• Journal of IKEEE
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• v.22 no.3
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• pp.850-853
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• 2018

In this paper, we propose a decoding method using a balanced binary tree and a canonical Huffman tree for efficient decoding of Huffman codes. The balanced binary tree scheme reduces the number of searches by lowering the height of the tree and binary search. However, constructing a tree based on the value of the code instead of frequency of symbol is a drawback of the balanced binary tree. In order to overcome these drawbacks, a balanced binary tree is reconstructed according to the occurrence probability of symbols at each level of the tree and binary search is performed for each level. We minimize the number of searches using a canonical Huffman tree to find level of code to avoid searching sequentially from the top level to bottom level.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.55-62
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• 2004

This paper proposes and analyzes two anti-collision algorithms in Ubiquitous ID system. We mathematically compares the performance of the proposed algorithms with that of binary search algorithm slotted binary tree algorithm using time slot, and bit-by-bit binary tree algorithm proposed by Auto-ID center. We also validated analytic results using OPNET simulation. Based on analytic result comparing the proposed Modified bit-by-bit binary tree algorithm with bit-by-bit binary tree algorithm which is the best of existing algorithms, the performance of Modified bit-by-bit binary tree algorithm is about 5% higher when the number of tags is 20, and 100% higher when the number of tags is 200. Furthermore, the performance of proposed Enhanced bit-by-bit binary tree algorithm is about 335％ and 145% higher than Modified bit-by-bit binary tree algorithm for 20 and 200 tags respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.5 s.335
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• pp.47-54
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• 2005

This paper proposes and analyzes a Tag Anti-collision algorithm in RFID system. We mathematically compare the performance of the proposed algorithm with existing binary algorithms(binary search algorithm, slotted binary tree algorithm using time slot, and bit-by-bit binary tree algorithm proposed by Auto-ID center). We also validated analytic results using OPNET simulation. Based on analytic result, comparing the proposed Improved bit-by-bit binary tree algerian with bit-by-bit binary tree algorithm which is the best of existing algorithms, the performance of Improved bit-by-bit binary tree algorithm is about $304\%$ higher when the number of tags is 20, and $839\%$ higher when the number of tags is 200.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.971-980
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• 2003

The tree method can be extended to multivariate responses, such as repeated measure and longitudinal data, by modifying the split function so as to accommodate multiple responses. Recently, some decision trees for multiple responses have been constructed by Segal (1992) and Zhang (1998). Segal suggested a tree can analyze continuous longitudinal response using Mahalanobis distance for within node homogeneity measures and Zhang suggested a tree can analyze multiple binary responses using generalized entropy criterion which is proportional to maximum likelihood of joint distribution of multiple binary responses. In this paper, we will modify CART procedure and suggest a new tree-based method that can analyze multiple binary responses using similarity measures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.345-353
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• 2019

Several studies have been proposed to improve storage space efficiency by expressing binary trie data structure as a linear binary bit string. Compact binary tree approach generated using one binary trie increases the key search time significantly as the binary bit string becomes very long as the size of the input key set increases. In order to reduce the key search range, a hierarchical compact binary tree technique that hierarchically expresses several small binary compact trees has been proposed. The search time increases proportionally with the number and length of binary bit streams. In this paper, we generate several binary compact trees represented by full binary tries hierarchically. The search performance is improved by allowing a path for the binary bit string corresponding to the search range to be determined through simple numeric conversion. Through the performance evaluation using the worst time and space complexity calculation, the proposed method showed the highest performance for retrieval and key insertion or deletion. In terms of space usage, the proposed method requires about 67% ~ 68% of space compared to the existing methods, showing the best space efficiency.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.39-56
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• 1997

This paper applies the binary tree classifier and discriminant analysis methods to predicting failures of banks and insurance companies. In this study, discriminant analysis is generally better than the binary tree classifier in the classification of bank defaults; the binary tree is generally better than discriminant analysis in the classification of insurance company defaults. This situation can be explained that the performance of a classifier depends on the characteristics of the data. If the data are dispersed appropriately for the classifier, the classifier will show a good performance. Otherwise, it may show a poor performance. The two data sets (bank and insurance) are analyzed to explain the better performance of the binary tree in insurance and the worse performance in bank; the better performance of discriminant analysis in bank and the worse performance in insurance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.365-371
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• 2015

CB tree represents the binary trie by a compact binary sequence. However, retrieval time grows fast since the more keys stored in the trie, longer the binary sequences are. In addition it is inefficient for frequent key insertion/deletion. HCB tree is a hierarchical CB tree consisting of small binary tries. However it can not avoid shift operations and have to scan an additional table to refer child or parent trie. In order to improve retrieval performance and avoid shift operations when keys are inserted or deleted, we in this paper represent each separated trie by a full binary trie and then assign the unique identifier to it. Finally the theoretical evaluations show that both the proposed approach and HCB tree provides better than CB tree for key retrieval. The proposed approach shows the highest performance in case of key insertion/deletion and moreover requires only 71%~89% of storage as compared with CB tree.

• Korean Journal of Mathematics
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• v.7 no.1
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• pp.37-44
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• 1999

We study the relations among the domination number, the independent domination number, and the independence number of an oriented tree and establish their bounds. We also do the same for a binary tree.