• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.7-13
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• 2002

Almost applications using fuzzy theory are based on the fuzzy inference. However fuzzy inference needs much time in calculation process for the fuzzy system with many input variables or many fuzzy labels defined on each variable. Inference time is dependent on the number of arithmetic Product in computation Process. Especially, the inference time is a primary constraint to fuzzy control applications using microprocessor or PC-based controller. In this paper, a simple fast fuzzy inference algorithm(FFIA), without loss of information, was proposed to reduce the inference time based on the fuzzy system with triangular membership functions in antecedent part of fuzzy rule. The proposed algorithm was induced by using partition of input state space and simple geometrical analysis. By using this scheme, we can take the same effect of the fuzzy rule reduction.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.239-250
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• 2013

Context-aware mobile computing has become the primary approach to realize automatic, autonomous, and user-centric computing in the context of largely increasing the amount of mobile devices used that embed available sensors. However, designing an inference engine nonetheless requires the tasks of analyzing contexts, situations that can be inferred, etc. Moreover, a mobile device has limited resources and limited computation capability, which results in recognizing the common sense of its unsuitable environment for processing inference. Hence, we propose context-situation reasoning elements and their formal models in this paper, and we verify the formal models' applicability by applying them to an example. Finally, we design and implement an inference engine that realize the context-situation inference elements in computing environment, and we experiment an example by using the proposed inference engine to verify applicability and reusability of the inference engine.

• Journal of the Korean Statistical Society
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• v.17 no.2
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• pp.93-100
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• 1988

Two properties of ancillary statistics are considered. One is to find a role of ancillary statistics in the statistical inference by showing that the ancillary statistic can recover the lost information and to give a criteria for comparing the conditional inference with unconditional inference. The other is to find an ancillary statistic of translation model and its relationship with observed Fisher information.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.704-709
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• 1993

In this paper, a method of using a look-up table for a fuzzy logic controller is proposed. A look-up table is designed for a fast inference. An algorithm for an inference is developed with a view to decrease execution time. The performance of the developed fuzzy controller is compared with that of the traditional one.

• Child Studies in Asia-Pacific Contexts
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• v.5 no.2
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• pp.83-94
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• 2015

The purpose of this study was to understand the influence of personality trait information on young children's perception of initial attraction in peer relationships. The sample consisted of 90 children of three to five years of age in South Korea. Children were presented with an inductive inference task where they had to make inference of a target character's preference on novel-play and prosocial act based on trait labels (smart-not smart, outgoing-shy, nice-mean) and perceptual (toy) similarity information of two test characters. Children showed difference in their use of trait information depending on the perceptual similarity information, trait valence, and inference question with age. This result provides initial support that not only do young children understand the significance of trait in peer attraction but also know when trait label is more informative to use to infer others depending on the situation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1856-1859
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• 1997

This paper describes the arousal measurement and the control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Membership function and rule base were determined form the modfied arousal level criteria. The output of fuzzy inference tracked well the change of subject's arousal level. When IRI(Inter-SIR interval) was under the 60sec, maximum output of three step warning method was medium sound, but that of fuzzy logic system was changed from medium to big. Furthermore, the output of the fuzzy inference was highly correlated with $N_{z}$(r=0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving sityation than three step warning method.ning method.

• 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.4 no.1
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• pp.18-22
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• 2004

It is difficult to understand a person's emotion, since it is subjective and vague. Therefore, we are proposing a method by which will effectively classify human emotions into two types (that is, single emotion and composition emotion). To verify validity of te proposed method, we conducted two experiments based on general inference and $\alpha$-cut, and compared the experimental results. In the first experiment emotions were classified according to fuzzy inference. On the other hand in the second experiment emotions were classified according to $\alpha$-cut. Our experimental results showed that the classification of emotion based on a- cut was more definite that that based on fuzzy inference.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.341-344
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• 1991

Rules in expert system have meaning of assigning never-happen-minterms. Overall logical relations of variables can be achived by making all prime implicants of never-happen-minterms. From prime implicants, two tables, which are necessary in the process of inference, are constructed. There are two inferencing modes. One excutes inference only one variable which the user is interested in, and the other excutes inference all variables simultaneously. Outputs of inference have not only 'true' or 'false' but also 'unknown' which is different from conventional expert system. In this paper, an efficient approach is presented, which can check logical inconsistency in knowledge base and contradiction between input facts and rules. The methods in the paper may be available in the field of diagnosis and alarm processing.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.27-44
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• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.