• 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 Digital Contents Society
• /
• v.5 no.2
• /
• pp.128-132
• /
• 2004

In this paper, a Fuzzy Inference System(FIS) based watermarking algorithm in Discrete Wavelet Transform(DWT) domain is proposed. A 2D fuzzy inference values, in which the inputs are parameters of the coefficients of the DWT block of the original image and the output is strength of watermark embedded, is devised. The fuzzy inference algorithm guarantees that the watermark to be embedded into the original image adaptively. The experimental results shows that proposed approach is robust to the digital image processing schemes.

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.4
• /
• pp.65-74
• /
• 2009

The risk of health impairment due to poor ventilation, fire and explosion by inflammable materials, and other unintended occurrences is always present in dangerous workplaces such as manholes, underground septic tanks, storage tanks and confined areas. Therefore, it a system which can monitor harmful working environment through sensors in workplace on a realtime basis and keep workers safe from the risk is needed. This paper has attempted to design an inference engine to monitor harmful environments in the workplace. The proposed inference engine has a rule-based system structure using JESS. This system is not confined to a particular computing platform and is easily interlocked with OSGi-based middleware.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.415-424
• /
• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2856-2858
• /
• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5
• /
• pp.1434-1445
• /
• 2000

An expert system executes the inference, based on the knowledge of specific domain. the reliability on the results of inference depends upon both the consistency and accuracy of knowledge. This is the reason why expert system requires the facilities which can practice an access to the various kinds of knowledge and maintain the consistency and accuracy of knowledge an maintain the consistency and accuracy of knowledge. This paper is to implement an expert system permitting an access of declarative and procedural knowledge in the knowledge base and in the data base. This paper is also to implement a knowledge acquisition system which adds the knowledge a only if its accuracy and consistency are maintained, after verifying the potential errors such as contradiction, redundancy, circulation, non-reachable rule and non-lined rule. In consequence, the expert system realizes a good access to the various sorts of knowledge and increases the reliability on the results of inference. The knowledge acquisition system contributes tro strengthening man-machine interface that enables users to add the knowledge easily to the knowledge base.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.2
• /
• pp.67-72
• /
• 2005

This paper presents a method to select contingency ranking considering voltage security problems in power systems. Direct method which needs not the detailed knowledge of the post contingency voltage at each bus is used. Based on system operator's experience and knowledge, the membership functions for the MVAR mismatch and allowable voltage violation are justified describing linguistic representation with heuristic rules. Rule base is used for the computation of severity index for each contingency by fuzzy inference. Contingency ranking harmful to the system is formed by the index for security evaluation. Compared with 1P-1Q iteration, this algorithm using direct method and fuzzy inference shows higher computation speed and almost the same accuracy. The proposed method is applied to model system and KEPCO pratical system which consists of 311 buses and 609 lines to show its effectiveness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.447-450
• /
• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10a
• /
• pp.124-140
• /
• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.417-423
• /
• 2002

In this paper, we propose intelligent steering control system that apply LIBL(Linguistic Instruction Based Learning) method to steering system of ship and take the place of process that linguistic instruction such as officer s steering instruction is achieved via ableman. We embody ableman s suitable steering manufacturing model using fuzzy inference rule by specific method of study, and apply LIBL method to present suitable meaning element and evaluation rule to steering system of ship, embody intelligent steering gear control system that respond more efficiently on officer s linguistic instruction. We presented evaluation rule to constructed steering manufacturing model based on ableman s experience, and propose rudder angle for steering system, compass bearing arrival time, meaning element of stationary state, and correct ableman manufacturing model rule using fuzzy inference. Also, we apply LIBL method to ship control simulator and confirmed the effectiveness.