• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.2
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• pp.65-74
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• 2015

In order to design an advanced composite materials drainage pipe structures for an undersea tunnel, mechanical properties for the lamina types of the structural member must be predetermined. It is also reported that the size effect of the specimen is significant. In this study the tensile tests for the lamina types of the structural member are conducted at the room temperature ($20^{\circ}C$) and the seawater temperature ($0^{\circ}C$). In addition, the mechanical properties are predicted by theory based on the rule of mixtures and elasticity solution technique. The predicted mechanical properties are compared with test results obtained by a test method. In the design of an advanced composite materials drainage pipe structural members for an undersea tunnel, the used mechanical properties must be applied at the room temperature with considering the modified factors. These are to be offered the datum for the design an advanced composite materials drainage pipe structures for an undersea tunnel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.485-499
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• 1999

Auctions are appealing market-type mechanisms because they can be deployed to solve the twin problems of resources pricing and allocation. Nonetheless the effectiveness of an auction mechanism in radio spectrum property rights should not be taken for granted. Policymakers need to be aware of the complexity of introducing market discipline in an area where none existed before. Auction design is critical to the success of the allocation process. However, a poorly designed auction mechanism can have detrimental effects on the spectrum rights allocation process. This study discusses some of the key elements and issues of auction design of radio spectrum rights for its efficient allocation. Particularly this study discusses, based on the existing auction theory and other countries' experiences, such issues as bidding rule, value interdependency and sequence of auction, information structure and asymmetric bidder, and wealth constraints and imperfect capital market.

• Journal of Practical Engineering Education
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• v.8 no.1
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• pp.1-7
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• 2016

In this paper, the proceedings and core procedures that involved the role of advisor to the theme, the set up rule of students, and complete the work steps involved in configuring graduate work that the overall design curriculum and graduation standards are summarized. The one or two students are designated as one team, and by the designated professor was conducted a discussion and technical guidance. Among various topics from 2011 to 2015, some specific results configured using a small drone and smartphones are presented. The survey are compared the activity results for the participating students in one major each in 2011 and 2015 and analyzed. Based on this review for improvement next year notes, it forms a continuous improvement, and has converged talents with passion and challenging spirit, sharing a plan to form a virtuous cycle that is associated with positive employment and entrepreneurship.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.757-766
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• 2009

This study presents an adaptive hypermedia learning system which can provide individual learning environment using a learner behavioral model. This system proposes a LBML which can manage learners' learning behavioral information by tracking down such information real-time. The system consists of a collecting system of learning behavioral information and an adaptive learning support system. The collecting system of learning behavioral information uses Web 2.0 technologies and collects learners' learning behavioral information real-time based on a SCORM CMI data model. The collected information is stored as LBML instances of individual learners based on a LBML schema. With the adaptive learning support system, a rule-based learning supporting module and an interactive learning supporting module are developed by analysing LBML instances.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1590-1597
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• 1999

Advertisements on the Web, rising as a major profit source of Web services, have a distinctive characteristic of detailed classification of potential customers, compared with those of other conventional media such as TV and newspaper. It is therefore possible to advertise selectively according to personal characteristics and to record precise advertisement effects. Web-based advertisement management systems of nowadays have the capability to select ones compatible with personal environment characteristics and registered information, and to provide processed information and knowledge about advertisement effects based on usage recordings. However, they have severe problems in modeling diverse requirements or characteristics of users : customers, advertisers and ISP, and in matching and coordinating of them. In order to solve these problems, we propose a frame work for coordinating the needs of users, advertisers, and ISPs, which is built on top of tree-style classification of advertisements. Other schemes are supported around the framework as follows : (1) characteristics management of pages within themselves, (2) rule-based modeling of advertisement target, and (3) user modeling and case-based analysis. We propose a prototype system within the framework.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.49-58
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• 2011

Augmented Reality(AR) assists human's cognitive ability through the information visualization by substantiating information about virtual situation. This technology is studied in a variety of ways including education, design, industry, and so on, by various supply of information devices equipped with cameras and display monitors. Since the most of the AR system depends on limited interaction that responds to the order from user, it can not reflect diverse real world situation. In this study, we suggest a knowledge based augmented reality system, which is composed of context awareness agent that provides recognized context information, along with knowledge based component that provides intelligent capability by utilizing domain knowledges. With this capability, the augmented object can generate dynamic model intelligently by reflecting context information, and can make the interaction possible among the multiple objects. We developed rule based context awareness system along with 3D model generation, and tested interaction among the augmented objects. And we suggest a framework that can provide a convenient way of developing augmented reality system for user.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.