• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.50-56
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• 2011

This paper proposes 3D visualization method of compound knowledge which will be able to identify and search easily compound knowledge objects based the multidimensional relationship. For this, we structurized a compound knowledge with link and node which become the semantic network. and we suggested 3D visualization method using SOM. Also, to arrange compound knowledge from 3D space and to provide the chance of realistic and intuitional information retrieval to the user, we proposed compound knowledge 3D clustering methods using object similarity. Compound knowledge 3D visualization and clustering using SOM will be the optimum method to appear context of compound knowledge and connectivity in space-time.

• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.113-114
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• 2015

In this study, 1D and 3D thermoacoustic analysis model were developed in order to predict fundamental characteristics of combustion instability in a gas turbine lean premixed combustor. The 1D network model can be used to analyze frequency and growth rate of combustor instability by simply dividing whole system into a couple of acoustic sub-elements, while the 3D Helmholtz solver model can predict directly acoustic modes as well as basic properties of combustion instability. Prediction results of both 1D and 3D models generally showed a good agreement with the measurements, even if there was a slight overestimation for instability range.

• Journal of Digital Convergence
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• v.10 no.3
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• pp.161-166
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• 2012

This paper study and analysis about road map and information assurance policy in center of information assurance issues, under the future combat environment, which is performing US military, and the background of modernization war of network centric.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.465-467
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• 1992

This paper shows 3D vision system based on halftone image analysis. Any halftone image has its own surface vector normal to surface patch. To classily the given 3D images, all the patch on 3D object are transformed to black/white halftone. First we extract the general learning patterns which represents required slopes and their attributes. And next we propose 3D segmentation by searching intensity, slope and density. Artificial neural network is found to be very suitable in this approach, because it has powerful learning quality and noise tolerant. In this study, 3D shape reconstruct using pyramidian model. Our results are evaluated to enhance the quality.

• 한국IT서비스학회:학술대회논문집
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• 2006.05a
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• pp.260-266
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• 2006

현재 모바일 게임시장은 2D 위주에서 3D로 변화를 모색하고 있다. 각 이동통신사들은 3D 게임과 Network 접속형 게임의 활성화를 위해 GXG, GPANG 등의 전용 서비스 사이트를 개설하고 다양하고 저렴한 월정액 요금제를 출시하는 등의 많은 노력을 보이고 있으며, 3D 지원 단말기 역시 보급속도가 점차 높아져가고 있는 추세이다. 본 논문에서는 이러한 변화된 모바일 게임 환경에 적용 가능한 육성게임을 설계 및 구현하였다. 본 연구의 육성게임은 웹과 게임서버와 클라이언트간의 연동시스템, NF3D를 이용한 3D 아바타의 생성, 그리고 육성되는 아바타의 지능성 부여 등의 특징을 가지고 있다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.5
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• pp.624-631
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• 2012

This paper presents a high-efficiency current mode class-D(CMCD) power amplifier for the 13.56 MHz band using a Guanella's 1:1 transmission-line transformer and filtering circuits at the output network. The second and third s are filtered out in the load network of the class-D amplifier. The implemented CMCD power amplifier exhibited a power gain of 13.4 dB and a high power-added efficiency(PAE) of 84.6 % at an output power of 44.4 dBm using the 13.56 MHz CW input signal. The second and third distortion levels were -50.3 dBc and -46.4 dBc at the same output power level, respectively.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.46-53
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• 2001

The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

• Journal of Korea Water Resources Association
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• v.51 no.4
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• pp.347-354
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• 2018

In urban areas, runoff flow is drained through sewer networks as well as surface areas. Therefore, it is very important to consider sewer networks as a component of hydrological drainage processes when conducting urban inundation modelling. However, most researchers who have implemented urban inundation/flood modelling, instinctively simplified the sewer networks without the appropriate criteria. In this research, a 1D-2D fully coupled urban inundation model is applied to estimate the influence of sewer network simplification on urban inundation modelling based on the dendritic network classification. The one-dimensional (1D) sewerage system analysis model, which was introduced by Lee et al. (2017), is used to simulate inlet and overflow phenomena by interacting with surface flow. Two-dimensional (2D) unstructured meshes are also applied to simulate surface flow and are combined with the 1D sewerage analysis model. Sewer network pipes are simplified based on the dendritic network classification method, namely the second and third order, and all cases of pipes are conducted as a control group. Each classified network case, including a control group, is evaluated through their application to the 27 July 2011 extreme rainfall event, which caused severe inundation damages in the Sadang area in Seoul, South Korea. All cases are compared together regarding inundation area, inflow discharge and overflow discharge. Finally, relevant criterion for the simplification method is recommended.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.