• Steel and Composite Structures
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• v.42 no.1
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• pp.107-121
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• 2022

Past studies have shown that the aspect ratio (width-to-height) of a steel plate shear wall (SPSW) can significantly affect its seismic response. SPSWs with lower aspect ratio (narrow SPSW) may experience low lateral stiffness and flexure dominated drift response. As the height of the frame increases, the narrow SPSWs prove to be uneconomical and demonstrate inferior seismic response than their wider counterparts. Moreover, the thicker web plates required for narrow SPSWs exerts high inward pull on the VBEs. The present study suggests the limiting values of the aspect ratio for an SPSW system by evaluating the seismic collapse performance of 3-, 6- and 9-story SPSW systems using FEMA P695 methodology. For this purpose, nonlinear models are developed. These models are validated with the past quasi-static experimental results. Non-linear static analyses and Incremental dynamic analyses are then carried. The results are then utilized to conservatively suggest the limiting values of aspect ratios for SPSW system. In addition to the conventional-SPSW (Conv-SPSW), the collapse performance of staggered-SPSW (S-SPSW) is also explored. Its performance is compared with the Conv-SPSW and the use of S-SPSW is suggested in the cases where SPSW with lower than recommended aspect ratio is desired.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.64-64
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• 2022

이상기후로 인해 돌발적이고 국지적인 호우 발생의 빈도가 증가하게 되면서 짧은 선행시간(~3 시간) 범위에서 수치예보보다 높은 정확도를 갖는 초단시간 강우예측자료가 돌발홍수 및 도시홍수의 조기경보를 위해 유용하게 사용되고 있다. 일반적으로 초단시간 강우예측 정보는 레이더를 활용하여 외삽 및 이동벡터 기반의 예측기법으로 산정한다. 최근에는 장기간 레이더 관측자료의 확보와 충분한 컴퓨터 연산자원으로 인해 레이더 자료를 활용한 인공지능 심층학습 기반(RNN(Recurrent Neural Network), CNN(Convolutional Neural Network), Conv-LSTM 등)의 강우예측이 국외에서 확대되고 있고, 국내에서도 ConvLSTM 등을 활용한 연구들이 진행되었다. CNN 심층신경망 기반의 초단기 예측 모델의 경우 대체적으로 외삽기반의 예측성능보다 우수한 경향이 있었으나, 예측시간이 길어질수록 공간 평활화되는 경향이 크게 나타나므로 고강도의 뚜렷한 강수 특징을 예측하기 힘들어 예측정확도를 향상시키는데 중요한 소규모 기상현상을 왜곡하게 된다. 본 연구에서는 이러한 한계를 보완하기 위해 적대적 생성 신경망(Generative Adversarial Network, GAN)을 적용한 초단시간 예측기법을 활용하고자 한다. GAN은 생성모형과 판별모형이라는 두 신경망이 서로간의 적대적인 경쟁을 통해 학습하는 신경망으로, 데이터의 확률분포를 학습하고 학습된 분포에서 샘플을 쉽게 생성할 수 있는 기법이다. 본 연구에서는 2017년부터 2021년까지의 환경부 대형 강우레이더 합성장을 수집하고, 강우발생 사례를 대상으로 학습을 수행하여 신경망을 최적화하고자 한다. 학습된 신경망으로 강우예측을 수행하여, 국내 기상청과 환경부에서 생산한 레이더 초단시간 예측강우와 정량적인 정확도를 비교평가 하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.19-22
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• 2022

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.101-102
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• 2016

본 논문에서는 차량의 특징점들 사이의 간격과 크기의 비례식으로 자동차의 차종을 식별하는 방법을 제시한다. 자동차 관련 영상은 그 편의성을 위하여 기본 RGB모델에서 Gray색상 모델로 변환시켜 사용한다. 자동차의 배경 제거는 Canny Edge Direction을 통하여 수행하고 외곽선 검을을 통하여 원하는 특징 점을 얻는다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.47-48
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• 2017

본 논문에서는 Microsoft Azure 플랫폼의 Azure PowerShell, Azure CLI(Command Line Interface), REST API를 활용하여 클라우드 기반 서비스 포털과 관리 포털을 개발함으로서 중소건설사에서 건설현장의 공사원가 관리 및 일일 관리를 위한 모듈과 서비스 제공을 위해 필요한 서비스 포털 및 관리 포털과 제품 관리 모듈 등 클라우드 서비스 구축 수행하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.203-204
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• 2017

SDLC 중에서 분석단계는 정보시스템의 개발을 준비하는 단계로서 정보기술 체계의 분석과 사용자의 요구 사항 도출 및 예측되는 위험 요소에 대한 평가도 함께 수행하였다. 정보보호의 요구사항은 도출은 기밀성, 무결성, 가용성, 그리고 책임 추적성 등의 관점에서 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.109-110
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• 2016

본 논문에서는 효율적으로 주차 공간을 확보와 주차장 성능을 향상을 위한 방법을 조사하였다. 이러한 방법으로는 차량 번호판 조사를 이용하여 주차 회전율을 구하는 방법이 있었다. 본 연구로 효율적으로 주차장을 사용하고 있는지를 판단할 수 있다. 또한 차량의 주차를 하여 잘 소통되는지를 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.498-500
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• 2019

Point cloud is a flexible set of points that can provide a scalable geometric representation which can be applied in different computer graphic task. We propose a method based on EdgeConv and densely connected layers to aggregate the features for better classification. Our proposed approach shows significant performance improvement compared to the state-of-the-art deep neural network-based approaches.

• The Journal of Advanced Prosthodontics
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• v.9 no.3
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• pp.152-158
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• 2017

PURPOSE. The purpose of this study was to evaluate the influence of different coping thicknesses and veneer ceramic cooling rates on the failure load of zirconia-ceramic crowns. MATERIALS AND METHODS. Zirconia copings of two different thicknesses (0.5 mm or 1.5 mm; n=20 each) were fabricated from scanning 40 identical abutment models using a dental computer-aided design and computer-aided manufacturing system. Zirconia-ceramic crowns were completed by veneering feldspathic ceramics under different cooling rates (conventional or slow, n=20 each), resulting in 4 different groups (CONV05, SLOW05, CONV15, SLOW15; n=10 per group). Each crown was cemented on the abutment. 300,000 cycles of a 50-N load and thermocycling were applied on the crown, and then, a monotonic load was applied on each crown until failure. The mean failure loads were evaluated with two-way analysis of variance (P=.05). RESULTS. No cohesive or adhesive failure was observed after fatigue loading with thermocycling. Among the 4 groups, SLOW15 group (slow cooling and 1.5 mm chipping thickness) resulted in a significantly greater mean failure load than the other groups (P<.001). Coping fractures were only observed in SLOW15 group. CONCLUSION. The failure load of zirconia-ceramic crowns was significantly influenced by cooling rate as well as coping thickness. Under conventional cooling conditions, the mean failure load was not influenced by the coping thickness; however, under slow cooling conditions, the mean failure load was significantly influenced by the coping thickness.