• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.361-374
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• 2008

In the latest intelligent computing environments, the self-adaptive software, as new software paradigm, which modifies its own behavior in response to changes in its operating environment is needed. To develop the self-adaptive software, it is necessary to analyze and design the context of software as well as the structure and the behavior of software. We need more efforts for self-adaptive software development than for traditional software development because we need more activities and technologies like context modeling and adaptation to develop the self-adaptive software. In this paper, we present the product line architecture for self-adaptive software and templates of artifacts to improve the efficiency of development through a reuse methodology. The artifacts of the architecture support the systematic reuse activities of core assets by expressing the commonality and variability of product line.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.801-808
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• 2013

In the virtual space with the interactive 3D contents, the degree of mental satisfaction is determined by how fully it reflect the real world. There are a few factors for getting the high completeness of virtual space. The first is the modeling technique with high-polygons and high-resolution textures which can heighten an visual effect. The second is the functionality. It is about how realistic represents dynamic actions between the virtual space and the user or the system. Although the studies on the techniques for animating and controlling the virtual characters have been continued, there are problems such that the long production time, the high cost, and the animation without expected behaviors. This paper suggest a method of behavior control of animation by designing the optimized skeleton which produces the movement of character and applying the procedural technique using physical law and mathematical analysis. The proposed method is free from the constraint on one-to-one correspondence rules, and reduce the production time by controlling the simple parameters, and to increase the degree of visual satisfaction.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.223-233
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• 2013

At local emerging stock markets such as Korea, Hong Kong, Singapore and Taiwan, foreign investors (FI) are recognized as important investment community due to the globalization and deregulation of financial markets. Therefore, it is required to monitor the behavior of FI against a sudden enormous selling stocks for the concerned local governments or private and institutional investors. The main aim of this study is to propose an early warning system (EWS) which purposes issuing a warning signal against the possible massive selling stocks of FI at the market. For this, we suggest machine learning algorithm which predicts the behavior of FI by forecasting future conditions. This study is empirically done for the Korean stock market.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.127-132
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• 2011

Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems. Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles. First of all, accurate analysis of load using the behavior of road itself is needed for solving illegal axle manipulation problems of overloaded vehicles and for installing intelligent embedded load analyzing system. Accordingly in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm and 10% error were checked.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.21-26
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• 2008

It was already realized at a current technological level of home network systems that the systems recognizes a user's simple order and carry out the order in the ubiquitous computing environment. However home is not a simple environment consisting into a large number of family members, so various order and situation would be needed accordingly. From now on we need to reach the technological level to infer that how is the user's behavior patterns and what kinds of service is the fittest to user who belong to the ubiquitous computing environment by using the result of the context interpreter. In this regards, active inferred-model needs to be suggested upgrading user's command into one step more higher level than the simple one adapting diversified feature. This study would like to suggest this active model recognizing context, which is user's environmental information applying basic network and inferring Context-based Service that user wants through the recognized result This study proposes a new method that can infer the user's desire in ubiquitous computing environment. First of all, we define a context as user's information of ubiquitous computing environment situation that user belongs to and we classify the context into 4W1H(Where, Who, When, What) formats. We construct Bayesian network and put the factor of context use as Bayesian network nodes. As a result, we can infer the user's behavior pattern and most proper service for user in the intelligent space from the probabilistic result of Bayesian network.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.25-32
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• 2015

With the increased frequency of crime such as assaults and sexual violence, the reliance on CCTV in arresting criminals has increased as well. However, CCTV, which should be monitored by human labor force at all times, has limits in terms of budget and man-power. Thereby, the interest in intelligent security system is growing nowadays. Expanding the techniques of an objects behavior recognition in previous studies, we propose a system to detect forms of violence between 2~3 objects from images obtained in CCTV. It perceives by detecting the object with the difference operation and the morphology of the background image. The determinant criteria to define violent behaviors are suggested. Moreover, provable decision metric values through measurements of the number of violent condition are derived. As a result of the experiments with the threshold values, showed more than 80% recognition success rate. A future research for abnormal behaviors recognition system in a crowded circumstance remains to be developed.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.1-14
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• 2006

This study proposed a customer preference estimation model for production recommendation and a method to enhance the performance of product recommendation using the estimated customer preference information. That is, we suggested customer preference estimation model to estimate exactly customer's product preference with his behavior. This model shows the relationship of customer's behaviors with his preferences. The proposed estimation model is optimized by learning the relative weights of customer's behavior variables to have an effect on his preference and enables to estimate exactly his preference. To validate our proposed models, we collected virtual book store data and then made a comparative analysis of our proposed models and a benchmark model in terms of performance results of collaborative filtering for product recommendation. The benchmark model means a prior preference weighting model. The results of our empirical analysis showed that our proposed model performed better results than the benchmark model.

• Polymer(Korea)
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• v.32 no.5
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• pp.421-426
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• 2008

pH-responsive P(MAA-co-EGMA) hydrogel microparticles were synthesized via dispersion photo polymerization and the feasibility of the particles as the cosmetic formulation was investigated. Rh-B and the functional materials for the cosmetic application such as ascorbic acid, adenosine, EGCG, and arbutin were loaded in the P (MAA-co-EGMA) hydrogel microparticles in order to examine the interaction between the hydrogel and the loaded materials. In the loading experiments, Rh-B showed the highest loading efficiency to the P(MAA-co-EGMA) hydrogels due to the electrostatic attraction between the negative charge of the hydrogels and the positive charge of Rh-B at the ionized states. However, the functional materials showed relatively low loading efficiencies because of the electrostatic repulsions between the negative charges of both the hydrogels and the materials at the ionized states. In addition, P(MAA-co-EGMA) hydrogel microparticles showed pH-responsive release behavior of Rh-B according to the external pH changes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.105-115
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• 2019

Recently, intelligent Android malicious codes have become difficult to detect malicious behavior by static analysis alone. Malicious code with SO file, dynamic loading, and string obfuscation are difficult to extract information about original code even with various tools for static analysis. There are many dynamic analysis methods to solve this problem, but dynamic analysis requires rooting or emulator environment. However, in the case of dynamic analysis, malicious code performs the rooting and the emulator detection to bypass the analysis environment. To solve this problem, this paper investigates a variety of root detection schemes and builds an environment for bypassing the rooting detection in real devices. In addition, SDK code hooking module for Android malicious code analysis is designed using Xposed, and intent tracking for code flow, dynamic loading file information, and various API information extraction are implemented. This work will contribute to the analysis of obfuscated information and behavior of Android Malware.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.341-359
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• 2023

The metaverse applies various technological means such as digital twin modeling, 3D rendering, and holographic imaging, which can provide an immersive tourism service experience. However, since the development of the metaverse is still in its infancy, there is relatively little research on digital tourism from the perspective of the metaverse. This research empirically studies the factors that promote the participation behavior of users on the metaverse platform for digital cultural tourism. Our results show that users' internal motivations for learning and entertainment and the functions provided by metaverse, which are sensory stimulation and social interaction lead to the intention to participate in cultural tourism on metaverse with the mediating effects of immersion experience and perceived pleasure.