• 한국보건간호학회:학술대회논문집
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• 2006.06a
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• pp.30-43
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• 2006

• Bulletin of the Korean Mathematical Society
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• v.52 no.4
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• pp.1339-1351
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• 2015

In this paper, using pseudo-spherical Frenet frame of pseudo-spherical curves in hyperbolic space, we define the notion of the structure functions on the non-developable ruled surfaces with timelike ruling. Then we obtain the properties of the structure functions and a complete classification of the non-developable ruled surfaces with timelike ruling in Minkowski 3-space by the theories of the structure functions.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.13.2-13
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• 2001

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.661-670
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• 2018

This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.167-174
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• 2002

Earth retaining structure is constructed structure in order to construct a multistoried building, the subway, a subterranean downtown for effective use and obtainments of the limited ground. Recently, many kinds of research have been actively developed for a standardization and a database on designing and constructing of bridge, tunnel, road. With the works of database construction of that, many kinds of data with respect to statistics is cumulated. However, Database work of designed and constructed earth retaining structure in the construction field is wholly lacking and lagged behind in the works of database construction. This paper suggested classification system on indication data in connection with designing and constructing earth retaining structures a hundred fields. On the basis of that, code work with classification system was practised and DB program of indication data in connection with designing and constructing earth retaining structures was developed.

• Journal of the Korean Society of Costume
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• v.53 no.5
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• pp.155-169
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• 2003

The purpose of this study was to find out criteria for classifying fashion brand from the viewpoint of fashion business practice in order to develop strategy of fashion brands and to manage brand effectively and systematically, and to suggest theoretical frame for application of these criteria. Survey was implemented for this research. 388 Data from the people who works for merchandising, sales or design in fashion business company was analyzed. Questionnaires were developed based on 37 fashion brand classification criteria. SPSS package and LISREL program were used to analyze data. Factor analysis, one-way ANOVA, $$\mu$tiple response analysis, correlation analysis, and structure equation model analysis were applied. The results of this study were as follows First, factor analysis considering 37 classification criteria identified 7 factors as classification criteria which can be used effectively by fashion business company. Second, in two cases, based on the job description and the responsible items, analysis showed that importance of the 7 classification criteria factors was different. And all of 7 criteria were correlated to each other. Third, the effective method to classify fashion brands was proposed by establishing the model of the relationship among the values of 7 criteria and by proving it by the structure equation model analysis. And the two types of the courses to classify fashion brand were shown. Forth, according to the evaluation of these criteria in the importance of appropriateness and difficulty of implementing, classification criteria factor of "the level of product concept" was found to be very effective and "the level of brand value" was ineffective to apply.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.822-832
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• 2001

We have implemented an inductive learning system that learns PROSPECTOR-rule-style classification rules from sets of examples. In our a approach, a genetic algorithm is used in which a population consists of rule-sets and rule-sets generate offspring through the exchange of rules relying on genetic operators such as crossover, mutation, and inversion operators. In this paper, we describe our learning environment centering on the syntactic structure and meaning of classification rules, the structure of a population, and the implementation of genetic operators. We also present a method to evaluate the performance of rules and a heuristic approach to generate rules, which are developed to implement mutation operators more efficiently. Moreover, a method to construct a classification system using multiple learned rule-sets to enhance the performance of a classification system is also explained. The performance of our learning system is compared with other learning algorithms, such as neural networks and decision tree algorithms, using various data sets.

• Journal of Information Management
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• v.41 no.1
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• pp.173-200
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• 2010

National construction project for SOC(Social Overhead Capital) is led by the government and invested high cost and all capabilities for long times. In general case the life times of SOC facilities are permanently. According to the long life time of the facilities documents and records of national projects are also retained permanently. Consistent classification systems are required to operation and maintenance of the facilities efficiently and to support the organic co-works for several Stakeholders. Therefore the classification of national construction projects are based on WBS of the project. WBS is the hierarchy structure that established and developed in project management project management methodology to produce deliverables of the projects. As a result, this study provides a prospect of project records classification systems in the 21st Century.

• The Journal of Society for e-Business Studies
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• v.12 no.2
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• pp.111-132
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• 2007

Developing and managing Web applications are more complex than ever because of their growing functionalities, advancing Web technologies, increasing demands for integration with legacy applications, and changing content and structure. All these factors call for a more inclusive and comprehensive Web application design method. In response, we propose a context-based Web application design methodology that is based on several classification schemes including a Webpage classification, which is useful for identifying the information delivery mechanism and its relevant Web technology; a link classification, which reflects the semantics of various associations between pages; and a software component classification, which is helpful for pinpointing the roles of various components in the course of design. The proposed methodology also incorporates a unique Web application model comprised of a set of information clusters called compendia, each of which consists of a theme, its contextual pages, links, and components. This view is useful for modular design as well as for management of ever-changing content and structure of a Web application. The proposed methodology brings together all the three classification schemes and the Web application model to arrive at a set of both semantically cohesive and syntactically loose-coupled design artifacts.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.