• The KIPS Transactions:PartC
• /
• v.9C no.6
• /
• pp.823-830
• /
• 2002

Enterprise security management system proposed to properly manage heterogeneous security products is the security management infrastructure designed to avoid needless duplications of management tasks and inter-operate those security products effectively. In this paper, we propose the model of generalized security policies. It is designed to help security management build invulnerable security policies that can unify various existing management infrastructures of security policies. Its goal is not only to improve security strength and increase the management efficiency and convenience but also to make it possible to include different security management infrastructures while building security policies. In the generalization process of security policies. we first diagnose the security status of monitored networks by analyzing security goals, requirements, and security-related information that security agents collect. Next, we decide the security mechanisms and objects for security policies, and then evaluate the properness of them on the basis of security goals, requirements and a policy list. With the generalization process, it is possible to integrate heterogeneous security policies and guarantee the integrity of them by avoiding conflicts or duplications among security policies. And further, it provides convenience to manage many security products existing in large networks.

• Journal of Korean Society for Geospatial Information Science
• /
• v.16 no.3
• /
• pp.35-41
• /
• 2008

Recently needs for the convergence of CAD and GIS data are increasing, and many studies on converting two systems to each other are being carried out. In this study, to revise and update the building data of digital map using CAD data for construction, the outline of building is abstracted from the CAD data and the outline is generalized to the same level of detail with the building data of digital map. Several line simplification algorithms to generalize the outline are adopted and compared, especially at the view of satisfaction to the drawing rule for digital map. Douglas-Peucker algorithm, Lang's algorithm, Reumann-Witkam algorithm, and Opheim algorithm are applied as the line simplification method. To evaluate the results of these algorithms, visual assessment and variation ratio of the number of points, total length of lines, the area of polygon, and satisfaction ratio to the drawing rule of digital map are analyzed. The result of Lang algorithm and Douglas-Peucker algorithm show superior satisfaction ratio. But general satisfaction ratio is 50~60% for all algorithm. Therefore there seems to be a limit to use these algorithms for the simplification method to update the building data in digital map and it is necessary to develop line simplification algorithm which satisfy the drawing rule well.

• Journal of the Korea Institute of Building Construction
• /
• v.23 no.2
• /
• pp.197-207
• /
• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.

• Structural Engineering and Mechanics
• /
• v.91 no.5
• /
• pp.443-457
• /
• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.365-373
• /
• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Korean Institute of Interior Design Journal
• /
• v.20 no.3
• /
• pp.59-68
• /
• 2011

As society alters and building ages, many industrial facilities lost their function and abandoned in central city areas, and sometimes creating many problems. However, many European countries successfully transformed those urban decay to vivid cultural hub. The purpose of this study was to analyze a concept and methods of converting idle industrial facilities to successful cultural spaces. A case study of Tate Modern, Baltic Center for Contemporary Art, and Ruhr Museum was conducted using literature review, site visit, and interview methods. Findings indicated that converting historically significant idle industrial facilities to cultural center had not only reused abandoned site but also helped regenerating adjacent urban areas. This article demonstrates the key factors of successful conversion strategies as convenience, participation, placeness, historicity, and accessibly; and thus an effort to actively enhance the strategic factors were demanded in future conversion projects. A continuous studies on exploring extensive cases in various perspectives are required for further generalization in future studies.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.345-352
• /
• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.469-481
• /
• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of Elementary Mathematics Education in Korea
• /
• v.1 no.1
• /
• pp.87-98
• /
• 1997

A practice is classified into the practice as a content and the practice as a method. The former means that the practical nature of mathematical knowledge itself should be a content of mathematics and the latter means that one should teach the mathematical knowledge in such a way as the practical nature is not damaged. The practical nature of mathematics means mathematician's activity as it is actually done. Activities of the mathematician are not only discovering strict proofs or building axiomatic system but informal thinking activities such as generalization, analogy, abstraction, induction etc. In this study, it is found that the most instructive ones for the future users of mathematics are such practice as content. For the practice as a method, students might learn, by becoming apprentice mathematicians, to do what master mathematicians do in their everyday practice. Classrooms are cultural milieux and microsoms of mathematical culture in which there are sets of beliefs and values that are perpetuated by the day-to-day practices and rituals of the cultures. Therefore, the students' sense of ‘what mathematics is really about’ is shaped by the culture of school mathematics. In turn, the sense of what mathematics is really all about determines how the students use the mathematics they have learned. In this sense, the practice on which classroom instruction might be modelled is that of mathematicians at work. To learn mathematics is to enter into an ongoing conversation conducted between practitioners who share common language. So students should experience mathematics in a way similar to the way mathematicians live it. It implies a view of mathematics classrooms as a places in which classroom activity is directed not simply toward the acquisition of the content of mathematics in the form of concepts and procedures but rather toward the individual and collaborative practice of mathematical thinking.

• Journal of the Korean Society of Earth Science Education
• /
• v.7 no.3
• /
• pp.293-302
• /
• 2014

The purpose of this study is to investigate the effects of research project program of science process skills and science-related attitudes for high school students. This study were accompanied by 72 junior students of G High School who were reorganized as students whose research subject was closely related to chemistry. These students went through 28 periods of 14 sessions of research project program, were tested before and after the study on their science process skills and science-related attitudes. A simple questionnaire afterwards to get their thoughts on this program, was surveyed. The results are as follows. First, the research project program was effective in the science process skills (p<0.01). There was a statistically meaningful difference in the subcategory of deduction, setting up hypotheses, finding variables, building experiments, graphing and interpreting data. Although there was an increase in the average scores of prediction, operant definition, and generalization factors, it was not statistically meaningful (p>0.05). Second, the research project program showed an increase in the post-test of the science-related attitudes but was not statistically meaningful (p>0.05). In terms of subcategoty, the social importance of science, criterion of scientists, application of scientific attitude, and enjoyment of science classes were statistically meaningful (p<0.05). Third, according to the survey of research project program, there was an increase in creating a research problem and solving it by oneself as well as in participating with other teammates to solve a problem. But the most difficult thing was when the experiment failed during the research was processing. The curiosity and interest, towards objects around all lives and science classes after the program done, were increased.