• Proceedings of the Korea Database Society Conference
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• 1999.06a
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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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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• v.19 no.3
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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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.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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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.

• Journal for History of Mathematics
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• v.37 no.1
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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'.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.505-513
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• 2009

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. But the most of fuzzy systems are difficult to make fuzzy inference rules in the case of MIMO system. The past days, We had proposed the MIMO fuzzy inference which had extended a Z. Cao's fuzzy inference to handle MIMO system. But many times and effort needed to determine the relation matrix elements of MIMO fuzzy inference by heuristic and trial and error method in order to improve inference performances. In this paper, we propose a MIMO fuzzy inference method with the learning ability witch is used a gradient descent method in order to improve the performances. Through the computer simulation studies for the inverse kinematics problem of 2-axis robot, we show that proposed inference method using a gradient descent method has good performances.