• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.141-146
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• 2001

Lots of the agricultural information come from human experiences and are in non-numerical forms. Therefore, it is difficult to process to be processed in a conventional data processing way. An internet expert system for agricultural application using the ASP(active server page) was developed to solve this problem and consisted of databases, an inference engine, and an user interface. The databases were composed of rule base, question base and link data. The inference engine was developed with the ASP for connection with web between databases. The used interface was developed with the CGI(common gateway interface), so that question could be answered on a web browser, and the session technique was used to provide proper result to each of multi-users. A prototype internet expert system was developed for diagnosis of diseases and nutritional disorders of paddy rice. The expert system was interactively worked through WWW(world wide web) at remote sites by multi-users, even at the same time. The rule base could be easily updated and modified from a web server computer by a knowledge engineer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.271-275
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• 한국IT서비스학회:학술대회논문집
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• 2003.05a
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• pp.571-576
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• 2003

보험시스템은 상품 및 보험 종류에 따라서 결정되는 요소들이 많고 이에 대한 예외 사항이 많이 존재하는 특성을 가지고 있다. 기존 시스템에서의 상품속성 반영은 테이블을 통한 값 정의와 어플리케이션에서의 예외처리 로직(if then else)을 병행하여 사용함으로 인해, 상품변경과 신상품 개발에 대한 비용이 증가하고 신속한 시장 대응이 어려웠다. 본 논문에서는 보험상품 속성의 비즈니스 로직을 데이터화로 가능하게 하는 Well Formed Rule Base 시스템을 제시하고 실제 프로젝트 적용을 통한 효과를 설명한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.1
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• pp.77-83
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• 2001

We analyzed the mechanism of EM emission in telecommunication system and extracted the dominant parameter in EMC design. The I/O cable, ventilation hole and shield design of chassis are important EMC design Issues in telecommunication systems. Because telecommunication systems have much more I/O cables than other electronic products, EMC design of I/O cable is very important in telecommunication systems. Therefore by the method of experimentation and simulation, EM coupling mechanism of I/O cable was analyzed and the design rule for low emission was extracted. On the base of these EMC design rules, EMC design of telecommunication system was executed without complex redesign or debug. The result obtained by these methods was shown in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1564-1569
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• 1991

In this paper we describe Flexible Manufacturing Systems (FMS) using Petri nets, since Petri nets provide a powerful tool for modeling dynamical behavior of discrete concurrent processes. We deal with off-Line and on-Line rule-based scheduling of FMS. The role of the rule-base is to generate appropriate priority rule for resolving conflicts, that is, for selecting one of enabled transitions to be fired in a conflict set of the Petri nets. This corresponds to select a part type to be processed in the FMS. Towards developing more Intelligent Manufacturing Systems (IMS) we propose a conceptual framework of a futuristic intelligent scheduling system.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.197-213
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• 1997

It is not easy to find a good production schedule which can be used in practice. Therefore, production scheduling simulation with a simple dispatching rule or a set of dispatching rules is used. However, a simple dispatching rule may not create a robust schedule, for the same rule is blindly applied to all internal production processes. The presumption is that there might be a specific combination of appropriate rules that can improve the efficiency of a total production system for a certain type of orders. In order to acquire a better set of dispatching rules, simulation is used to examine the performance of various combinations of dispatching rule sets. There are innumerable combination of rule sets. Hence it takes too much computer simulation time to find a robust set of dispatching rule for a specific production system. Therefore, we propose a concept of the knowledge based simulation to circumvent the problem. The knowledge based simulation consists of knowledge bases, an inference engine and a simulator. The knowledge base is made of rule sets that is extracted from both simulation and human intuition obtained by the simulation studies. For a certain type of orders, the proposed system provides several sets of dispatching rules that are expected to generate better results. Then the scheduler tries to find the best by simulating all proposed set of rules with the simulator. The knowledge-based simulator armed with the acquired knowledge has produced improved solutions in terms of time and scheduling performance.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.138-145
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• 2000

A identification algorithm that finds optimal fuzzy membership functions and rule base to fuzzy model isproposed and a fuzzy controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base is varied according to increase of the elements. The adjusted system is in competition with system which doesn't include any increased elements. The adjusted system will be removed if the system lost. Otherwise, the control system is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.