• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.634-639
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• 1998

A methodology based on the concept of variable string length GA(VGA) is developed for determining automatically the number of hyperplanes and their appropriate arrangement for modeling the class boundaries of a given training data set in RN. The genetic operators and fitness functionare newly defined to take care of the variability in chromosome length. Experimental results on different artificial and real life data sets are provided.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.50-52
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• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.739-753
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• 2014

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1014-1021
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• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.69-74
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• 2021

This paper accumulates the product structure data such as bond obligation ratio and investment ratio for variable insurance using crawling from the insurance company's API, also accumulates variable insurance income and project expenses for variable insurance using crawling from the API of life insurance association. From these accumulated data, the correlation coefficient between fund product and customer preference is calculated with an investment algorithm, and variable insurance funds by customer investment preference and product structure are recommended according to market conditions. From the simulation results, it is shown that the proposed variable insurance management system properly recommends and manages variable insurance according to customer preferences.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.399-409
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• 2006

This paper presents a novel method for satisfying the thermal comfort of indoor environment and reducing the summer peak demand power by minimizing the power consumption for an Air-conditioner within a space. Korea Electric Power Corporation (KEPCO) use the fixed duty cycle control method regardless of the indoor thermal environment. However, this method has disadvantages that energy saving depends on the set-point value of the Air-Conditioner and direct load control (DLC) has no net effects on Air-conditioners if the appliance has a lower operating cycle than the fixed duty cycle. In this paper, the variable duty cycle control method is proposed in order to compensate the weakness of conventional fixed duty cycle control method and improve the satisfaction of residents and the reduction of peak demand. The proposed method estimates the predict mean vote (PMV) at the next step with predicted temperature and humidity using the back propagation neural network model. It is possible to reduce the energy consumption by maintaining the Air-conditioner's OFF state when the PMV lies in the thermal comfort range. To verify the effectiveness of the proposed variable duty cycle control method, the case study is performed using the historical data on Sep. 7th, 2001 acquired at a classroom in Seoul and the obtained results are compared with the fixed duty cycle control method.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.1-9
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• 2018

Recently, the rapid development of artificial intelligence industry has resulted in a great change in our modern society. Due to this background, this paper takes the United Kingdom as an example to explore the determinants of artificial intelligence industry in terms of United Kingdom's macroeconomics. The quarterly time series from the first quarter of 2010 to the fourth quarter of 2017 will be employed to conduct an empirical analysis under the vector error correction model. In this paper, the real GDP, the employment figure, the real income, the foreign direct investment, the government budget and the inflation will be regarded as independent variables. The input of artificial intelligence industry will be regarded as a dependent variable. These macroeconomic variables will be applied to perform an empirical analysis so as to explore how the macroeconomic variables affect the artificial intelligence industry. The findings show that the real GDP, the real income, the foreign direct investment and the government budget are the driving determinants to promote the development of artificial intelligence industry. Conversely, the employment figure and the inflation is the obstructive determinants to hamper the development of artificial intelligence industry.

• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.203-205
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• 2003

This paper is proposed a newly developed approach to identify the mechanical speed of an induction motor based on artificial neural networks technique. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.354-356
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• 2011

Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Solar energy is one of the most successful sources of renewable energy for the production of electrical energy following solar energy. And, the solar/photovoltaic cell generators depend on the solar radiation, which is a random variable so this poses difficulty in the system scheduling and energy dispatching, as the schedule of the photovoltaic cell generators availability is not known in advance. This paper proposes to use the two-layered artificial neural networks for predicting the actual solar radiation from the previous values of the same variable.