• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Journal of the Korea Organic Resources Recycling Association
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• v.23 no.3
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• pp.68-77
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• 2015

This study was conducted to characterize food waste leachate (FWL) and to compare its characteristics according to generation source: feed supplement- and compost-producing facilities. FWL contained high levels of organic compounds such as carbohydrate, protein and lipid. The moisture content of FWL was among the range of conventional wet anaerobic digestion. FWL was acidic (pH of $4.0{\pm}0.3$) and showed high coefficients of variance for specific parameters: carbohydrate, protein, lipid, ethanol, acetic acid and propionic acid. FWL derived from feed supplement-producing facilities showed slightly lower concentrations of most parameters than FWL derived from compost-producers. However, the difference was not significant at 5% significance level according to analysis of variance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3479-3489
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• 2000

Most of the study on fairness control method for Distributed-Queue Dual-Bus(DQDB) have been performed under specific load types such as equal probability load types or symmetric load types. On Web-based internet enviroments client-server load types are more practical traffic patlerns than specrfic load types. In this paper, an effiective fairness control method to distribute DQDR network bandwidth fairly to all stations under a client-server load is proposed. In order to implement a dynamic bandwidth timing capabihty needed to distribute the bandwidth fairty at heavy loads, the proposed method uses two pararnetexs, one is an access hrnit to legulate each station's packet transmission and the other is the number of extra emply slots that are yielded to downstream stations. In point of implementation this mechanism is simpler and easier than Bandwidth Tuning Mechanism(BTM) that uses an intermediate pattern and an adptation function. Simulation results show that it outperforms othen mecharusms.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.570-570
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• 2012

그래핀(graphene)은 육각형의 탄소원자 한층으로 이루어진 이차원 구조체로써 우수한 물리적, 전기적 특성으로 인해 다양한 분야에서 응요을 위한 연구가 활발히 진행되고 있다. 특히, 그래핀과 금속 나노입자의 복합구조는 수소 저장체, 가스센서, 연료전지, 화학 촉매등의 다양한 분야에서 응용이 가능하다. 현재까지 그래핀/금속나노입자 복합구조의 제작 방법에는 열증발(thermal evaporation), 전기도금법(electrodeposition), 표면 기능화(surface functionalization)를 이용한 방법이 보고되었다. 하지만 이러한 방법은 긴 공정시간이 요구되며, 나노입자의 크기 분포가 넓다는 단점을 지닌다. 본 연구에서는 화학기상증착법을 통해 합성된 그래핀이 전사된 SiO2 (300nm)/Si 기판에 염화기가 포함된 백금 화합물 분산용액을 스핀코팅(spin-coating)하고 MeV 전자빔을 조사하여 Pt/grapheme 복합구조를 형성하였다. 이 방법은 균일한 크기 분포의 나노입자의 형성이 가능하며, 간단하고, 대면적 공정이 가능하며, 다른 방법에 비해 그래핀의 결함형성이 적다는 장점을 지닌다. Pt/grapheme 의 기하학적 구조를 주사전자현미경(scanning electron microscopy)와 투과전자현미경(transimission)을 통해 분석하였고, Pt와 graphene의 일함수(workfunction)의 차이에 의해 야기되는 전하이동에 의한 도핑(doping)현상을 라만 분광기(Raman spectroscopy)와 X-선 광전자 분광기(X-ray photoelectron spectroscopy)를 통해 분석하였다.

• Journal of the Korean Ceramic Society
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• v.39 no.11
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• pp.1042-1047
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• 2002

The sedimentation characteristics of ceramic fiber were analyzed when viscosity of the slurry for ceramic paper formation was varied and zeta potential change and degree of dispersion with pH were studied as well. The proper viscosity of the slurry for dispersion of fibers was between 28 and 31 cps. Zeta potential of the slurry was sensitively changed with pH adjustment and showed maximum value of -35~-36 mV at ph 7.5~9.5, which indicated better dispersion of ceramic fiber as zeta potential of the slurry was increased. The sedimentation rate of ceramic fiber in a slurry was reported minimum at the maximum zeta potential. Water content of the casted paper should be lower than 83% after vacuum dehydration for retention of binder and lower than 62% after press rolling for wet paper handling. The obtained ceramic paper had tensile strength and basis weight, $102 kgf/cm^2$ and $98 g/m^2$, respectively.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.5
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• pp.369-378
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• 2003

This paper presents design and VLSI implementations of a reassembly processor for ATM/AAL. The assembly processor is responsible for processing ATM cells from the receive physical interface. It controls the transfer of the AAL payload to host memory and performs all necessary SAR and CPCS checks. We propose the improved structure of cell identification algorithm and smart scatter method for host memory management. The proposed cell identification algorithm quickly locates the appropriate reassembly VC table based on the received VPI./VCI channel value in the ATM header. The cell identification algorithm also allow complete freedom in assignment of VCI/VPI values. The reassembly processor uses a smart scatter method to write cell payload data to host memory. It maintains the scatter operation and controls the incoming DMA block during scatter DMA to host memory. The proposed reassembly processor can perform reassembly checks on AAL. OAM cell. For an AAL5 connection, only CPCS checks, including the CRC32, are performed. In this paper, we proposed a practical reassembly architecture. The design of reassembly processor has become feasible using 0.6${\mu}{\textrm}{m}$ CMOS gate array technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.378-384
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• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.975-981
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• 2013

A robust optimization is only one of the ways to minimize the effects of variances in design variables on the objective functions at the preliminary design stage. To predict the variances and to formulate the probabilistic constraints are the most important procedures for the robust optimization formulation. Though several methods such as the process capability index and the six sigma technique were proposed for the prediction and formulation of the variances and probabilistic constraints, respectively, there are few attempts using a percent defective which has been widely applied in the quality control of the manufacturing process for probabilistic constraints. In this study, the robust optimization for a lower control arm of automobile vehicle was carried out, in which the design space showing the mean and variance sensitivity of weight and stress was explored before robust optimization for a lower control arm. The 2nd order Taylor expansion for calculating the standard deviation was used to improve the numerical accuracy for predicting the variances. Simplex algorithm which does not use the gradient information in optimization was used to convert constrained optimization into unconstrained one in robust optimization.