• 대한전기학회논문지
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• 제41권8호
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• 한국항행학회논문지
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• 제2권1호
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• pp.61-67
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• 1998

이 연구에서는 항공기의 주기 문제를 해결하여 주는 스케줄링 시스템과 그 조정을 위한 전문가 시스템(RACES : Ramp Activity Co-ordination Expert System)을 설계 및 개발한 내용을 기술하고 있다. RACES는 공항에서 매일 발생하는 출발편 및 도착편 항공기를 브릿지(bridge)와 스팟(spot)에 배정하기 위해 인간 전문가(human expert)로부터 습득한 해당 분야의 지식(도메인 지식) 및 휴리스틱(heuristic)을 지식 베이스로 갖고 있다. 이 RACES는 브릿지/스팟과 항공기 간에 내적 관계, 예를 들어 승객 및 공항의 그라운드 핸들링(ground handling) 등과 같은 복잡하며 동적인 제약조건 들로부터 발생하는 복잡한 스케줄링 문제를 수반한다. 매일 발생하는 600편 정도의 항공기에 대한 주기장 관리 스케줄링이 인간 전문가에 의해 수행되어졌을 경우에는 약 4~5시간이 소요되는 반면 RACES에 의해 수행되어졌을 경우에는 약 20초 정도의 시간이 소요되었고 RACES로부터 얻어진 스케줄링 결과는 해당 분야의 전문가들로부터 인정되었다. RACES는 또한 예외적인 상황이 발생했을 경우에 스케줄의 부분적인 조정을 처리하도록 설계되었다. 하루의 스케줄링이 완료된 후 항공기의 변경 및 지연 메시지는 도메인 전문가의 지식을 바탕으로 스케줄링에 반영되어 스케줄이 조정되어야 한다. 동적 재스케줄링(reactive scheduling) 단계는 도메인 전문가의 지식 모델 분석을 통해 사용자 그래픽 인터페이스의 규칙과 시나리오로써 효과적으로 나타내어진다. 항공편의 변경 및 취소로 인해 발생되는 항공기 배치의 조정은 현재 스케줄에 반영되어져야 하기 때문에 이러한 항공기 배치의 조정은 동적 재스케줄링을 위해 메인 프레임으로부터 RACES에게 통보되어져야 하며 부분적인 재스케줄링을 처리하는 것에는 불규칙적인 요소들이 많기 때문에 RACES에 의해 스케줄의 조정이 반 자동적으로 수행된다.

• 가정간호학회지
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• 제29권1호
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• pp.18-30
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• 2022

Purpose: Purpose: This study aimed to identify whether infection control practice would correlate significantly with the knowledge and attitude of infection control in the pre-, mid-, and postvisiting rounds among community-visiting nurses. Methods: A descriptive study was conducted based on the knowledge, attitude, and practice (KAP) model by administrating questionnaires during September-October 2020. A total of 65 nurses working for 15 community health centers in Seoul, South Korea were included. The questionnaires were developed based on the epidemiologic triangle model and comprised of 28 items on practice, 18 items on knowledge, and 10 items on attitude. Results: The infection control practice showed a mean of 88.9 (range, 0-100). The infection control knowledge had 89.2% on the host domain, 80.0% on the environment domain, and 74.8% on the agent domain (range, 0-100). The infection control attitude showed a mean of 39.5 (range, 0-50). Higher scores on the infection control practice are significantly correlated with the higher scores on the infection control knowledge about the host domain (p= .004) at the pre-, mid-, and post-visiting rounds. Higher scores on the infection control practice are significantly correlated with the higher scores on the infection control attitude at the mid- (p= .018) and postvisiting rounds (p= .028). Conclusions: The infection control practice by community-visiting nurses may be enhanced with increased knowledge and attitude levels of infection control at the mid- and post-visiting rounds. The enhancement should be included in the on-the-job education for community-visiting nurses.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.421-424
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• 1996

To provide an appropriate knowledge representation technique dependent on a particular domain, we consider the combine analytic hierachy process(CAHP). This is an extended method of the conventional AHP which is useful when two different expert groups are involved. Our problem domain is confined to human resource management including such major activities as planning, selection, placement, compensations, performance evaluation, training, and labor-management relations. We prioritize rules, frames, semantic nets, and predicate logic representation techniques best suited to each and all domains through an exploratory study.

• 제어로봇시스템학회논문지
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• 제5권1호
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• pp.54-61
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• 1999

Objective of the research is to put the vast and complex fault tracking knowledge of human experts in semiconductor production line into the knowledge base of computer system. We mined the fault tracking knowledge of domain experts(engineers of production line) for the construction of knowledge base of the expert system. Object oriented fact models which increase the extensibility and reusability have been built. The rules are designed to perform the fault diagnosis of the items in production device. We have exploited the evidence accumulation method to assign check priority in rules. The major contribution is in the overall design and implementation of the nile base and related facts of the expert system in object oriented paradigm for the application of the system in fault diagnosis in semiconductor production line.

• 한국지능시스템학회논문지
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• 제1권2호
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.142-148
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• 1993

Recectly, development of expert system utilizing the domain specific knowledge focuses upon the machining operations. This paper describes an expert system for selecting the optimum grinding wheel based on the Analytic Hierarchy Process and Fuzzy Logic. Knowledge-base, in this system, for selecting of grinding wheel is designed to appling the knowhow and experience knowledge of skilled hands. In this paper, firstly determination method of fuzzy membership function utilizing the qualitative knowledge, and then selection of the optimum wheel from among the available components according to Saaty's priority rule are described. Lastly,some implementation results are suggested.