• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2157-2164
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• 2011

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.2
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• pp.106-113
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• 2000

This study describes how a properly calibrated simulation method could be used to investigate the latchup immunity characteristics among the various high energy ion implanted CMOS twin well (retro-grade/BILLI/BL) structures. To obtain the accurate quantitative simulation analysis of retrograde well, a global tuning procedure and a set of grid specifications for simulation accuracy and computational efficiency are carried out. The latchup characteristics of BILLI and BL structures are well predicted by applying a calibrated simulation method for retrograde well. By exploring the potential contour, current flow lines, and electron/hole current densities at the holding condition, we have observed that the holding voltage of BL structure is more sensitive to the well design rule (p+to well edge space /n +to well edge space) than to the retrograde well itself.

• 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.

• 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 Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.151-158
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• 2014

In this paper, we introduce an inference system using hard scatter partition method and model the nonlinear process. To do this, we use the hard scatter partition method that partition the input space in the scatter form with the value of the membership degree of 0 or 1. The proposed method is implemented by C-Means clustering algorithm. and is used for the initial center values by means of binary split. by applying the LBG algorithm to compensate for shortcomings in the sensitive initial center value. Hard-scatter-partitioned input space forms the rules in the rule-based system modeling. The premise parameters of the rules are determined by membership matrix by means of C-Means 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. The data widely used in nonlinear process is used to model the nonlinear process and evaluate the characteristics of nonlinear process.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• ETRI Journal
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• v.27 no.2
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• pp.188-194
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• 2005

We report on the improvement of critical dimension (CD) linearity on a photomask by applying the concept of process proximity correction to a laser lithographic process used for the fabrication of photomasks. Rule-based laser process proximity correction (LPC) was performed using an automated optical proximity correction tool and we obtained dramatic improvement of CD linearity on a photomask. A study on model-based LPC was executed using a two-Gaussian kernel function and we extracted model parameters for the laser lithographic process by fitting the model-predicted CD linearity data with measured ones. Model-predicted bias values of isolated space (I/S), arrayed contact (A/C) and isolated contact (I/C) were in good agreement with those obtained by the nonlinear curve-fitting method used for the rule-based LPC.

• BMB Reports
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• v.49 no.8
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• pp.443-448
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• 2016

The arginylation branch of the N-end rule pathway is a ubiquitin-mediated proteolytic system in which post-translational conjugation of Arg by ATE1-encoded Arg-tRNA-protein transferase to N-terminal Asp, Glu, or oxidized Cys residues generates essential degradation signals. Here, we characterized the ATE1^−/− mice and identified the essential role of N-terminal arginylation in neural tube development. ATE1-null mice showed severe intracerebral hemorrhages and cystic space near the neural tubes. Expression of ATE1 was prominent in the developing brain and spinal cord, and this pattern overlapped with the migration path of neural stem cells. The ATE1^−/− brain showed defective G-protein signaling. Finally, we observed reduced mitosis in ATE1^−/− neuroepithelium and a significantly higher nitric oxide concentration in the ATE1^−/− brain. Our results strongly suggest that the crucial role of ATE1 in neural tube development is directly related to proper turn-over of the RGS4 protein, which participate in the oxygen-sensing mechanism in the cells.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.67-74
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• 1982

The dynamic behaviour of an elastically supported finite beam subjected to an axial force and moving loads with acceleration is investigated. Within the Euler beam theory the solutions are obtained by using finite Fourier and Laplace transformation methods with respect to space and time variable. Integrations involved in the theoretical results are carried out by Simpson's rule. From the results of the theoretical analysis, it is evident that dynamic behaviour of the beam are affected remarkably by acceleration and axial force.