• Journal of KIISE:Databases
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• v.35 no.2
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• pp.97-111
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• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.501-517
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• 2014

Binary translation is a restructuring process in order to execute a program targeting a specific device on the other devices. In binary translation, it is very important to generate the translation rules between two devices. There are two methods for generating the translation rules, direct and indirect mapping. The direct mapping is the method for performance, while the indirect mapping is the method for retargetability. This paper suggests a binary translation method based on the direct mapping for the embedded systems. Because, however, the retargetability is also important requirement, we suggest the direct mapping based binary translation with considering the retargetability. In addition, we implement an automatic generation tool for translation rules to prove our concept. Through this method, we can generate the translation rules with considering the performance as well as the retargetability. Furthermore, we can reduce costs for the binary translation.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.235-244
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• 2004

We propose a format of a binary phrase structure grammar with composite labels. The grammar adopts binary rules so that the dependency between two sub-trees can be represented in the label of the tree. The label of a tree is composed of two attributes, each of which is extracted from each sub-tree so that it can represent the compositional information of the tree. The composite label is generated from part-of-speech tags using an automatic labeling algorithm. Since the proposed rule description scheme is binary and uses only part-of-speech information, it can readily be used in dependency grammar and be applied to other languages as well. In the best-1 context-free cross validation on 31,080 tree-tagged corpus, the labeled precision is 79.30%, which outperforms phrase structure grammar and dependency grammar by 5% and by 4%, respectively. It shows that the proposed rule description scheme is effective for parsing Korean.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.109-119
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• 1996

We propsoed a multiplexed optical correlation filter method for an optical parallel addition based on symbolic substitution. In the proposed mthod, we used redundant binary number which was easy to minimize the number of the symbolic substitution rules. We chose MACE filter which had very low sidelobes and good correlation peak compared with SDF filter as the optical correlation recognition filter and encoded input numbers properly to increase the discrimination capability. In order to minimize the number of symbolic substitution rules, sixteen input patterns were divided into six groups of the same addition results and six filters for recognizing the input patterns were used. these filters were multiplexed in two MMACE filter planes and the corresponding substitution method was proposed. Through the computer simulation, we confirmed the proposed method was suitable to implement the optical parallel adder.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.129-133
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part therefore fast data transfer rate and high storage capacity about $1Tb/cm^3$ can be realized. In this paper, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers and fuzzy rules. Therefore, By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.78-87
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• 2009

In the IEEE 802.16 Wireless MAN standard, the best effort service class is ranked on the lowest position in priority and is assisted by a MAC scheme based on reservation ALOHA. In such a MAC scheme, a collision among the requests is unavoidable so that the standard adopted a binary exponential back-off rule to arbitrate a collision. Aiming at improving throughput performance, we present two generic collision arbitration rules based on p-persistence rule, (identified as pristine and metamorphosed rules), as alternatives in a wireless MAN. For each of these rules, we then develop an analytical method to calculate an approximate value of saturated throughput. In comparison with simulation results, we confirm the high accuracy of the analytical method. Also, the pristine and metamorphosed rules are observed to exhibit higher saturated throughput compared with the binary exponential back-off rule.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9B
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• pp.577-588
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• 2007

The rapid growth of the Internet has stimulated the development of various new applications and services, and the service providers and the Internet users now require different levels of service qualities rather than current best-effort service which treats all incoming packet equally. Therefore, next generation routers should provide the various levels of services. In order to provide the quality of services, incoming packets should be classified into flows according to pre-defined rules, and this should be performed for all incoming packets in wire-speed. Packet classification not only involves multi-dimensional search but also finds the highest priority rule among all matching rules. Area-based quad-trie is a very good algorithm that constructs a two-dimensional trie using source and destination prefix fields. However, it performs the linear search for the prefix length, and hence it does not show very good search performance. In this paper, we propose to apply binary search on length to the area-based quad-trie algorithm. In improving the search performance, we also propose two new algorithms considering the priority of rules in building the trie.

• Journal of KIISE
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• v.42 no.11
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• pp.1410-1422
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• 2015

In order to design a system, software developers use an object model such as the UML class diagram. Object-Relational Transformation Methodology (ORTM) is a methodology to transform the relationships that are expressed in the object model into relational database tables, and it is applied for the implementation of the designed system. Previous ORTM studies have suggested a number of transformation methods to represent one relationship. However, there is an implementation problem that is difficult to apply because the usage criteria for each transformation method do not exist. Therefore, this paper proposes a binary decision diagram-based modeling rule for each relationship. Hence, we define the conditions for distinguishing the transformation methods. By measuring the query execution time, we also evaluate the modeling rules that are required for the verification. After evaluation, we re-define the final modeling rules which are represented by propositional logic, and show that our proposed modeling rules are useful for the implementation of the designed system through a case study.