• Architectural research
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• 제7권2호
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• pp.55-60
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• 2005

Based on an experimental study, this study provides an equation to describe the shear strength of high-strength concrete deep beams with rectangular openings and without web reinforcements. Twenty-four concrete deep beams were tested with the variables of concrete strength, size of web opening, and shear span-to-depth ratio. The proposed equation is expressed as the sum of the shear strength provided by longitudinal bars and concrete. It is illustrated that the proposed equation predicts the load-carrying capacity of the deep beams more properly than the experimental equations proposed by other researchers.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.110-116
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• 2021

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a real-time earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

• 한국컴퓨터정보학회논문지
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• 제24권11호
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• pp.51-59
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• 2019

웹에서 정보 접근에 대한 폭발적인 주문으로 웹 사용자의 다음 접근 페이지를 예측하는 필요성이 대두되었다. 웹 접근 예측을 위해 마코브(markov) 모델, 딥 신경망, 벡터 머신, 퍼지 추론 모델 등 많은 모델이 제안되었다. 신경망 모델에 기반한 딥러닝 기법에서 대규모 웹 사용 데이터에 대한 학습 시간이 엄청 길어진다. 이 문제를 해결하기 위하여 딥 신경망 모델에서는 학습을 여러 컴퓨터에 동시에, 즉 병렬로 학습시킨다. 본 논문에서는 먼저 스파크 클러스터에서 다층 Perceptron 모델을 학습 시킬 때 중요한 데이터 분할, shuffling, 압축, locality와 관련된 기본 파라미터들이 얼마만큼 영향을 미치는지 살펴보았다. 그 다음 웹 접근 예측을 위해 다층 Perceptron 모델을 학습 시킬 때 성능을 높이기 위하여 이들 스파크 파라미터들을 튜닝 하였다. 실험을 통하여 논문에서 제안한 스파크 파라미터 튜닝을 통한 웹 접근 예측 모델이 파라미터 튜닝을 하지 않았을 경우와 비교하여 웹 접근 예측에 대한 정확성과 성능 향상의 효과를 보였다.

• Steel and Composite Structures
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• 제47권3호
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• pp.383-393
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• 2023

Conventional reinforced concrete design codes assume ideal strain evolution in semi-deep beams with externally bonded fiber-reinforced polymer (EB-FRP) web strips. However, there is a strain interaction between internal stirrups and web strips, leading to a notable difference between code-based and experimental shear strengths. Current study provides an experiment-verified detailed numerical framework to assess the potential strain interaction under quasi-static monotonic load. Based on the observations, steel stirrups are effective only for low EB-FRP amounts and the over-strengthening of semi-deep beams prevents the stirrups from yielding, reducing its shear strength contribution. A notable difference is detected between the code-based and the study-based EB-FRP strain values, which is a function of the normalized FRP stress parameter. Semi-analytical relations are proposed to estimate the effective strain and stress of the components considering the potential strain interaction. For the sake of simplification, a linearized correction factor is proposed for the EB-FRP web strip strain, assuming its restraining effect as constant for all steel stirrup amounts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• 한국전자거래학회지
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• 제14권4호
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• pp.181-193
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• 2009

웹 기술이 발전함에 따라, 공공기관에서는 웹을 이용하여 업무를 처리하고 또한 국가와 시민간의 연결통로로 사용하고 있다. 웹기록물은 공공기관에서 이용하는 웹 사이트상에서의 업무처리의 결과로, 정보로서 중요한 가치를 담고 있으나 보존의 방법과 도구가 부족하여 많은 양의 자원들이 소실되고 있는 실정이다. 본 논문은 웹기록물의 한 분류인 심층 웹기록물 아카이빙에 필요한 메타데이터 설계를 목적으로 하고 있다. 이를 위해 우선 국외 연구기관 및 연방정부에서 제공하는 심층 웹기록물에 대해 알아보고, 이를 바탕으로 국내 공공기관의 심층 웹기록물을 정의하였다. 정의된 심층 웹기록물을 바탕으로 아카이빙에 필요한 메타데이터 항목을 설계하고, 국내외 호환성을 위해 전자기록물 장기보존포맷과 더블린코어 메타데이터와의 관계를 설명하였다. 이는 국내 웹기록물 아카이빙의 기반기술로 활용될 수 있다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2000년도 봄 학술발표회 논문집
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• pp.587-592
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• 2000

As the residential spaces become high-rised and high-density, Multi-story buildings were constructed with transfer girders, Deep beams, wall foundations, floor diaphragms an shear walls which may have column offsets. Especially, In the analysis and design of Multi-story buildings, the lateral loads must be taken into account. But, there have been no appropriate theory and national design code for predicting ultimate shear strength of reinforced concrete Deep beams with web opening. Only empirical and semi-empirical formulas for predicting their ultimate load bearing capacities due to the complexities of the structural non-linearity and material heterogeneity. So this study analyze tow-dimensional finite element model that represents exactly the behavior of real structures with SBETA which are general nonlinear finite element analysis program, and compare the results with that from the real reinforced Concrete Deep beams with web opening tests. From the comparison, and parametric study, The Study presents the elementary data of the earthquake resistance for the reinforced concrete Deep beams with web opening.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.253-260
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• 2022

Existing web-based cost databases have proved invaluable for construction cost estimating. These databases have been utilized to compute approximate cost estimates using assembly rates, unit rates, and etc. These web-based databases can be used independently with traditional cost estimation methods (manual methods) or used to support BIM-based cost estimating platforms. However, these databases are rigid, costly, and require a lot of manual inputs to reflect recent trends in prices or prices relative to a construction project's location. To address this gap, this study integrated deep learning techniques with web-based price analysis to develop a database that incorporates a project's location cost estimating standards and current cost trends in generating a cost estimate. The proposed method was tested in a case study project in Lagos, Nigeria. A cost estimate was successfully generated. Comparison of the experimental results with results using current industry standards showed that the proposed method achieved a 98.16% accuracy. The results showed that the proposed method was successful in generating approximate cost estimates irrespective of project's location.

• 한국구조물진단유지관리공학회 논문집
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• 제8권3호
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• pp.149-158
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• 2004

본 연구는 유효전단스팬에 대한 깊이의 비가 1.0인 깊이가 큰 보에 대해서 유효전단영역내에 개구부에 대한 파괴 메카니즘의 변화와 전단파괴된 후 보수 보강에 따른 부재 내력의 복원력에 관한 연구로서, 그 결론는, 전단파괴한 유공보 시험체의 초기사균열 하중은 시험체 간에 큰 차이가 없어 시험체의 배근형태에 영향을 받지 않는 것으로 나타났으며, 아라미드 시트로 보강된 시험체의 균열 및 파괴형태는 중앙부의 휨 균열과 전단지간의 사균열이 동시에 발생한 후, 최대내력근처에서 유공측의 전단균열이 확대되어, 시트면의 중앙부위가 박리되면서 전단파괴 되었다. 전단파괴된 깊이가 큰 보 시험체를 아라미드 섬유시트로 보강한 결과 보강전과 비교하여 최대내력은 최소 34.4%, 최대 83.8%의 증가를 나타내어 파괴전의 내력을 복원하는 것으로 나타났다.

• 품질경영학회지
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• 제48권4호
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• pp.581-596
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• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.