• Journal of Digital Contents Society
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• v.19 no.4
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• pp.719-729
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• 2018

Abstract In 2017, it has been reported that Google had more than 90% of the market share in search-engines of desktops and mobiles. Most people may consider that Google surely searches the entire web area. However, according to many researches for web data, Google only searches less than 10%, surprisingly. The most region is called the Deep Web, and it is indexable by special search engines, which are different from Google because they focus on a specific segment of interest. Those engines build their own deep-web databases and run particular algorithms to provide accurate and professional search results. There is no search engine that indexes the entire Web, currently. The best way is to use several search engines together for broad and efficient searches as best as possible. This paper defines that kind of search engine as Hybrid Search Engine and provides characteristics and differences compared to conventional search engines, along with a frame of hybrid search engine.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.665-670
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• 1999

The shear behavior and strength of simply supported reinforced concrete deep beams with web opening subject to concentrated loads have been investigated experimentally on the effects of structural parameters such as location of web opening and reinforcements. A total of 12 specimens were tested at the laboratory under two-point top loading. The shear span-to-depth ratio was taken constantly 0.8, and various types of reinforcements based on truss models were adopted. In the tests, the effects of location, reinforcements of web openings on the shear behavior, and crack initiation and propagation have been carefully checked and analyzed. The test results have been compared with the formulas proposed currently being used and analyzed by nonlinear finite element method. Shear strengths obtained from the tests showed good matches with Kong and Ray's equation, and also with the results calculated by nonlinear finite element method.

• Transactions of Materials Processing
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• v.12 no.6
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• pp.550-557
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• 2003

In this study, a web-based system was developed by utilizing finite element method and virtual system designed using Virtual Reality Modeling Language (VRML). The simulation program for axi-symetric sheet forming is developed using finite flement method. The developed system consists of two modules, client module and server module. The client module was developed by using Active-X control. The input data for FEM calculation is transferred to the server module by using communication protocol. Then sever module performs several successive processes: input data generation, forming simulation, conversion of results to VRML format. After that, the results from the simulation can be visualized on the web browser in client computer. Besides, client module offers the capability to control and navigate on virtual forming machine and calculated result. By using this system simulation result can be investigated more realistically in virtual environment including forming machine.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.151-156
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• 2021

Low-end IoT devices do not have enough computation and memory resources for DNN learning and inference. Integer quantization of real-type neural network models can reduce model size, hardware computational burden, and power consumption. This paper describes the design and implementation of a web-based quantization platform for CNN deep learning accelerator chips. In the web service platform, we implemented visualization of the model through a convenient UI, analysis of each step of inference, and detailed editing of the model. Additionally, a data augmentation function and a management function of files that store models and inference intermediate results are provided. The implemented functions were verified using three YOLO models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.43-50
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• 2021

Recently, researches and projects of companies based on artificial intelligence have been actively carried out. Various services and systems are being grafted with artificial intelligence technology. They become more intelligent. Accordingly, interest in deep learning, one of the techniques of artificial intelligence, and people who want to learn it have increased. In order to learn deep learning, deep learning theory with a lot of knowledge such as computer programming and mathematics is required. That is a high barrier to entry to beginners. Therefore, in this study, we designed and implemented a web-based deep learning platform called DeepBlock, which enables beginners to implement basic models of deep learning such as DNN and CNN without considering programming and mathematics. The proposed DeepBlock can be used for the education of students or beginners interested in deep learning.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.378-381
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• 2006

Test results of four reinforced concrete two-span continuous deep beams are summarized. Main variables were the configuration of web opening reinforcement. Shear span-to-overall depth ratio and the size of the web opening were fixed by 1.0 and 0.5 a $\times$ 0.2 h, respectively. To control diagonal crack and enhance strength, it can be recommended that diagonal reinforcement crossing the crack plane joining between loading points and corner of openings should be provided.

• Computers and Concrete
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• v.14 no.1
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• pp.19-40
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• 2014

Reinforced concrete deep beams are structural beams with low shear span-to-depth ratio, and hence in which the strain distribution is significantly nonlinear and the conventional beam theory is not applicable. A strut-and-tie model is considered one of the most rational and simplest methods available for shear strength prediction and design of deep beams. The strut-and-tie model approach describes the shear failure of a deep beam using diagonal strut and truss mechanism: The diagonal strut mechanism represents compression stress fields that develop in the concrete web between diagonal cracks of the concrete while the truss mechanism accounts for the contributions of the horizontal and vertical web reinforcements. Based on a database of 406 experimental observations, this paper proposes a new strut-and-tie-model for accurate prediction of shear strength of reinforced concrete deep beams, and further improves the model by correcting the bias and quantifying the scatter using a Bayesian parameter estimation method. Seven existing deterministic models from design codes and the literature are compared with the proposed method. Finally, a limit-state design formula and the corresponding reduction factor are developed for the proposed strut-andtie model.

• Journal of Korean Society of Steel Construction
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• v.16 no.5 s.72
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• pp.575-586
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• 2004

This paper presents an experimental study on the flexural capacity of an encased(slim-floor) composite beam, which is a wider plate under bottom flange of H-beam with web openings. Five simple full-scale bending tests were conducted on the encased(slim-floor) composite beams at varying steel beam heights (250mm and 300mm), positions of web openings, and loading conditions. The test results revealed that the web-open encased composite beam had sufficient composite action, without any additional shear connection devices, because of the inherent shear-bond effects between the steel beam and the concrete, and a stable structural performance without web-opening reinforcements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.