• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.123-129
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• 2007

On the Semantic Web, the content of the documents must be explicitly represented through metadata in order to enable contents-based inference. In this study, we propose a mechanism to convert the Conceptual Graph (CG) into Resource Description Framework (RDF). Quite a large number or representation languages for representing knowledge on the Web have been established over the last decade. Most of these researches are focused on design of independent knowledge description. On the Semantic Web, however, a knowledge conversion mechanism will be needed to exchange the knowledge used in independent devices. In this study, the CG could give an entire conceptual view of knowledge and RDF can represent that knowledge on the Semantic Web. Then the CG-based object oriented PROLOG could support the natural inference based on that knowledge. Therefore, our proposed knowledge conversion mechanism will be used in the designing of Semantic Web-based knowledge representation and inference systems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.90-96
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• 2005

This paper presents hardware scheme of fuzzy inference engine, based on α-level set decomposition of fuzzy sets for fuzzy control of DC servo system. We propose a method which is directly converted to PWM actuating signal by a one body of fuzzy inference and defuzzification. The influence of quantity α-levels on input/output characteristics of fuzzy controller and output response of DC servo system is investigated. It is concluded that quantity α-cut 4 give a sufficient result for fuzzy control performance of DC servo system. The experimental results shows that the proposed hardware method is effective for practical applications of DC servo system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.13-16
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• 2000

In fuzzy expert systems or database systems that have huge volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environment. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy rules or data, the parallel fuzzy inference algorithm extracts effective parallel ism and achieves a good speed factor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1054-1057
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• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.29-43
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• 2008

Although RDF ontologies might be expressed in XML tree model, existing methods for protection of XML documents are not suitable for securing RDF ontologies. The graph style and inference feature of RDF demands a new security model development. Driven by this goal, this paper proposes a new query-oriented model for the RDF ontology access control. The proposed model rewrites a user query using a three-layered ontology view. The proposal resolves the problem that the existing approaches should generate inference models depending on inference rules. Accessible ontology concepts and instances which a user can visit are defined as ontology views, and the inference view defined for controling an inference query enables a controlled inference capability for the user. This paper defines the three-layered view and describes algorithms for query rewriting according to the views. An implemented prototype with its system architecture is shown. Finally, the experiment and comparative evaluation result of the proposal and the previous approach is described.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.71-83
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• 1999

Conventional symbolic inference systems lack flexibility because they do not well reflect flexible semantic structure of knowledge and use symbolic logic for their basic inference mechanism. For solving this problem. we have recently proposed the 'Connectionist Semantic Network(CSN)' as a model for flexible knowledge representation and inference based on neural networks. The CSN is capable of carrying out both approximate reasoning and commonsense reasoning based on similarity and association. However. we have difficulties in representing general and structured high-level knowledge and variable binding using the connectionist framework of the CSN. In this paper. we propose a hybrid system called SymCSN(Symbolic CSN) that combines a symbolic module for representing general and structured high-level knowledge and a connectionist module for representing and learning low-level semantic structure Simulation results show that the SymCSN is a plausible model for human-like flexible knowledge representation and inference.

• Korean Journal of Logic
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• v.16 no.1
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• pp.87-115
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• 2013

In my previous article "The Uncontested Principle and Wonbae Choi's Objections", I argued that the validity of modus ponens (as a deductive inference) is compatible with the claim that the Uncontested Principle is controversial. In his recent paper "The Uncontested Principle and Modus Ponens", Wonbae Choi criticizes my view again by making the following three claims: First, even though I do not take an inference of the form 'If A then (probably) C. A. $\therefore$ C' as an instance of modus ponens, this form of inference can be taken to be such an instance. Second, there is no grammatical indicator which allows us to distinguish between an indicative conditional based on a deductive inference and an indicative conditional based on an inductive inference, so that inferences based on these conditionals should not be treated as different types of inferences. Third, if we allow an indicative conditional based on an inductive inference, we thereby violate the so-called 'principle of harmony', which any logical concept should preserve. In this paper, I reply that his criticisms are all implausible.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.488-501
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. But such services are usually limited to those served on a single mobile device. To resolve the resource limitation problem of mobile devices, a nearby resource sharing research has been studied. Also, not only the nearby resource share but also a resource recommendation through context-based resource reasoning has been studied such as an UMO Project. The UMO Project share and manage the various context information for the personalization resource recommendation and reason based on current context information. Also, should verify resource inference rules for reliable the resource recommendation. But, to create various context information requires huge cost and time in actuality. Thus, we propose a inference verification tool called USim to resolve problem. The proposed inference verification tool provides convenient graphic user interfaces and it easily creates context information. The USim exactly verifies new inference rules through dynamic changes of context information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.