• Korean Chemical Engineering Research
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• v.48 no.6
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• pp.784-790
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• 2010

VOCs such as acetone, benzene, toluene, ethylbenzene were adsorbed in a fixed-bed adsorber using zeolite synthesized from coal fly ash and 4 kinds of activated carbon at 101.3 kPa. The adsorber was operated batchwise with the charge of 5 g adsorbent to obtain the breakthrough curve of VOCs. Experiments were carried out at $40^{\circ}C$, nitrogen flow rate of $70cm^3/min$ and sparger temperature of $30^{\circ}C$. The deactivation model was tested for these curves by combining the adsorption of VOCs and the deactivation of adsorbent particles. The observed values of the adsorption rate constant and the deactivation rate constant were evaluated through analysis of the experimental breakthrough data using a nonlinear least square technique. The experimental breakthrough data were fitted very well to the deactivation model than the adsorption isotherm models in the literature. Also, adsorption capacities of adsorbents were obtained from the breakthrough curve to observe the correlation between adsorption capacity and the physical properties of VOCs.

• Journal of Korean Society of Steel Construction
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• v.28 no.6
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• pp.383-394
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• 2016

This study is intended to predict the seismic behavior of the down-scaled 3-story steel frame structures subjected to the real ground motion, and evaluate their structural damage through advanced finite element (FE) analysis results. The FE frame models are designed by considering the effect of the soft story. In addition, the effect of structural asymmetry is also taken into consideration during the nonlinear dynamic analyses. After observing the analysis results, it is reconfirmed that the damage of the steel frame building under the ground motion should be governed by the soft story column rather than the structural mass asymmetry.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.483-490
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• 2022

Malaria is a disease caused by a parasite and it is prevalent in all over the world. The usual method used to recognize malaria cells is a thick and thin blood smears examination methods, but this method requires a lot of manual calculation, so the efficiency and accuracy are very low as well as the lack of pathologists in impoverished country has led to high malaria mortality rates. In this paper, a malaria cell image recognition model using transfer learning is proposed, which consists in the feature extractor, the residual structure and the fully connected layers. When the pre-training parameters of the VGG-19 model are imported to the proposed model, the parameters of some convolutional layers model are frozen and the fine-tuning method is used to fit the data for the model. Also we implement another malaria cell recognition model without residual structure to compare with the proposed model. The simulation results shows that the model using the residual structure gets better performance than the other model without residual structure and the proposed model has the best accuracy of 97.33% compared to other recent papers.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.303-306
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• 2000

AIN CS-3를 기반으로 하는 IMT-2000 망은 지능망의 구성요소들과 통합 및 연동을 통하여 기존에 개발되었거나 앞으로 개발될 유선 지능망의 서비스들을 쉽게 수용할 수 있게 된다. 본 논문에서는 IMT-2000 및 IMT-2000과 연동되는 타 네트워크의 상이한 플랫폼 환경의 개발환경 및 향후 유지보수과정에서 발생할 것으로 예측되는 TMN 시스템 내 에이전트의 Q3 인터페이스 구현상의 표준을 이를 수 없다는 층의 문제점을 해결하기 위하여 I-Farmer 모델을 제안하였다.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.258-260
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• 2009

본 논문에서는 최적화 알고리즘인 유전자 알고리즘과 context-based FCM 클러스터링 방법을 이용하여 새로운 형태의 RBF 뉴럴 네트워크의 포괄적인 설계 방법론을 소개한다. 제안된 구조는 클러스터링 기법을 기반하여 사용된 데이터의 특성에 효과적인 모델을 구축하고자 한다. 또한 유전자 알고리즘을 이용하여 모델의 최적화에 주요한 영향을 미치는 파리미터들(-은닉층에서의 contex의 수, contex에 포괄되는 노드의 수, 그리고 contex에 입력되는 입력변수)을 동조한다. 제안된 모델의 설계 공정은 1) K-means 클러스터링을 통한 context fuzzy set에 대한 정의와 설계, 2) context-based fuzzy clustering에 대한 모델의 적용과 이에 따른 모델 구축의 효율성, 3) 유전자 알고리즘을 통한 모델 최적화를 위한 파라미터들의 최적화와 같은 단계로 구성되어 있다. 구축된 RBF 뉴럴 네트워크의 후반부 다항식에 대한 parameter들은 성능지수를 최소화하기 위해 Least Square Method에 의해서 보정된다. 본 논문에서는 모델을 설계함에 있어서 체계적인 설계 알고리즘을 포괄적으로 설명하고 있으며, 더 나아가 제안된 모델의 성능을 다른 표준적인 모델들과 대조함으로써 제안된 모델의 우수성을 나타내고자 한다.

