• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.6
• /
• pp.764-770
• /
• 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.

• Journal of the Korean Chemical Society
• /
• v.53 no.4
• /
• pp.427-431
• /
• 2009

The thermodynamical mechanism of the extraction of soybean isoflavones from soybean residuals using organic solvent method has been studied. On the basis of experiments, a simple model for determining the distribution coefficients in organic solvent extraction was employed to calculate the thermodynamical functions between $K,\;{\Delta}H^0,\;{\Delta}S^0\;and\;{\Delta}G^0$ in the soybean isoflavones extraction process. The results show that the soybean isoflavones extraction is an endothermic and an entropy-increasing process: the ${\Delta}G^0$ decreases when the temperature arises.

• The Transactions of the Korea Information Processing Society
• /
• v.1 no.3
• /
• pp.311-318
• /
• 1994

In this paper, a new stroke extraction method of Chinese character base on the human optical field(the Receptive Field of Cell) is proposed. In processing the feature extraction of the chinese character, needed are more perfect extraction methods for separated informations and its data base. This method can be applied to processing neural cell using conventional feature extraction mechanism in the optical boundary of retina and cerebrum. With this method, its applicability and effectiveness were demonstrated extracting strokes from Chinese character.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.884-888
• /
• 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.

• Journal of Soil and Groundwater Environment
• /
• v.20 no.1
• /
• pp.28-35
• /
• 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.

• Journal of Korea Soil Environment Society
• /
• v.4 no.3
• /
• pp.77-84
• /
• 1999

Sequential extraction was applied to characterize the soil-metal binding mechanism in three kinds of soils contaminated with lead and copper The results showed that soil-metal binding was dependent on soil characteristics and metal species. In Munwha dong soil, lead was mainly carbonate form (37.7%), in agriculture soil was associated with amorphous Fe oxide form (23.9%) and in industry area was associated with exchangeable form (22.9%) Meanwhile for copper. organically bound form represented main fraction in most soil and also carbonate and amorphous Fe oxide form showed high fraction. Crystallized Fe oxide and residuals form of copper showed higher fraction than those of lead. Thus, it can be concluded that copper is bound with soil stronger and more difficult wash out Consequently, this mechanism analysis through sequential extraction can provide useful informations for better soil remediation.

• Journal of Intelligence and Information Systems
• /
• v.9 no.2
• /
• pp.19-38
• /
• 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.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.99-104
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.231-240
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.366-370
• /
• 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.