• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.184-190
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• 2005

The railway characteristic, which is concerned, as most utilities is unbalance produced by the large single-phase loads. Here are two theoretical concerns associated with unbalanced loads. First, generator rotor heating resulting from unbalanced current flow, Second, there is the possibility of motor overheating in industrial plants, due to the unbalanced voltage. Therefore, the exact assessment of the voltage unbalance must be carried out preferentially as well as load forecast at stages of designing and planning for the electric railway system. This paper proposes a new analysis model to more effectively estimate voltage unbalance. Numerous distributed circuits in the electric railway system are composed by components. The entire system can be easily modeled by the combination of four-port representation of each component in parallel and/or series. Simulation results using the model are compared with field data, and it verifies the accuracy of the proposed model.

• Steel and Composite Structures
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• v.20 no.1
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• pp.43-56
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• 2016

Polyurethane foam with low density used in sandwich panels is examined in the paper. A series of experiments was carried out to identify mechanical parameters of the foam. Various experimental methods were used for determining the shear modulus, namely a four and three point bending tests (the most common in engineering practice), a double-lap shear test and a torsion test. The behavior of PU in axial compression and tension was also studied. The experiments revealed pronounced anisotropy of the PU foam. An orthotropic model is proposed. Limitations of application of isotropic model of PU in engineering practice is also discussed.

• Geomechanics and Engineering
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• v.11 no.5
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• pp.691-711
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• 2016

In this study, the uplift capacity of anchor plates embedded in sand was investigated by conducting model tests. Square shaped anchors were used in the tests and parameters such as relative density of sand, embedment ratio (H/B), spacing ratio between anchors (S/B) and anchor configuration affecting the uplift capacity were investigated. Breakout factor and group efficiency which are dimensionless parameters were used to show the results. A series of finite element analyses and analytical solutions were additionally performed to ascertain the validity of the findings from the laboratory model tests and to supplement the results of the model tests. It can be concluded that the embedment depth in dense sand soil condition is the most important parameter with respect to the other parameters as to influencing the uplift capacity of group anchors.

• Journal of Magnetics
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• v.22 no.2
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• pp.196-202
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• 2017

The occurrence of ferroresonance in electrical systems including nonlinear inductors such as transformers will bring a lot of malicious damages. The intense ferromagnetic saturation of the iron core is the most influential factor in ferroresonance that makes nonsinusoidal current and voltage. So the nonlinear behavior modeling of the magnetic core is the most important challenge in the study of ferroresonance. In this paper, the ferroresonance phenomenon is investigated in a single phase transformer using the finite element method and considering the hysteresis loop. Jiles-Atherton (JA) inverse vector model is used for modeling the hysteresis loop, which provides the accurate nonlinear model of the transformer core. The steady-state analysis of ferroresonance is done while considering different capacitors in series with the no-load transformer. The accurate results from copper losses and iron losses are extracted as the most important specifications of transformers. The validity of the simulation results is confirmed by the corresponding experimental measurements.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1100-1102
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• 2000

Most systems in tile real world are non-linear and can be represented by the non-linear autoregressive moving average (NARMA) model. The extension of fuzzy system for modeling the system that is represented by NARMA model will be proposed in this paper. Here, fuzzy basis function (FBF) is used as fuzzy NARMA(p,q) model. Then, an approach to Identify fuzzy NARMA models based on fuzzy basis functions is proposed. The efficacy of the proposed approach is shown from experimental results.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09c
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• pp.49-54
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• 2010

In order to study the performance of levees reinforced with steel sheet piles under high water condition, a series of model tests was conducted by simulating the high water condition before and after applying severe seismic loading history. As a result, the seepage behavior through the subsoil layers underlying the levee was not significantly affected by the seismic loading history. It was also verified that, irrespective of the seismic loading history, the sheet piles installed at the levee crest or shoulder are effective in preventing the breakage of levees caused by overflow. In addition, applicability of drainage works at the foot of the levee in preventing the seepage failure was confirmed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.10
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• pp.579-586
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• 1999

This paper describes a simulation model and a scaled hardware model to analyze the dynamic performance of Unified Power Flow Controller, which can flexibly adjust the active power flow through the ac transmission line. The design of control system for UPFC was developed using vector control method. The results of simulation and scaled hardware test show that the developed control system works accurately. Both models would be very effective for analyzing the dynamic performance of the Unified Power Flow Controller.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.