• Journal of Internet Computing and Services
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• v.4 no.5
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• pp.69-76
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• 2003

In this paper we show a new learning algorithm for pattern classification. This algorithm considered a scheme to find a solution to a problem of incremental learning algorithm when the structure becomes too complex by noise patterns included in learning data set. Our approach for this problem uses a pruning method which terminates the learning process with a predefined criterion. In this process, an iterative model with 3 layer feedforward structure is derived from the incremental model by an appropriate manipulations. Notice that this network structure is not full-connected between upper and lower layers. To verify the effectiveness of pruning method, this network is retrained by EBP. From this results, we can find out that the proposed algorithm is effective, as an aspect of a system performence and the node number included in network structure.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.4
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• pp.19-33
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• 2008

Dynamic response measurements from natural excitation were carried out for 25- and 42-story buildings to evaluate their inherent properties, such as natural frequencies, mode shapes and damping ratios. Both are reinforced concrete buildings adopting a core wall, or with shear walls as the major lateral force resisting system, but frames are added in the plan or elevation. In particular, shear walls in a 25-story building are converted to frames from the 4th floor level downwards while maintaining a core wall throughout, resulting in a fairly complex structure. Due to this, along with similar stiffness characteristics in the principal directions, significantly coupled and closely spaced modes of motion are expected in this building, making identification rather difficult. By using various state-of-the-art system identification methods, the modal parameters are extracted, and the results are then compared. Three frequency-domain and four time-domain based operational modal identification methods are considered. Overall, all natural frequencies and damping ratios estimated from the different identification methods showed a greater consistency for both buildings, while mode shapes exhibited some degree of discrepancy, varying from method to method. On the other hand, in comparison with analysis results obtained using the initial finite element(FE) models, test results exhibited a significant difference of about doubled frequencies, at least for the three lower modes in both buildings. To improve the correlation between test and analysis, a few manual schemes of FE model updating based on plausible reasons have been applied, and acceptable results are obtained. The advantages and disadvantages of each identification method used are addressed, and some difficulties that might arise from the updating of FE models, including automatic procedures, for such large structures are carefully discussed.

• Journal of the Korean Society of Propulsion Engineers
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• v.19 no.1
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• pp.9-17
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• 2015

The basic flow configuration is composed of a plane, double shear layer where relatively thin mid gas layer is sandwiched between air and fuel stream. The present study describes numerical investigations concerning the combustion enhancement according to a variation of mid layer thickness. In this case, the effect of heat release in turbulent mixing layers is important. For the numerical solution, a fully conservative unsteady $2^{nd}$ order time accurate sub-iteration method and $2^{nd}$ order TVD scheme are used with the finite volume method including k-${\omega}$ SST model. The results consists of three categories; single shear layer consists of fuel and air, inert gas sandwiched between fuel and air, cold fuel gas sandwiched between fuel and air. The numerical calculations has been carried out in case of 1, 2, 4 mm of mid layer thickness. The height of total gas stream is 4 cm. The combustion region is broadened in case of inert gas layer of 2, 4 mm thickness and cold fuel layer of 4 mm thickness compared with single shear layer.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2001.11a
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• pp.115-118
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• 2001

마이크로 스트립을 이용하여 셀룰러(824-894 MHz) 이통통신용 송수신 안테나를 설계 및 제작하였다. 이때 넓은 대역폭을 얻기 위하여 개구면 결합형(aperture-coupled) 안테나를 설계하였으며 사용된 기판은 2층의 FR-4 ($\varepsilon$$_{r}$ =4.7) 기판과 공기층을 합한 3단으로 이루어져 있다. 안테나 특성파악을 위한 스트립의 전류분포와 슬롯의 자류분포 해석은 전송선로 모델법을 적용하였고 제작된 안테나는 VSWR 1.5:1 이하의 높은 복사 특성을 얻으므로써 좁은 공간에서 편리하게 설치하여 쓸 수 있는 우수한 안테나임을 확인하였다.