• Korean Journal of Mathematics
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• v.9 no.2
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• pp.149-156
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• 2001

We characterize some properties of $t$-fuzzy groups and $t$-fuzzy invariant groups and represent every subgroup S of a group X using the level set of $t$-fuzzy group constructed from S.

• Korean Journal of Mathematics
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• v.10 no.2
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• pp.149-155
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• 2002

We develop some basic properties of $t$-fuzzy ideals in a monoid or a group and find the sufficient conditions for a fuzzy set in a division ring to be a $t$-fuzzy subring and the necessary and sufficient conditions for a fuzzy set in a division ring to be a $t$-fuzzy ideal.

• Journal of the Chungcheong Mathematical Society
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• v.20 no.4
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• pp.439-447
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• 2007

In this paper, we introduce the notion of T-fuzzy $\mathcal{M}{\Gamma}$-subgroup of $\mathcal{M}{\Gamma}$-group, and give its characterization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.285-292
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• 1998

We introduce the notion of TL-p-subgroups that is an ex-tension of the notion of fuzzy p=subgroups and show that a torsion TL-subgroup of an Abelian group with T=${\bigwedge}$ can be written as the intersection of its minimal TL-p-subgroups.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.3-6
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• 1998

We introduce the notion of TL-p-subgroups that is an extension of the notion of fuzzy p-subgroups and show that a torsion TL-subgroup of an Abelian group with T=∧ can be written as the intersection of its minimal TL-p-subgroups.

• Structural Engineering and Mechanics
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• v.56 no.3
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• pp.385-407
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• 2015

This paper deals with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems. It is well known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system. Finding system parameters for stabilizing the control system is also an issue need to be concerned. In this paper, we give additional sufficient conditions for the global stabilization of a TLP nonlinear system. In particular, we consider a class of NN based Takagi-Sugeno (TS) fuzzy TLP systems. Using the so-called parallel distributed compensation (PDC) controller, we prove that this class of systems can be globally asymptotically stable. The proper design of system parameters are found by a swarm intelligence algorithm called Evolved Bat Algorithm (EBA). An illustrative example is given to show the applicability of the main result.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.29-43
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• 2019

In recent years, as the network traffic in the WSN(Wireless Sensor Network) has been increased by the growing number of IoT wireless devices, SDWSN(Software-Defined Wireless Sensor Network) and its security that aims a secure SDN(Software-Defined Networking) for efficiently managing network resources in WSN have received much attention. In this paper, we study on how to efficiently and securely design a PUF(Physical Unclonable Function)-assisted group key distribution scheme for the SDWSN environment. Recently, Huang et al. have designed a group key distribution scheme using the strengths of SDN and the physical security features of PUF. However, we observe that Huang et al.'s scheme has weak points that it does not only lack of authentication for the auxiliary controller but also it maintains the redundant synchronization information. In this paper, we securely design an authentication process of the auxiliary controller and improve the vulnerabilities of Huang et al.'s scheme by adding counter strings and random information but deleting the redundant synchronization information.