• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.353-359
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• 1999

We present a matrix-based inference algorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation. 2. Matrix operation: All the subjective knowledge is delineated in a matrix form. so that inference process can proceed based on the matrix operation which is computationally efficient. 3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.353-359
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• 1999

We present a matrix-based inference alorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matric computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.49-52
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• 한국지능시스템학회논문지
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• 제13권6호
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• pp.743-748
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database (RDB) and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert system. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently. and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• Journal of Mechanical Science and Technology
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• 제19권3호
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• pp.743-755
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• 2005

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

• 전자공학회논문지B
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• 제33B권2호
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• pp.118-130
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• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• 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.

• 한국수학사학회지
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• 제37권1호
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• pp.1-17
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• 2024

In this study, we identified the intellectual triggers for Bayesian inference and what key ideas contributed to its occurrence and discussed the practical implications of introducing Bayesian inference into the school mathematics curriculum by reflecting them. The results of the study show that the need for statistical inference about the parameter itself served as a trigger for the occurrence of Bayesian inference, and the most important idea for the occurrence of that inference was to regard the parameter itself as a probability variable rather than any fixed value. On the other hand, these research results suggest that the meaning of introducing Bayesian inference into the secondary mathematics curriculum is 'statistics education that expands the scope of uncertainty'.

• 한국정밀공학회지
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• 제15권5호
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• pp.161-166
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• 1998

As a part of the configuration design expert system of machine tools, inference mechanisms are constructed in this paper. In addition to procedural inference, the method of multivariable inference is considered as an efficient approach to deal with the cases of highly coupled condition. We propose a generalized multi-variable inference procedure. The procedure is applied to the type selection module of the configuration design expert system of machine tools in order to demonstrate the efficiency and validity.

• 한국정보통신학회논문지
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• 제13권3호
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• pp.505-513
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• 2009

Z. cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지 추론방식에 비하여 좋은 성능을 보였다. 그러나 대부분의 퍼지스템의 경우, MIMO 시스템에 적용 시 퍼지 추론규칙을 도출해 내기 힘들고 많은 규칙의 수가 요구되는 단점을 갖는다. 그러므로 본 연구자에 의하여 과거에 Z. Cao's의 퍼지 추론방법을 MIMO 시스템으로 확장된 MIMO 퍼지추론 방식이 제안되었다. 그러나 정밀한 추론을 위하여 relation matrix는 휴리 스틱 (heuristic)한 방법이나 시행착오법을 사용하여 구하였고, 이는 많은 시간과 노력이 필요하다. 본 연구에서는 이러한 relation matrix를 구하기 위하여 시행 착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사용한 학습기 능을 갖는 MIMO 퍼지추론 방식을 제안하고자 한다. 모의실험은 2축 로봇의 역기구학 문제를 푸는데 적용하여 제안된 추론방식이 좋은 성능을 보였다.