• 대한원격탐사학회지
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• 제3권2호
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• pp.115-119
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• 1987

After the introduction of the topologically structured geographic information system(GIS) with relational DBMS, the attribute data can be handled without considering locational data. By utilzing of the characteristic of the relational DBMS, it can be used as an expert system building tool in GIS. The relational DBMS of the GIS furnishes the data needed to perform deductive functions of the expert system, and the rule based approach provides the decision rules. Therefore, rule based approach with the expert judgement can be easily combined with relational DBMS.

• 정보처리학회논문지D
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• 제17D권6호
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• pp.405-414
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• 2010

다양한 업무요소들의 값의 조합에 따라 대상 값이 결정되는 것을 규칙이라고 한다. 업무를 표현한 기업의 정보시스템은 이러한 수많은 규칙들을 포함하는데, 이러한 규칙들을 구현한 서버 시스템을 규칙기반 시스템이라고 한다. 규칙기반 시스템은 규칙 엔진 기법을 사용하거나 직접 데이터베이스를 사용하여 구현된다. 규칙 엔진 기법은 많은 단점을 가지기 때문에 대부분 관계형 데이터베이스를 사용하여 규칙기반 시스템을 구현한다. 업무의 규모가 커지고 복잡하게 될수록 수많은 다양한 경우의 규칙이 존재하게 되므로 시간과 비용이 크게 증가하고, 대량의 저장공간을 요구하게 될 뿐만 아니라 수행속도의 저하 현상도 많이 발생한다. 따라서 본 연구에서는 이러한 수많은 경우의 규칙들을 동일한 효과를 가지는 간소화된 경우의 규칙들로 변환시킬 수 있는 알고리즘을 제안한다. 본 연구의 알고리즘을 가지고 다양한 업무 규칙 데이터에 적용하여 테스트한 결과 데이터 건수를 간소화시킬 수 있음을 입증하였다. 본 연구의 알고리즘을 사용하여 업무 규칙 데이터를 간소화하게 되면 데이터 베이스를 사용하여 구현된 규칙기반 시스템의 성능을 개선할 수 있다.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• 로봇학회논문지
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• 제18권2호
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• 한국CDE학회논문집
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• 제21권4호
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• pp.433-442
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• 2016

This paper aims to define the relational logic of in-between code articles as well as within atomic sentences in Korean Building Code, as an intermediate research and development process for the automated building permit system of Korea. The approach depicted in this paper enables the software developers to figure out the logical relations in order to compose KBimCode and its databases. KBimCode is a computer-readable form of Korean Building Code sentences based on a logic rule-based mechanism. Two types of relational logic definition are described in this paper. First type is a logic definition of relation between code sentences. Due to the complexity of Korean Building code structure that consists of decree, regulation or ordinance, an intensive analysis of sentence relations has been performed. Code sentences have a relation based on delegation or reference each other. Another type is a relational logic definition in a code sentence based on translated atomic sentence(TAS) which is an explicit form of atomic sentence(AS). The analysis has been performed because the natural language has intrinsic ambiguity which hinders interpreting embedded meaning of Building Code. Thus, both analyses have been conducted for capturing accurate meaning of building permit-related requirements as a part of the logic rule-based mechanism.

• 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.

• 정보과학회 논문지
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• 제42권11호
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• pp.1410-1422
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• 2015

소프트웨어 개발자들은 시스템의 설계를 위해 UML의 클래스 다이어그램과 같은 객체 모델을 이용한다. 객체-관계 변환 방법론은 객체 모델에 표현된 관계성들을 관계형 데이터베이스 테이블로 변환하는 방법론으로, 설계된 시스템의 구현을 위해 적용된다. 기존 객체-관계 변환 방법론의 연구들은 하나의 관계성을 표현하기 위해 여러 변환 기법들을 제안하였다. 하지만 각 변환 기법의 사용기준들이 존재하지 않아 구현에 적용하기 어려운 문제점이 있다. 따라서 이 논문은 각 관계별로 이진 결정 다이어그램 기반의 모델링 규칙을 제안한다. 이를 위해 변환 기법들을 구분하는 조건들을 정의하고, 질의 수행시간을 측정함으로 검증이 요구되는 모델링 규칙들을 평가한다. 평가 후, 이 논문은 명제 논리로 표현된 최종 모델링 규칙을 재정의하고, 사례 연구를 통하여 제안된 모델링 규칙이 설계된 시스템을 구현하는데 유용함을 보인다.

• 아동학회지
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• 제30권2호
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• pp.145-159
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• 2009

In this study, mothers of 205 4- to 5-year-old preschoolers responded to aggression episodes of Werner et al. (2006); preschoolers' teachers responded to the Preschool Social Behavior Scale (Crick et al., 1997). Results showed, (1) boys exhibited more overt and relational aggression. (2) In overt aggression episodes, mothers used encouragement to boys and rule violation responses to girls; in relational aggression episodes, mothers used encouragement and power assertion responses to girls. (3) Mothers' power assertion about overt aggression related negatively with preschoolers' overt aggressive behaviors; mothers' discussion about relational aggression related positively with preschoolers' overt aggressive behaviors. Implications of these findings for the mothers' responses by aggression types were discussed in order in better understand preschooler's aggressive behaviors.