• Journal of Computing Science and Engineering
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• v.7 no.3
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• pp.147-158
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• 2013

This invited paper introduces results on Web science and technology obtained during work with the Korea Advanced Institute of Science and Technology. In the first part, we discuss algorithms for exploring the deep Web, which refers to the collection of Web pages that cannot be reached by conventional Web crawlers. In the second part, we discuss sorting algorithms on the MapReduce system, which has become a dominant paradigm for massive parallel computing.

• International journal of advanced smart convergence
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• v.10 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Steel and Composite Structures
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• v.47 no.3
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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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• v.16 no.2
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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.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.181-193
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• 2009

According to the development of web sites' technologies, public institutions use web sites to carry out their business and also to utilize as pathway between government and the people also. Public web records means the result of business process over web sites in public institutions. Although there is much valuable information, it is vanished away easily because there is not yet proper methods and tools for preservation. The purpose of this paper is to design the metadata elements required when archiving deep web records, which is a kind of web records. For that, we first analyze oversea's related researches to define what public deep web records is. Then we define metadata elements about that and also explain the relationship on archival information package in Korea and dublin core metadata to support interoperability for them. The defined metadata can be used for the basis technologies in archiving domestic public web records.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.3
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• pp.149-158
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• 2004

In this study, deep beam specimens are designed to have the effective shear span to depth ratio 1.0 and web opening within effective shear region. The purpose of this study is to investigate experimentally the shear strengthening effect between before failure and after failure upon using fiber sheets for RC deep beam with opening in web. The results can be summarized as follows; 1)When deep beams with web opening were failed in shear, their initial diagonal crack load and crack width were not influenced by their types of the arranged steel bars. 2)Deep beam with the horizontal reinforced bar was effective in the ultimate load of deep beam with web opening in shear failure 3)There were the approximate values between the experimental values and the analysis of finite element method. 4)The ultimate failure strengths of the repaired and strengthened specimens were increased about 34.4%~83.8% in comparison with specimens not to be strengthened.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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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.