• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.100-109
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• 1999

The bearing design includes the steps of selection bering type, selection bearing subtype, and determining the peripheral equipments. In this paper decision making methodologies are compared to propose a stepwise decision methodology to the bearing selection problem. An artificial neural network trained with design cases is used for selecting a bearing type in the first step. Then the subtype of the bearing is selected using the weighting method, high is a kind of multi-criteria decision making method. Finally, the types of peripheral equipments such as lubrication devices, seals and bearing housings are determined using a rule-based expert system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.303-306
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• 2000

본 논문에서는 선박에서 화재탐지를 위해서 규칙 기반 추론과 사례 기반 추론을 통합하는 방법에 대해서 논의하였다. 규칙은 어떤 영역에서 광범위한 경향을 표현하는데 적합하며 사례는 규칙에서 예외적인 상황을 다루는데 적합하다는 점에서 규칙과 사례는 상호 보완적이라 할 수 있다. 즉 어떤 행동이 충분히 반복되면 자연스럽게 규칙이 되며, 잘 확립된 규칙이 있다면 사례를 먼저 추론할 필요가 없다. 그러나 규칙이 실패하게 되면 실패를 만회하기 위해서 사례를 생성하는 것이 하나의 대안이 될 수 있다. 본 논문에서는 일반적인 화재탐지 지식은 규칙으로 표현하고, 예외적인 화재탐지 지식은 사례로 표현함으로써 규칙과 사례가 서로 보완적인 역할을 할 수 있는 통합 방법을 제안하였다. 또한 기존의 규칙 기반 FFES(Fire Fighting Expert System)와 사례기반 추론에 의해 확장된 C-FFES(Combined-Fire Fighting Expert System)를 비교를 통해, 제안한 접근 방법이 화재 탐지율을 향상시킴을 보였다.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.358-361
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• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.92-103
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• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.492-495
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• 1995

기존의 반도체 공장에서 사용되어진 제품의 코드는 필요에 의해 각 부문의 코드 담당자가 필요한 제품의 특성을 표현할 수 있도록 생성시켜 사용하여 왔으므로 여러 제품의 특성에 따라, 유사하나 독자적인 생성규칙이 존재하고 있으며 종합적인 관리체계가 이루어질 수 없었다. 이러한 문제점을 해결하고 코드를 자동으로 생성시켜 생산라인에 제공할 수 있는 전문가 시스템을 구축하기 위하여 각각의 규칙을 정의하여 병렬로 처리할 수 있는 지능형 코드관리 시스템을 개발하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.131-137
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• 1999

전문가시스템 개발에 있어서 지식획득 병목현상(knowledge acquisition bottleneck)은 해결해야 할 큰 걸림돌중 하나이다. 지식획득을 위한 여러 과정을 단순화하고 자동화함으로 지식공학자의 작업을 최소화하면서 전문지식을 쉽고 빠르게 획득할 수 있도록 지식획득시스템을 설계·구현한다면 전문가시스템의 대중화는 지금보다 쉽게 이루어질 것이다. 본 연구는 지식 획득시스템 설계와 구현을 위한 연구의 일환으로 기계학습의 한 방법인 PIPPER(Repeated Incremental Pruning to Produce Error Reduction)를 이용하여 규칙을 생성하고 생성된 규칙을 JESS(Justification based Expert System Shell)에서 처리하도록 하였다. 규칙을 생성하기 위한 데이터는 Bohanec이 1997년도에 만든 자동차 평가 데이터베이스(Car Evaluation Database)를 사용하여 실험하였으며, 1700여 개의 레코드에서 약 40개의 규칙이 생성되었고, 생성된 규칙은 지식베이스의 정당성을 위반하지 않으면서 실행되었다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.439-443
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• 1996

A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball ＆ beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.1
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• pp.14-23
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• 1996

Scheduling jobs effectively under consideration of actual loads on machines is one of the most complicated tasks in production control. The complexity of the finite capacity scheduling often makes the conventional methods of industrial engineering fail. As an alternative, Knowledge-based approaches to job-shop scheduling have been evolved recently. This paper presents a blackboard- based scheduling expert system which combines knowledge-based scheduling with interactive scheduling. It is shown to be possible to generate the feasible schedule within a reasonable time. Flexible reaction management is also possible while keeping the changes in the generated schedule to the minimal and adjusting the schedule to tardy operations or working environmental changes. The system is equipped with a rule base with heuristics for handling conflicted event. A case study applying the implemented system is described.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.63-73
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• 2003

The major objective of this paper is to propose the method of attacker and host modeling for cyber-attack simulation. In the security modeling and simulation for information assurance, it is essential the modeling of attacker that is able to generate various cyber-attack scenarios as well as the modeling of host, which is able to represent behavior on attack concretely The security modeling and simulation, which was announced by Cohen, Nong Ye and etc., is too simple to concretely analyze attack behavior on the host. And, the attacker modeling, which was announced by CERT, Laura and etc., is impossible to represent complex attack excepting fixed forms. To deal with this problem, we have accomplished attacker modeling by adopted the rule-based SES which integrates the existing SES with rule-based expert system for synthesis and performed host modeling by using the DEVS formalism. Our approach is to show the difference from others in that (ⅰ) it is able to represent complex and repetitive attack, (ⅱ) it automatically generates the cyber-attack scenario suitable on the target system, (ⅲ) it is able to analyze host's behavior of cyber attack concretely. Simulation tests performed on the sample network verify the soundness of proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.