• Spatial Information Research
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• v.15 no.3
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• pp.323-333
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• 2007

Recently, the South and North Korea have collaborated in the economic cooperation. Success for the cooperation of the South and North Korea has supported the basic and fundamental GIS data building for geographic information (GI) and the land-use of the North Korea. This North Korea GIS project is also vital to facilitate rebuilding and reconnecting socio-economic infrastructures such as reconnecting road and railway networks between the South and North Korea. Thus, this paper emphasizes on the fundamental issues of GIS data building in North Korea area and suggests GI and data establishment methods of the North Korea regions which has not been achieved in GIS research activities in Korea. As the basic GI and data in the North Korea, topographical maps, satellite imageries, and thematic maps were collected and used for surveying of territorial areas of the North Korea. The database of those dataset were built by scanning, image processing, and classifying land-use types. In addition, this paper exacted vector data from the database and included the vector data into the database as other basic GI dataset that enable to analyze quantitative and qualitative territorial land use and development in the North Korea.

• Korean Journal of Metals and Materials
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• v.56 no.11
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.17-23
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• 2019

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1177-1183
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• 2009

In this paper, support vector machines (SVM) are applied to multi-user detector (MUD) for direct sequence (DS)-CDMA system. This work shows an analytical performance of SVM based multi-user detector with some of kernel functions, such as linear, sigmoid, and Gaussian. The basic idea in SVM based training is to select the proper number of support vectors by maximizing the margin between two different classes. In simulation studies, the performance of SVM based MUD with different kernel functions is compared in terms of the number of selected support vectors, their corresponding decision boundary, and finally the bit error rate. It was found that controlling parameter, in SVM training have an effect, in some degree, to SVM based MUD with both sigmoid and Gaussian kernel. It is shown that SVM based MUD with Gaussian kernels outperforms those with other kernels.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.142-150
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• 1994

This paper deals with fast competitive learning using classified learning rates. The basic idea of the proposed method is to assign a classified learning rate to each weight vector. The weight vector associated with an output node is updated using its own learning rate. Each learning rate is changed only when its corresponding output node wins the competition, and the learning rates of the losing nodes are not changed. The experimental results obtained with image vector quantization show that the proposed method learns more rapidly and yields better quality that conventional competitive learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1279-1284
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• 2012

This study is about the change of coastline through using ASTER images. ASTER image is a sensor loaded in earth resources satellite shoot in Japan on Dec. 1999. It has 15m, 30m, 90m coastline, three sensors of VNIR, TIR and WIR, therefore it's possible to obtain more information on the Earth than the existing satellite images cause it contains various a wavelength range in spite of relatively economic image. The coastline is changed according to topography shape because it's strongly localized. Besides, it's one of the most important factors in MGIS(Marine Geographic Information System). Therefore, this study is accomplished by analysing variation after abstraction the coastline automatically by Vector Line from ASTER satellite images. The study result will be used as an important basic data when analysis the change of e coastline hereafter.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.97-105
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• 2007

In this paper, visualization of convolution operation is presented, which is implemented by scalable vector graphics (SVG). Convolution operation is one of the basic essential concepts in the area of signal and image processing. However, it is difficult for students to intuitively understand the operation of convolution since it is mainly based on mathematical representation. We present the visualization of convolution operation and its applications which are implemented by SVG. The effects of the proposed approach have been analyzed by interviews. It has been seen that the proposed visualization of convolution operation could be effectively applied to learn the convolution operation and its applications.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1604-1613
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• 2018

The paper presents the equivalent circuit of an induction machine (IM) model which includes fundamental stray load and iron losses. The corresponding equivalent resistances are introduced and modeled as variable with respect to the stator frequency and flux. Their computation does not require any tests apart from those imposed by international standards, nor does it involve IM constructional details. In addition, by the convenient positioning of these resistances within the proposed equivalent circuit, the order of the conventional IM model is preserved, thus restraining the inevitable increase of the computational complexity. In this way, a compromise is achieved between the complexity of the analyzed phenomena on the one hand and the model's practicability on the other. The proposed model has been experimentally verified using four IMs of different efficiency class and rotor cage material, all rated 1.5 kW. Besides enabling a quantitative insight into the impact of the stray load and iron losses on the operation of mains-supplied and vector-controlled IMs, the proposed model offers an opportunity to develop advanced vector control algorithms since vector control is based on the fundamental harmonic component of IM variables.

• Journal of Power Electronics
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• v.3 no.4
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• pp.239-248
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• 2003

This paper presents a novel prototype of three-phase voltage source type zero voltage soft-switching inverter with the auxiliary resonant snubbers suitable for high-power applications with IGBT power module packages in order to reduce their switching power losses as well as electromagnetic conductive and radiative noises. A proposed single inductor-assisted resonant AC link snubber circuit topology as one of some auxiliary resonant commutation snubbers developed previously to achieve the zero voltage soft-switching (ZVS) for the three-phase voltage source type sinewave PWM inverter operating under the instantaneous space voltage vector modulation is originally demonstrated as compared with the other types of resonant AC link snubber circuit topologies. In addition to this, its operation principle and unique features are described in this paper. Furthermore, the practical basic operating performances of the new conceptual instantaneous space voltage vector modulation resonant AC link snubber-assisted three-phase voltage source type soft-switching PWM inverter using IGBT power module packages are evaluated and discussed on the basis of switching voltage and current waveforms, output line to line voltage quality, power loss analysis, actual power conversion efficiency and electromagnetic conductive and radiative noises from an experimental point of view, comparing with those of conventional three-phase voltage source hard-switching PWM inverter using IGBT power modules.

• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.135-154
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• 2002

This paper presents the convected material frame approach to study the nonlinear behavior of inelastic frame structures. The convected material frame approach is a modification of the co-rotational approximation by incorporating an adaptive convected material frame in the basic definition of the displacement vector and strain tensor. In the formulation, each discrete element is associated with a local coordinate system that rotates and translates with the element. For each load increment, the corresponding strain-displacement and nodal force-stress relationships are defined in the updated local coordinates, and based on the updated element geometry. The rigid body motion and deformation displacements are decoupled for each increment. This modified approach incorporates the geometrical nonlinearities through the continuous updating of the material frame geometry. A generalized nonlinear function is used to derive the inelastic constitutive relation and the kinematic hardening is considered. The equation of motion is integrated by an explicit procedure and it involves only vector assemblage and vector storage in the analysis by assuming a lumped mass matrix of diagonal form. Several numerical examples are demonstrated in close agreement with the solutions obtained by the ANSYS code. Numerical studies show that the proposed approach is capable of investigating large deflection of inelastic planar structures and providing an excellent numerical performance.