• 한국지능시스템학회논문지
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• 제14권6호
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• 대한화학회지
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• 제53권4호
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• pp.427-431
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• 2009

대두 잔기로부터 유기용매를 사용하여 이소플라본을 추출하는 열역학적 메카니즘을 조사하였다. 대두 잔기로부터 유기용매를 사용한 이소플라본 추출과정에서의 분배 계수를 결정하기 위한 간단한 모델을 설정하고 $K,\;{\Delta}H^0,\;{\Delta}S^0\;and\;{\Delta}G^0$ 간에 열역학적 함수를 계산하였다. 그 결과 대두 이소플라본 추출은 흡열과정이며 엔트로피 증가과정임을 발견하였다. 온도가 증가할수록 ${\Delta}G^0$가 감소하였다.

• 한국정보처리학회논문지
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• 제1권3호
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• pp.311-318
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• 1994

시각신경계의 특정추출 기구인 수용영역 즉, RF(Receptive Field)모델을 이용하여 한자의 획의 분리 및 추출에 관한 방법을 제안한다. 한자의 복잡한 정보에 대한 분리 추출과 데이터베이스화 등은 더욱 명백한 처리과정을 필요로하고 있다. 본 기법의 특 징은 망막과 대뇌 시각영역의 특징추출 기구인 수용영역을 모델링 하였고 신경세포 입력 방식에 따라 국소적인 처리에서 얻어진 정보를 대국적인 처리로 통합 추출하는 것으로서 그 기능성과 유효성을 확인할 수 있었다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권1호
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• pp.28-35
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• 2015

In this study, the mechanisms on ultrasonication enhanced metals extraction were investigated compared with the conventional washing technique. We hypothesized the mechanisms on enhanced extraction of ultrasonication: ultrasonication increased the temperature of soil slurry and decreased average particle size of soil due to breakdown of soil aggregate. Actually, the ultrasonication increased the temperature of soil slurry to $60^{\circ}C$ in this study, and the increase in the temperature enhanced the metal extraction to 15-20% even in the conventional simple mixing. The conventional washing technique decreased average size of soil particles because of breakdown of soil aggregate, and the ultrasonication decreased the size more than that of washing. The breakdown of soil aggregate improved the contact between metals and washing agent, which enhanced the extraction of metals in the ultrasonication. Therefore, we concluded that the main mechanisms of ultrasonication are increase in the temperature and breakdown of the soil aggregate. Finally, the ultrasonicaiton increased the extractability of metals upto 40% compared to conventional washing technique.

• 한국토양환경학회지
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• 제4권3호
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• pp.77-84
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• 1999

본 논문에서는 고농도의 중금속 (납, 구리)으로 오염된 세 가지의 토양을 대상으로 sequential extraction 방법을 사용하여 토양과 중금속의 차이에 따른 결합 특성을 밝혀내고자 하였다. 고농도의 납과 구리로 오염된 토양을 형태별로 추출하였을 때 토양의 특성과 중금속의 종류에 따라 다른 결과를 보였다. 납은 문화동 토양에서는 Carbonate 형태가 37.7%로 가장 높았고 농토는 Amorphous Fe oxide 형태가 23.9%, 공단 토양에서는 Exchangeable 형태가 22.9%로 나타났다. 이에 비하여 구리는 세 가지 토양에서 공통적으로 Organically bound 형태가 농토에서 26.1%, 문화동 토양은 20.4%. 공단 토양에서는 24.1%로 높게 나타났고 Carbonate 형태와 Amorphous Fe oxide형태의 비율도 높게 나타났다. 또한 Crystallized Fe oxide 형태와 Residuals 형태도 납보다 높은 비율을 나타냄으로서 구리가 납보다는 토양과 강한 결합을 형성하는 것으로 생각된다. 이러한 토양과 중금속의 결합 특성은 오염 토양의 복원시 유용한 자료가 될 수 있다.

• 지능정보연구
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• 제9권2호
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.