• KIPS Transactions on Computer and Communication Systems
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• v.5 no.1
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• pp.7-16
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• 2016

This paper discusses a method of selecting variant groups to be reviewed for LGR (Label Generation Rule) of Internet Top-Level Hanja Domain. The most difficult problem in setting up LGR of Internet Top-Level Hanja Domain is how to treat Hanja variants. If domains containing variants (e.g.: 東海國) are directed to different addresses, confusion will arise. Therefore, it is desirable that such domains are directed to the same address. Since variant groups of Korea and China are not same, we need to unify variant groups of Korea and China. In the process of reviewing 3093 Chinese variant groups, the author found that Korea does not need to review Chinese variant groups which include no or just one Korean Hanja character. Korea only need to review Chinese variant groups which include two or more Korean Hanja characters. By doing so, the author could reduce the number of Chinese variant groups to be reviewed by Korea from 3093 to 303, which is only one-tenth of the original number of Chinese variant groups. After Korea finishes reviewing 303 Chinese variant groups selected according to the method suggested in this paper, the job of setting up LGR of Internet Top-Level Hanja domain will be accelerated by negotiating with China.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.593-601
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• 2000

Analyzing monthly inflows of the Chung-Ju Dam associated with EI Nino Southern Oscillation (ENSO), Kim and Lee(2000) reported that the fall and winter inflows in EI Nino years tended to be low while those in La Nina years tended to be high. This study proposes a methodology of employing such a teleconnection between ENSO and inflow in reservoir operations. The ENSO information is used as a hydrologic state variable in stochastic dynamic programming (SDP) to derive a monthly optimal rule for operating the Chung- Ju Dam. An alternative operating rule is also derived with the SDP with no hydrologic state variable. Both of the SDP operating rules are simulated and compared to examine the value of using the ENSO information in operations of the Chung-Ju Dam. The simulation results show that the operating rule using the ENSO information increases energy generation and reliability of water supply as well as reduces spill. spill.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1127-1135
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• 2012

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. We generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.341-346
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• 2013

In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.823-826
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• 2008

This paper shows linkage management for external services. There are many services for specific entities such as DBLP and OntoWorld. OntoFrame, as a Semantic Web-based research information service portal, aims at one-stop service in ways that it connects external services with hyperlinks. For managing the linkage, linkage rules are manually edited by human administrators and automatically verified and tested by linkage management system. It consists of linkage rule management, linkage rule verification, linkage test, and dynamic link generation. Linkage rule management creates and edits linkage rules to connect external services on the Web. After finished rule editing and verification step, linkage management invokes linkage test with entity list. Only valid links are visible to enable users to click on our system, and thus it increases user's reliability on the system.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.643-657
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• 2009

The major reason to construct large dams is to store surplus water during rainy seasons and utilize it for water supply in dry seasons. Reservoir storage has to meet a pre-defined target to satisfy water demands and cope with a dry season when the availability of water resources are limited temporally as well as spatially. In this study, a Hedging rule that reduces total reservoir outflow as drought starts is applied to alleviate severe water shortages. Five stages for reducing outflow based on the current reservoir storage are proposed as the Hedging rule. The objective function is to minimize the total discrepancies between the target and actual reservoir storage, water supply and demand, and required minimum river discharge and actual river flow. Mixed Integer Linear Programming (MILP) is used to develop a multi-reservoir operation system with the Hedging rule. The developed system is applied for the Han River basin that includes four multi-purpose dams and one water supplying reservoir. One of the fours dams is primarily for power generation. Ten-day-based runoff from subbasins and water demand in 2003 and water supply plan to water users from the reservoirs are used from "Long Term Comprehensive Plan for Water Resources in Korea" and "Practical Handbook of Dam Operation in Korea", respectively. The model was optimized by GAMS/CPLEX which is LP/MIP solver using a branch-and-cut algorithm. As results, 99.99% of municipal demand, 99.91% of agricultural demand and 100.00% of minimum river discharge were satisfied and, at the same time, dam storage compared to the storage efficiency increased 10.04% which is a real operation data in 2003.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.56-66
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• 1998

본 논문에서는 보상 신호를 이용하는 근사 추론에 기반한 개선된 퍼지 논리 제어기를 제안한다. 제안된 방법은 근사 추론을 위한 인위적인 퍼지 규칙의 생성이나 소속함수의 정의 없이 자동적으로 퍼지 논리 제어기를 구성할 수 있다. 제안된 퍼지 논리 제어기를 cart-pole 제어에 적용하여 기존의 방법들과의 비교를 통해 제시한 방법의 유용성을 검증한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.721-723
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• 2005

패턴 분류에 많이 사용되는 MBR(Memory Based Reasoning) 기법은 메모리에 저장된 학습패턴과 테스트 패턴간의 거리를 계산하여 가장 가까운 학습패턴의 클래스로 분류하기 때문에 테스트 패턴을 분류하는 기준을 설명할 수 없다는 문제점을 가지고 있다. 본 논문에서는 RPA(Recursive Partition Averaging) 기법을 이용하여 분류 기준을 설명할 수 있는 IF-THIN 형태의 규칙을 생성하고 생성된 규칙의 일반화 성능을 향상시키기 위하여 불필요한 조건을 제거하는 규칙 pruning 알고리즘과 생성되는 규칙의 개수를 줄일 수 있는 점진적 규칙 추출 알고리즘을 제안한다.