• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.365-369
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• 1997

In this paper, solar array of KOMPSAT was modeled and analyzed. The modeling results of solar array was achieved by neural algorithm, which is powerful of nonlinear system with a few data sets. The algorithm was analyzed and verified by simulation considering on solar cell data of KOMPSAT. The characteristics VI curves and power generation of solar array are analyzed by using the modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.1
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• pp.8-16
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• 1986

For digital process control, a low order discrete time modeling method is suggested and a direct digital control algorithm has been developed. The modeling method maintains process order of 3, while the sampling rate is doubled for fast response. With easy calculation it is possible to compute the model parameters and the controller gains. Controller tuning is possible on the spot. Simulation results show that this method has better performance than the deadbeat control agorithm.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.159-166
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• 1994

A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

• Membrane and Water Treatment
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• v.9 no.5
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• pp.363-371
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• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.432-441
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.724-729
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• 2016

Nowadays high-cost energy storage system using flywheel or secondary battery is applying to hybrid generation system with WT(Wind Turbine) and diesel generator in island areas for stable operation. This paper proposes an operating algorithm and modeling method of the hybrid generation system that can operate for variable wind speed and load, which is composed of energy storage system, variable-speed PMSG(Permanent Magnet Synchronous Generator) WT and diesel generator applied in island areas. Initially, the operating algorithm was proposed for frequency and voltage to be maintained within the proper ranges for load and wind speed changes. Also, the modeling method is proposed for variable speed PMSG WT, diesel generator and BESS(Battery Energy Storage System). The proposed operating algorithm and modeling method were applied to a typical island area. The frequency and voltage was kept within the permissible ranges and the proposed method was proven to be appropriate through simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1200-1207
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• 2009

The GMDH(Group Method of Data Handling) algorithm can be used to model the complex nonlinear systems. The traditional GMDH algorithm produces the output of the system model in the output layer through the input layer and the intermediate layers as the prescribed process. The outputs of each layer are produced only by the outputs of the former layer. However among the inputs there may be the inputs which can influence the modeling result more than the other inputs. Therefore in this paper the method which improve the modeling capability by interlayer connection of more influential inputs is proposed. The capability improvement of the proposed algorithm compared to the traditional algorithm is verified through computer simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.766-770
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• 2005

Cascade-Correlation Neural Networks Model(CCNNM) is used to estimate daily evaporation using limited climatical variables such as atmospheric temperature, dewpoint temperature, relative humidity, wind speed, sunshine duration and radiation. DeBruln equation is applied to estimate daily free-surface evaporation. It is converted into pan evaporation using pan coefficient. The results of CCNNM shows better than those of Debruin equation. This research represents that the strong nonlinear relationship such as evaporation modeling can be generalized by the CCNNM ; a special type of Backpropagation algorithm Neural Networks Model.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.50_51
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• 2009

This paper describes the novel real-time embeded Dynamic System Monitor(KDSM) for dynamic device modeling and disturbace monitoring. The KDSM uses the variable resampling technique together with DFT algorithm so that it overcomes the shortcomings of the existing DFT algorithm at the big deviation of network frequency. The suggested algorithm is implemented by using the NI-PXI system, and verified by applying to the generator testing.