• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.327-332
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• 1996

Rough Set theory suggested by Pawlak has a property that it can describe the degree of relation between condition and decision attributes of data which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a occupancy degree which is a measure can represent a degree of relational quantity between condition and decision attributes of data table. We also propose a method that can find an optimal fuzzy rule table and membership functions of input and output variables from data without linguistic information and examine the validity of the method by modeling data generated by fuzzy rule.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.49-54
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• 1998

AS it takes a considerable time for database operations for processing the condition part of active rule, the operations have an important effect on the efficiency of active database system. The processing time of operations should be minimized in order to improve the efficiency of system. The previous works are limited to basic database operations and the partial aggregate functions. In this paper, the processing technique using the structuralization and the state table of relations is suggested. The processing time for basic database operations can be reduced with the structuralization of relations to classification tree and the introduction of deletion information table. With the introduction of binary search tree and relation state table, the aggregate function which has a big of processing cost can be processed effectively and the function of the active database system can be maximized.

• Journal of KIISE
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• v.42 no.8
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• pp.961-971
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• 2015

As a representative area-decomposed algorithm, an area-based quad-trie (AQT) has an issue of search performance. The search procedure must continue to follow the path to its end, due to the possibility of the higher priority-matching rule, even though a matching rule is encountered in a node. A leaf-pushing AQT improves the search performance of the AQT by making a single rule node exist in each search path. This paper proposes a new algorithm to further improve the search performance of the leaf-pushing AQT. The proposed algorithm implements a leaf-pushing AQT using a hash table and an on-chip Bloom filter. In the proposed algorithm, by sequentially querying the Bloom filter, the level of the rule node in the leaf-pushing AQT is identified first. After this procedure, the rule database, which is usually stored in an off-chip memory, is accessed. Simulation results show that packet classification can be performed through a single hash table access using a reasonable sized Bloom filter. The proposed algorithm is compared with existing algorithms in terms of the memory requirement and the search performance.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.65-73
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• 2011

In rough set literatures, methods for inducing minimal rules from a given decision table have been proposed. When the decision attribute is ordinal, inducing rules about upward and downward unions of decision classes is advantageous in the simplicity of obtained rules. However, because of independent applications of the rule induction method, inclusion relations among upward/downward unions in conclusion parts are not inherited to the condition parts of obtained rules. This non-inheritance may debase the quality of obtained rules. To ensure that inclusion relations among conclusions are inherited to conditions, we propose two rule induction approaches. The performances of the proposed approaches considering the inclusion relations between conclusions are examined by numerical experiments.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.11a
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• pp.51-57
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• 2003

In operating distributed systems, proactive management is one of the major concerns for better quality of service and future capacity planning. In order to handle this management problem effectively, it is necessary to analyze performances of the distributed system and events generated by components in the system. This paper provides a rule-based event parsing engine for proactive management. Our event parsing engine uses object hooking-based and event-token approaches. The object hooking-based approach prepares new conditions and actions in Java classes and allows dynamically exchange them as hook objects in run time. The event-token approach allows the event parsing engine consider a proper sequence and relationship among events as an event token to trigger an action. We analyze the performance of our event parsing engine with two different implementations of rule structure; one is table-based and the other is tree-based.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.41-54
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• 2001

This paper propose an active rule and its semantic model integrating active rule system into temporal database. Most of previous works have been focused on condition action(CA) rule for temporal relations. However, proposed active rule model supports full SQL:3 trigger functionalitics including transition variable and table as well as tuple-level and statement-level active rules in E CA granularity. A bi-temporal concept of active rule and valid time event are also proposed newlv.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.388-398
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• 1998

In constructing fuzzy control systems. there are many parameters such as rule base. membership functions. inference m method. defuzzification. and I/O scaling factors. To control the system in properly using fuzzy logic. we have to consider t the correlation with those parameters. This paper deals with self-tuning of fuzzy control systems. The fuzzy controller h has parameters that are input and output scaling factors to effect control output. And we propose the looklongleftarrowup table b based self-tuning fuzy controller. We propose the PMTM(Plus-Minus Tuning Method) for self tuning method, self-tuning the initial look-up table to the appropriate table by adding and subtracting the values.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.249-255
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• 2000

HRIV(Hybrid Rule-Interval Variation) method is presented to stabilize a class of nonlinear systems, where SMC(Sliding Mode Control) and ADC (ADaptive Control) schemes are incorporated to overcome the unstable characteristics of a conventional FLC(Fuzzy Logic Control). HRIV method consists of two modes: I-mode (Integral Sliding Mode PLC) and R-mode(RIV method). In I-mode, SMC is used to compensate for MAE(Minimum Approximation Error) caused by the heuristic characteristics of FLC. In R-mode, RIV method reduces interval lengths of rules as states converge to an equilibrium point, which makes the defined Lyapunov function candidate negative semi-definite without considering MAE, and the new uncertain parameters generated in R-mode are compensated by SMC. In RIV method, the overcontraction problem that the states are out of a rule-table can happen by the excessive reduction of rule intervals, which is solved with a dynamic modification of rule-intervals and a transition to I-mode. Especially, HRIV method has advantages to use the analytic upper bound of MAE and to reduce Its effect in the control input, compared with the previous researches. Finally, the proposed method is applied to stabilize a simple nonlinear system and a modified inverted pendulum system in simulation experiments.