• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.127-130
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• 1990

The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.517-522
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• 2000

When a real robot manipulator is mathematically modeled. uncertainties are not avoidable. The uncertainties are often nonlinear and time-varying. The uncertain factors collie from imperfect knowledge ok system parameters. payload change. friction. external disturbance. and etc. In this paper. we propose a class of robust hybrid controls of manipulators without knowing the exact stiffness and provide the stability analysis. Simulation results are provided to show the effectiveness of the algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.201-210
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• 1994

We proposed so-called AFHR (adaptable fuzzy hyper-resolution principle) that can manipulate uncertain knowledge and tune the range of resolution. The AFHR can make a rangable resolution to execute an efficient resolution and can represent linguistic truth values. In this paper, we introduce new concepts of law of contrary, meaningless range level for truth values and strict degree of adaptable resolution. We show that the differences of AFHR and existing fuzzy resolution. Finally we prove completeness of AFHR.

• Knowledge Management Research
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• v.10 no.1
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• pp.53-70
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• 2009

This study empirically explored what promotes exploration, through a case analysis of a Korean SME (small and medium sized enterprise), based on the research framework which focuses on the identification and the selection of exploratory NPD (new product development) alternatives, and the accumulation of novel capabilities in new technology domains. The learning process of the exploratory NPD project described is as follows. The identification barrier of exploratory NPD project is relatively low. Constructive crisis is germane to selecting exploratory NPD alternatives and to enduring the long payback period. New separated R&D unit is likely to implement the exploratory NPD project. The length of the gestation period of the exploratory NPD project is related with the level of the conflict between old members and new members. This study identified several antecedents of the exploratory NPD project. Prior success promotes the identification process of the exploratory NPD projects. Constructive crisis is related with CEO's personal characteristics such as future oriented and proactive personality. The proactive involvement and persuasion of CEO are germane to reducing the conflict between old and new members and to the success of the exploratory NPD project. Based on the results, this study discusses several implications and future research directions.

• Journal of the Korean earth science society
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• v.27 no.7
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• pp.705-714
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• 2006

The purpose of this study was to explore the elementary students' perceptions of Earth systems and environmental issues. A survey was conducted to determine the students' perceptions on the following aveas: (1) the concepts of certainty and tangibility, (2) self-reported knowledge level, (3) perceived danger level of selected eight Earth systems and environmental issues, and (4) their primary information source on these issues. Results indicated that ozone hole, acid rain, El $Ni\widetilde{n}o$, and global warming were identified by the students as uncertain and intangible issues. Perceived certainty and perceived tangibility were highly positively correlated with self-reported knowledge compared to other relationships. The results also showed that learning from school was the most frequent information source for environmental issues. The second most frequently used source of information was television among several mass media sources. It is hoped that this study contributes to understanding the elementary school students' perceptions toward the selected Earth systems and environmental issues.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.42-53
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• 2007

Looking back the history of formal treatment of linguistics, we cannot disregard the contribution of possible world semantics. Intensional logic of Montague semantics, DRT (Discourse Representation Theory), mental space, and situation theory are closely related to or compared with the notion of possible world. All these theories have commonly clarified the structure of belief context or uncertain knowledge, employing hypothesized worlds. In this talk, I firstly brief the pedigree of these theories. Next, I will introduce the recent development of modal logic for the representation of (i) knowledge and belief and (ii) time, in which belief modality is precisely discussed together with the accessibility among possible worlds. I will refer to BDI (belief-desire-intention) logic, CTL (computational tree logic), and sphere-based model in belief revision. Finally, I will discuss how these theories could be applied to the further development of analyses of natural language.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.401-403
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• 1996

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, one often bas only a small number of state measurements. This could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network is employed to solve this kind of problem. The neural network serves as a compensator without a prior knowledge about the system. The proposed control algorithm is applied to a class of uncertain nonlinear system. The robustness against parameter uncertainty, nonlinearity and external disturbances, and the effectiveness is verified by the simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Knowledge Management Research
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• v.24 no.1
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• pp.173-193
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• 2023

Organizational effectiveness has long been the most central topic of organizational research, but the concept is not only capable of various operational definitions depending on the purpose of the study, but it is an uncertain concept composed of various measurement elements. In this review, we identified measurement variables by type of research subjects such as public organizations, private organizations, research organizations, and production organizations, and examined job satisfaction and organizational commitment, which were the most studied sub-variables.