• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.297-300
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• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.53-58
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• 2002

In this paper, various aspects in magnetoencephalography (MEG) source localization are studied. To minimize the errors in experimental data, an approximation technique using a polynomial function is proposed. The simulation shows that the proposed technique yields more accurate results. To improve the convergence characteristics in the optimization algorithm, a hybrid algorithm of evolution strategy and sensitivity analysis is applied to the neuromagnetic inverse problem. The effectiveness of the hybrid algorithm is verified by comparison with conventional algorithms. In addition, an artificial neural network (ANN) is applied to find an initial source location quickly and accurately. The simulation indicates that the proposed technique yields more accurate results effectively.

• ETRI Journal
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• v.36 no.6
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• pp.953-959
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• 2014

This paper studies the uplink resource allocation for multiple radio access (MRA) in reconfigurable radio systems, where multiple-input and multiple-output (MIMO) multicarrier-code division multiple access (MC-CDMA) and MIMO orthogonal frequency-division multiple access (OFDMA) networks coexist. By assuming multi-radio user equipment with network-guided operation, the optimal resource allocation for MRA is analyzed as a cross-layer optimization framework with and without fairness consideration to maximize the uplink sum-rate capacity. Numerical results reveal that parallel MRA, which uses MC-CDMA and OFDMA networks concurrently, outperforms the performance of each MC-CDMA and OFDMA network by exploiting the multiuser selection diversity.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• ETRI Journal
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• v.36 no.4
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• pp.643-653
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• 2014

To reduce interconnect delay and power consumption while improving chip performance, a three-dimensional integrated circuit (3D IC) has been developed with die-stacking and through-silicon via (TSV) techniques. The power supply problem is one of the essential challenges in 3D IC design because IR-drop caused by insufficient supply voltage in a 3D chip reduces the chip performance. In particular, power bumps and TSVs are placed to minimize IR-drop in a 3D power delivery network. In this paper, we propose a design methodology for 3D power delivery networks to minimize the number of power bumps and TSVs with optimum mesh structure and distribute voltage variation more uniformly by shifting the locations of power bumps and TSVs while satisfying IR-drop constraint. Simulation results show that our method can reduce the voltage variation by 29.7% on average while reducing the number of power bumps and TSVs by 76.2% and 15.4%, respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.964-967
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• 1993

This paper presents an auto tuning method of fuzzy inference using Genetic Algorithm. The determination of membership functions by human experts is a difficult problem. Therefore, some auto-tuning methods have been proposed to reduce the time-consuming operations. However, the convergence of the tuning by the previous methods depends on the initial conditions of the fuzzy model. So, we proposes an auto tuning method for the fuzzy neural network by Genetic Algorithm (ATF system). This paper shows effectiveness of the ATF system by simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.132-139
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• 2008

This paper proposes a scatternet topology formation, self-healing, and self-routing path optimization algorithm based on Relative Neighborhood Graph. The performance of the algorithm using ns-2 and extensible Bluetooth simulator called blueware shows that even though RNG-FHR does not have superior performance, it is simpler and easier to implement in deploying the Ad-Hoc network in the distributed dynamic environments due to the exchange of fewer messages and the only dependency on local information. We realize that our proposed algorithm is more practicable in a reasonable size network than in a large scale.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2103-2108
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• 2011

In this paper, the problem of passivity analysis for neural networks with time-varying delays and norm-bounded parameter uncertainties is considered. By constructing a new augmented Lyapunov functional, a new delay-dependent passivity criterion for the network is established in terms of LMIs (linear matrix inequalities) which can be easily solved by various convex optimization algorithms. Two numerical example are included to show the effectiveness of proposed criterion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1809-1814
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• 2009

In this paper, the problem of stability analysis for neural networks with discrete and distributed time-varying delays is considered. By constructing a new Lyapunov functional, a new delay-dependent stability criterion for the network is established in terms of LMIs (linear matrix inequalities) which can be easily solved by various convex optimization algorithms. Two numerical example are included to show the effectiveness of proposed criterion.