• Knowledge Management Research
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• v.15 no.2
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• pp.23-41
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• 2014

Firms increasingly utilize Social Networking Service(SNS) to lead user's voluntary behavior. In the web-based environment, users show coexist loyal behavior which is represented by 'web-based pro-organization citizenship behavior' and 'anti-citizenship behavior'. To measure genuine performance of web-activity, we separated degree of compliance based on credibility, 'deep-level' and 'surface-level' to comprehend different behavior after compliance. The analysis result shows that contents credibility is important to enhance deep-level of compliance which has significant influence on web-based pro-organization citizenship behavior. Contrastively, surface-level of compliance has influence on anti-citizenship behavior. Based on the results of these analyses, the directions of web-based activities for the common good and self-interests of the stakeholders of the web-based activities will be proposed.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.147-155
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• 2016

According to rapid development of technology, web text is growing explosively and attracting many fields as substitution for survey. The user of Facebook is reaching up to 113 million people per month, Twitter is used in various institution or company as a behavioral analysis tool. However, many research has focused on meaning of the text itself. And there is a lack of study for text's creation subject. Therefore, this research consists of sex/age text classification with by using 20,187 Facebook users' posts that reveal the sex and age of the writer. This research utilized Convolution Neural Networks, a type of deep learning algorithms which came into the spotlight as a recent image classifier in web text analyzing. The following result assured with 92% of accuracy for possibility as a text classifier. Also, this research was minimizing the Korean morpheme analysis and it was conducted using a Korean web text to Authorship Attribution. Based on these feature, this study can develop users' multiple capacity such as web text management information resource for worker, non-grammatical analyzing system for researchers. Thus, this study proposes a new method for web text analysis.

• Journal of the Korea Concrete Institute
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• v.15 no.5
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• pp.697-704
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• 2003

The purpose of this experimental study is to investigate the relationship of the shear behavior and the variety of width, depth and location of an opening in reinforced concrete deep beams with rectangular web openings, and to present an improved shear strength equation of those members. The main parameters considered were concrete strength(fck), shear span-to-overall depth ratio(a/h), and the size and vortical position of the web openings. Twenty five deep beams were tested under two symmetric loading-points. Test results showed that the shear behavior of deep beams with web openings was influenced by a/h and the size of opening. In addition, the KCI shear design provision is a tendency to be more unconservative according to the increase in a/h and the area-ratio of opening to shear span(Ao/Ash). Based on the concrete strut action of top and bottom member of an opening and the tie action of longitudinal reinforcement, a proper design equation which closely predicts the capacity of deep beams with rectangular openings is developed.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.885-890
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• 2000

12 RC deep beams with a/d = 1.17 are reported. This paper is to study the effect of vertical and horizontal web reinforcement and web reinforcement arrangement on inclined cracking shear, ultimate shear strength, midspan deflection, and inclined crack width. Test results indicated that web reinforcement produces and arrangement seems to moderately affect inclined cracking shear, ultimate shear strength and crack width. However, addition of horizontal web reinforcement(pv = 0.0085) little or no influence on inclined cracking shear, ultimate shear strength and crack width. The member which vertical and horizontal web reinforcement concentrate on the center web considerably increases in load-carrying capacity.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.21-30
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• 2024

This research aims to design a system capable of generating real web pages based on deep learning and big data, in three stages. First, a classification system was established based on the industry type and functionality of e-commerce websites. Second, the types of components of web pages were systematically categorized. Third, the entire web page auto-generation system, applicable for deep learning, was designed. By re-engineering the deep learning model, which was trained with actual industrial data, to analyze and automatically generate existing websites, a directly usable solution for the field was proposed. This research is expected to contribute technically and policy-wise to the field of generative AI-based complete website creation and industrial sectors.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.4
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• pp.1-26
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• 2017

This study aims to provide the efficient sharing and utilization method of disasters scattered by each institution and develop automated collection algorithm using web crawler for disaster information in deep web accounts. To achieve these goals, this study analyzes the logical structure of the deep web and develops algorithms to collect the information. With the proposed automatic algorithm, it is expected that disaster management will be helped by sharing and utilizing disaster safety information.

• Journal of the Korea Concrete Institute
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• v.25 no.6
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• pp.683-691
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• 2013

Failure behaviour of deep beams is largely depend on shear span to effective depth ratio(a/d). For deep beams with $a/d{\leq}1.0$, the diagonal splitting of the web and local crushing of concrete near applied loads or supports is the most common failure mode. If the beam contains sufficient web reinforcement, area of local crushing will be increased while area of diagonal splitting is decreased. When web reinforcement above purpose, web should be reinforced both in the horizontal and vertical directions or be reinforced in perpendicular direction to diagonal cracks. For deep beams with 1.0${\leq}2.5$, the increment of shear strength by the horizontal web reinforcement are hardly expected unless amount of main bars very small that the main bars should be reached yielding state when shear failure occurs. In contrast, the vertical web reinforcement prevent the beam from being transferred to tied arch mechanism, and as a result th shear strength of beam increase. In this study, formulas based on upper bound theorem of plastic theory are proposed to predict the shear strength of reinforced concrete deep beams with $a/d{\leq}1.0$. Comparisons with other experiment results are performed, and good agreement between measured and predicted value by proposed formulas is obtained.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10a
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• pp.519-524
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• 1998

The shear behavior and reinforcement effects of simply supported reinforced concrete deep beams with web opening subject to concentrated loads have been scrutinized experimentally to verify the effects of structural parameters such as size, location and reinforcements of web opening. A total of 14 specimens were tested at the laboratory under two-point top loading. The shear span ratio was taken constantly 0.8, and various types of reinforcements based on truss models were adopted. In the tests, the effects of loction, reinforcements of web openings on the shear behavior, and crack initiation and propagation have been carefully checked and analyzed.

• Journal of International Academy of Physical Therapy Research
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• v.12 no.1
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• pp.2261-2266
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• 2021

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of 'Download All Images' and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

• Journal of the Korea Concrete Institute
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• v.13 no.6
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• pp.619-628
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• 2001

In building construction, openings of the story-height deep beams are usually required for accessibility and service lines such as air conditioning ducts, drain pipes and electric units. It is known that the main parameters affecting the load bearing capacity of deep beams with web openings are size, shape, location and reinforcements of openings. However, there have been no pertinent theories and national design codes for predicting ultimate shear strength of reinforced concrete deep beams with web openings. In this study, the shear behavior of simply supported reinforced concrete deep beams with web openings subject to concentrated loads has been scrutinized experimentally. A total of 34 specimens, the geometry of openings, its reinforcements and shear span to depth ratio, being taken as the experimental variables, has been cast and tested in the laboratory. The effects of these structural parameters on the shear strength and crack initiation and propagation have been carefully checked and analyzed. From the tests, it has been observed that the failures of all specimens were due to shear mechanism and the ultimate strength of specimens varies according to the location of openings, by which the formation of compression struts between the loading points and supports are deterred. All of the test results of specimens have been compared with the formulas proposed by previous researchers. The results were closely coincident with the formulas given by Ray and Kong's equation except for some X series specimens having a larger dimension of openings beyond the geometric limits of proposed equations.