• Journal of the Korean housing association
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• v.14 no.4
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• pp.77-85
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• 2003

The facade design of apartment is getting more important, since residents'needs on raising the quality of housing have increased, and also public interests on urban landscape are higher than before. As a way of improving facade design the necessity of remodeling consequently has been risen. The government also relieves the regulations related to remodeling. A apartment facade design should address how to express both identities and aesthetic tastes of residents, so they want to reflect their own ideas and opinions on the apartment facade design during the remodeling process. It is not always easy, unfortunately, to reflect residents'ideas and opinions due to the public characteristics of apartment facades and absence of tools that support effective design participation. This paper proposes an internet-based collaborative design process that supports residents'active participation in the exterior remodeling process of apartment. We outline three main issues involved in developing the design system: 1) a semantic facade database built upon classification of building facades; 2) an Internet-based facade modeler based on a real-time, rule-based procedural modeling method; and finally 3) the virtual reality(VR) simulation that allows a group of designers and residents to design in a collaborative fashion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.28-34
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• 2021

Internet Service Providers (ISPs) constantly endeavor to resolve network congestion, in order to provide fast and cheap services to the customers. This study suggests two models based on Markov chain, using three and four access attempts to complete the call. It involves a comparative study of four models to check the relationship between Internet Access sharing traffic, and the possibility of network jamming. The first model is a Markov chain, based on call-by-call attempt, whereas the second is based on two attempts. Models III&IV suggested by the authors are based on the assumption of three and four attempts. The assessment reveals that sometimes by increasing the number of attempts for the same operator, the chances for the customers to complete the call, is also increased due to blocking probabilities. Three and four attempts express the actual relationship between traffic sharing and blocking probability based on Markov using MATLAB tools with initial probability values. The study reflects shouting results compared to I&II models using one and two attempts. The success ratio of the first model is 84.5%, and that of the second is 90.6% to complete the call, whereas models using three and four attempts have 94.95% and 95.12% respectively to complete the call.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1818-1832
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• 2022

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1630-1641
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• 2010

LCC can be defined as "the sum of present values of investment costs, capital costs, installation costs, energy costs, operating costs, maintenance costs, and disposal costs over the life-time of the project, product, or measure." LCCA involves estimating the costs and timing associated with each alternative over a selected analysis period and conversion of those costs to economically comparable values considering the time-value of money. The several Excel-Based LCC tools found on the internet are described in this paper. Also, This paper performed an analysis of the existing LCC commercial tools, assessing various aspects of each program. The goal was to evaluate the best features of each tool and to identify the requirements for LCC evaluation of Urban Transit Vehicle. The LCC tools are developed to address problems in many different areas and a tool developed and structured for one area cannot generally be used in another area. No general LCC tool exists and if one is needed for Urban Transit Vehicle it has to be developed by the project. Since a full LCC can be very complex it is likely that this Excel-Based LCC tool should be a small and simple tool for quick cost estimates. This paper presents a LCC tool consisting of eight excel sheets, which are "Project", "CBS", "PBS", "PM", "CM", "Others", "LCC Result" and "Diagram".

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Journal of the Korea Society for Simulation
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• v.8 no.1
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• pp.101-111
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• 1999

The Web-based simulation was introduced for conducting simulation experiments in the Internet and the Web. Due to the use of the Java language, the Web-based simulation can have such characteristics as reusability, portability, and the capability of execution on the Web. Most of existing Web-based simulation tools have focused mainly on the development of the runtime simulation libraries and mechanisms on the Web. Thus, the model development work in such Web-based simulation tools still requires hand-written coding of model developers. This paper presents a visual model development environment for the Web-based simulation. The proposed environment provides a framework for model development and animation. To show the effectiveness of the proposed environment, we perform simulation experiments for transaction routing algorithms in a distributed transaction processing system.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.675-678
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• 2006

IPTV (Internet Protocol Television) is an efficient system which serves various contents to subscribing consumers by using the IP over a broadband connection. The IPTV services can be multi-channel broadcasting service, VoD, T-Commerce, video conference, on-line game and so on. TV Anytime metadata provides various description tools for TV program contents by which TV program contents can serve more information. In this paper, we mention about the overview and feature of IPTV and TV Anytime metadata, propose the essential functions in the EPG program of IPTV and survey the description tools for the proposed functions in EPG. Finally, we show the authored metadata and the implementation of advanced EPG application.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.115-136
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• 2007

In this study, we investigate the possibility of deploying a recently emerging Internet-based technology, called Web log or Blog, to address the problems of knowledge transfer and sharing, particularly in the case of tacit knowledge. We examined the use practice of four blogs and then identified several properties relevant to knowledge transfer and sharing. They include the specific style of blog format, content ownership attribution, posted article organization, communication tools and method, news feed function, and various links from/to outside websites. These features were argued to facilitate knowledge transfer and sharing. In particular, we discussed a great deal about the structure of comments and links as tools for collaboration and idea sharing, which enables the knowledge conversion processes (socialization, externalization, combination, and internalization), We then provide several guidelines to develop blogs as a knowledge management tool.

• BMB Reports
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• v.41 no.11
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• pp.757-764
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• 2008

The genomic sequences of a huge number of species have been determined. Typically, these genome sequences and the associated annotation data are accessed through Internet-based genome browsers that offer a user-friendly interface. Intelligent use of the data should expedite biological knowledge discovery. Such activity is collectively called data mining and involves queries that can be simple, complex, and even combinational. Various tools have been developed to make genome data mining available to computational and experimental biologists alike. In this mini-review, some tools that have proven successful will be introduced along with examples taken from published reports.