• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.41 no.8
• /
• pp.55-62
• /
• 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
• /
• v.42 no.5 s.335
• /
• pp.47-54
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.208-212
• /
• 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
• /
• v.6 no.1
• /
• pp.98-103
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.218-223
• /
• 1995

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature or feature subset among all the available features is selected based on fitness function in genetic algorithm which is inversely proportional to classification error, balance between cluster, number of feature used. The proposed design scheme is applied to the handwtitten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.6
• /
• pp.102-110
• /
• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.8A
• /
• pp.942-949
• /
• 2004

This paper proposes and analyzes the anti-collision algorithm in Ubiquitous ID system. We mathematically compares the performance of the proposed algorithm 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 the analytic results, comparing the proposed algorithm with bit-by-bit algorithm which is the best of existing algorithms, the performance of proposed algorithm is about 5% higher when the number of tags is 20, and 100% higher when the number of tags is 200.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.5
• /
• pp.1089-1095
• /
• 2004

The genetic algorithms have been used to take a near optimal solution because The generation of the optimal Steiner tree from a given network is NP-hard problem,. The chromosomes in genetic algorithm are represented with the floating point representation instead of the existing binary string for solving this problem. A spanning tree was obtained from a given network using Prim's algorithm. Then, the new Steiner point was computed using genetic algorithm with the chromosomes in the floating point representation, and it was added to the tree for approaching the result. After repeating these evolving steps, the near optimal Steiner tree was obtained. Using this method, the tree is quickly and exactly approached to the near optimal Steiner tree compared with the existing genetic algorithms using binary string.

• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2330-2332
• /
• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• ETRI Journal
• /
• v.32 no.4
• /
• pp.638-641
• /
• 2010

With the growth of memory capacity and density, memory testing and repair with the goal of yield improvement have become more important. Therefore, the development of high efficiency redundancy analysis algorithms is essential to improve yield rate. In this letter, we propose an improved built-in redundancy analysis (BIRA) algorithm with a minimized binary search tree made by simple calculations. The tree is constructed until finding a solution from the most probable branch. This greatly reduces the search spaces for a solution. The proposed BIRA algorithm results in 100% repair efficiency and fast redundancy analysis.