• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.319-328
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• 2003

With the proliferation of WWW, providing more intelligence to Web sites has become a major concern in e-business industry. In recent days, this trend is more accelerated by prosperity of CRM (Customer Relationship Management) in terms of various aspects such as product recommendation, self after service, etc. To accomplish this goal, many e-companies are eager to embed web enabled rule-based system, that is, expert systems into their Web sites and several well-known commercial tools are already available in the market. Most of those tools are developed based on CGI so far but CGI based systems inherently suffer over-burden problem when there are too many service demands at the same time due to the nature of CGI. To overcome this limitation of the existing CGI based expert systems, we propose a new form of Web-enabled expert system using hyperlink-based inference mechanism. In terms of burden to Web server, our approach is proven to outperform CGI based approach theoretically and also empirically. For practical purpose, our this approach is implemented in a software system, WeBIS and a graphic rule editing methodology, Expert Diagram is incorporated into the system to facilitates rule generation and maintenance. WeBIS is now successfully operated for financial consulting in the web site of a leading financial consulting company in Korea.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.2
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• pp.60-68
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• 1992

Fuzzy logic rule based controller has many desirable advantages, whih are simple to implement on the real time and need not the information of structure and dynamic characteristics of the system. Thus, nowadays, the scope of the application of the fuzzy logic controller becomes enlarged. But, if the controlled plant is a time-varying/nonlinear system, it is not easy to construct the fuzzy logic rules which need the knowledge of and expert. In this paper, an approach by which the logic control rules can be auto-generated using the genetic algorithm that is known to be very effective in the optimization problem will be proposed and the effectiveness of the proposed approach will be verified by computer simulation of the 2 d.o.f. planner robot.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.255-257
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• 2007

Korea electricity market consists of two payment systems, capacity and energy. Capacity payments are given to the generators according to its hourly availability considering hourly and seasonal weighting factors. Energy payments are settled at the marginal generation cost based on generator variable cost. In 2007, base load energy market is closed and single capacity payment system is begun to apply in electricity market. In this revised market rule, energy price cap for base load generators is newly introduced. We analyze impact of market rule revision in 2007 on base load generators and suggest improvement scheme to enforce market system in this study.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1818-1826
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• 2000

This research is one the design and implementation of XSL document authoring system based on the XSL pattern to transform XML document. This system is developed not according to the rule of conventional CSS methods but according to the rule of XSL syntax for document processing language of SML structure. The XSL authoring system supports Korean character documents and is available of various kinds of pattern generations so that it is useful for generation of XSL document. This system is implemented for the environment of Window NT2000 in compatible IBM PC an develops by Visual J++6.0.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.73-78
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• 1996

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.125-128
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• 2000

인터넷 중심의 정보화 사회가 되면서 기존의 문서는 대부분 전자 문서로 대치되어 가고 있다. 전자 문서간의 호환과 표준화를 위하여 XML(eXtensible Markup Language)이 웹 문서의 표준으로 지정되었으나, 현재까지 사용되고 있는 문서들이 XML 형태의 문서가 아니므로 이를 수동으로 변환해야 하는 어려움이 있다. 본 논문에서는 규칙기반 분서 분류기(Rule-Based Document Classifier)를 설계하여 다양한 형태의 문서를 자동으로 분류하고 그룹화한다. 그룹화된 문서를 이용하여 자동으로 DTD(Document Type Definition)를 생성하고, 자동 생성된 DTD를 이용하여 XML 형태의 문서로 자동 변환할 수 있는 자동 XML 변환기를 제시한다. 이러한 방법은 문서들을 자동으로 분류하고, 문서의 행태에 변화가 있을 때에도 유사한 문서로 분류할수 있을 뿐만 아니라 문서를 재분류할 때 DTD의 중복 생성을 줄일 수 있는 등의 장점을 갖는다.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.40-49
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• 1992

In this paper, an approach to identify fuzzy rules is proposed. The decision of the optimal number of fuzzy rule is made by means of fuzzy c-means clustering. The identification of the parameters of fuzzy implications is carried out by use of genetic algorithms. For the efficinet and fast parameter identification, the reduction thechnique of search areas of genetica algorithms is proposed. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of Gas Furnace. Despite the simplicity of the propsed apprach the accuracy of the identified fuzzy model of gas furnace is superior as compared with that of other fuzzy modles.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.679-684
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• 1989

In this paper, we describe the PTP notion of robot manipulators by neural networks. The PTP motion requires the inverse kinematic redline and the joint trajectory generation algorithm. We use the multi-layered Perceptron neural networks and the Error Back Propagation(EBP) learning rule for inverse kinematic problems. Varying the number of hidden layers and the neurons of each hidden layer, we investigate the performance of the neural networks. Increasing the number of learning sweeps, we also discuss the performance of the neural networks. We propose a method for solving the inverse kinematic problems by adding the error compensation neural networks(ECNN). And, we implement the neural networks proposed by Grossberg et al. for automatic trajectory generation and discuss the problems in detail. Applying the neural networks to the current trajectory generation problems, we can refute the computation time for trajectory generation.