and easier to calculated the matchng score. We experimented 20 test images scaled, rotated, and translated versions of five kinds of automobile images. The simulation result using octagonal structure elements shows 95% correct recognition rate. The experimental results using approximated circular structure elements are examined. Also, the effect of noise on MSR scheme is considered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.348-359
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• 1989

The multitree pattern recognition algorithm proposed by [1] and [2] is modified in order to improve its performance. The basic idea of the multitree pattern classification algorithm is that the binary dceision tree used to classify an unknow pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature ordet. So the feature ordering needed in the calssification procedure is simple and the number of features used in the classification procedure is small compared with other classification algorithms. Thus the algorithm can be easily applied to real pattern recognition problems even when the number of features and that of the classes are very large. In this paper, the wighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11A
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• pp.1130-1137
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• 2006

One of the important performance issues in large-scale RFID systems is to resolve collisions among responses from RFID tags. Considering two do facto anti-collision solutions, namely the binary-tree splitting algorithm and the Slotted-Aloha algorithm, we propose to use multi-packet reception (MPR) capability to enhance the RFID tag reading rate (i.e., throughput). MPR allows an RFID reader to receive multiple reponses transmitted by tags at the same time. We analyze the effect of MPR capability in the above anti-collision algorithms, which is also validated by simulation. The analysis and simulation results show that RFID reader antenna design and signal separation techniques play an important role in improving RFID system performance with MPR capability.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.409-415
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• 2009

The head-related transfer function (HRTF), which has an important role in virtual sound localization has different characteristics across the subjects. Measuring HRTF is very time-consuming and requires a set of specific apparatus. Accordingly, HRTF customization is often employed. In this paper, we propose a method to search an adequate HRTF from a set of the HRTFs. To achieve rapid and reliable customization of HRTF, all HRTFs in the database are partitioned, where a binary search tree was employed. The distortion measurement adopted in HRTF partitioning was determined in a heuristic way, which predicts the differences in perceived sound location well. The DC-Davis CIPIC HRTF database set was used to evaluate the effectiveness of the proposed method. In the listening test, where 10 subjects were participated, the stimuli filtered by the HRTF obtained by the proposed method were closer to those by the personalized HRTF in terms of sound localization. Moreover, performance of the proposed method was shown to be superior to the previous customization method, where the HRFT is selected by using anthropometric data.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.394-405
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• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.172-175
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• 1992

This paper proposes the methods to construct the tree-structured database and analyze the distribution system network. In order to cope with an extensive amount of data and the frequent breaker switching operations in distribution systems, the database for system configuration is constructed using binary trees. Once the tree-structured database has been built, the system tracing of distribution network can be rapidly performed. This remarkably enhances the efficiency of data search and easily adapts to system changes due to switching operations. The computation method of fast power flow using tree search strategies is presented. The methods in the paper may be available in the field of distribution system operation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.73-80
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• 2005

Recently there have been many efforts to support searching and browsing based on the visual content of image and multimedia data. Most existing approaches to content-based image retrieval rely on query by example or user based low-level features such as color, shape, texture. But these methods of query are not easy to use and restrict. In this paper we propose a method for automatic color object extraction and labelling to support multiple queries of content-based image retrieval system. These approaches simplify the regions within images using single colorizing algorithm and extract color object using proposed Color and Spatial based Binary tree map(CSB tree map). And by searching over a large of number of processed regions, a index for the database is created by using proposed labelling method. This allows very fast indexing of the image by color contents of the images and spatial attributes. Futhermore, information about the labelled regions, such as the color set, size, and location, enables variable multiple queries that combine both color content and spatial relationships of regions. We proved our proposed system to be high performance through experiment comparable with another algorithm using 'Washington' image database.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.33-40
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• 2005

Recently So far Many low-cost mobile machinery have been produced. Those are being used for study and business. But those are some weak Points which are small-capacity storage and quite low-speed system. If we use general database programs or key-searching algorithm, It could decrease in performance of system. To solve those Problems, we applied CBDS(Compact Binary Digital Search) trie to mobile environment. As a result we could accomplish our goal which are quick searching and low-capacity indexing. We compared with some Java classes such as TreeSet to evaluation. As a result, the velocity of searching was a little slow than B-tree based TreeSet. But the storage space have been decreased by 29 percent. So I think that it would be practical use.