• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.278-283
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• 2010

In this paper, we designed and proposed framework of extended user model to support student tailored learning in ubiquitous environment. For the purpose, existents model that is domain model, user model, adaptation model and interaction model connected to LMS(Learning Management System) and LCMS(Learning Contents Management System). Students information management process that is extended user model is in between LMS and adaptive learning system. And the process connected u-LMS to use u-learning. u-LMS and u-LCMS could support the learning contents through exchange the contents according to connect and request from the students.

• Journal of Communications and Networks
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• v.8 no.3
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• pp.290-296
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• 2006

In this paper, a generalized blind adaptive algorithm is introduced for multi-user detection of direct sequence code division multiple access (OS-COMA) wireless communication systems. The main property of the proposed algorithm is its ability to resolve the multipath fading channel resulting in inter symbol interference (ISI) as well as multiple access interference (MAI). Other remarkable properties are its low complexity and mitigation to the near-far problem as well as its insensitivity to asynchronous transmission. The proposed system is based on the minimization of the output energy and convergence to the minimum mean square error (MMSE) detector. It is blind in the sense that it needs no knowledge of the other users' signatures, only the intended user signature and timing are required. Furthermore, the convergence of the minimum output energy (MOE) detector to the MMSE detector is analytically proven in case of M-ary PSK. Depicted results show that the performance of the generalized system dominates those previously considered. Further improvements are obtained when multiple input multiple output (MIMO) technique is employed.

• Journal of Communications and Networks
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• v.11 no.3
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• pp.279-286
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• 2009

This paper proposes an adaptive mode switching algorithm between two strategies in multiple antenna cellular networks:A single-user mode and a multi-user mode for the broadcast channel. If full channel state information is available at the base station, it is known that a multi user transmission strategy would outperform all single-user transmission strategies. In the absence of full side information, it is unclear what the capacity achieving method is, and thus there are few criteria to decide which of the myriad possible methods performs best given a system configuration. We compare a single user transmission and a multi user transmission with linear receivers in this paper where the transmitter and the receivers have multiple antennas, and find that neither strategy dom inates the other. There is instead a transition point between the two strategies. Then, the mode switching point is determined both ana lytically and numerically for a multiple antenna cellular downlink with correlation between transmit antennas.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.17-24
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• 2007

Critical practicality problems are cause to search the presentation and contents according to user request and purpose in previous internet system. Recently, there are a lot of researches about dynamic adaptable ontology based system. We designed ontology based educational system which uses discrete probability and user profile. This system provided advanced usability of contents by ontology and dynamic adaptive model based on discrete probability distribution function and user profile in ontology educational systems. This models represents application domain to weighted direction graph of dynamic adaptive objects and modeling user actions using dynamically approach method structured on discrete probability function. Proposed probability analysis can use that presenting potential attribute to user actions that are tracing search actions of user in ontology structure. This approach methods can allocate dynamically appropriate profiles to user.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.245-252
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• 2009

We present a design for personalisation of a chording keyboard. There are two primary design goals. Firstly, the keyboard layout should be easy to learn, and easy to use, taking into account the background and physical constraints of the user. Secondly, the keyboard layout should be readily extensible, based on the previous behaviour of the keyboard user. The design proposal accomplishes these goals, and can be simply implemented on cost-effective hardware. In addition, we present preliminary experimental results on optimising the initial keyboard layout.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.225-229
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• 2008

We present an intelligent forging simulator AFDEX. The intelligent forging simulator is determined by the adaptive and optimal mesh generation technique and many intelligent application-oriented special functions which minimize the user-intervention during forging simulation. Of course, the solution accuracy should be optimized in the intelligent simulation. We have developed AFDEX to meet the requirement on intelligent simulation. Its characteristics are introduced with the help of typical application examples.

• Journal of Communications and Networks
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• v.11 no.5
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• pp.464-472
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• 2009

In this paper, we propose a packet scheduling algorithm for cellular relay networks by considering relay selection, variation of channel quality, and packet delay. In the networks, mobile users are equipped with not only cellular but also user relaying radio interfaces, where base station exploits adaptive high speed downlink channel. Our proposed algorithm selects a user with good cellular channel condition as a relay station for other users with bad cellular channel condition but can get access to relay link with good quality. This can achieve flexible packet scheduling by adjusting transmission rates of cellular link. Packets are scheduled for transmission depending on scheduling indexes which are calculated based on user's achieved transmission rate, packet utility, and proportional fairness of their throughput. The performance results obtained by using computer simulation show that the proposed scheduling algorithm is able to achieve high network capacity, low packet loss, and good fairness in terms of received throughput of mobile users.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1367-1374
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• 2022

In this paper, we propose an automatic cosmetic foundation recommendation system that suggests a good foundation product based on the user's skin color. The proposed system receives and preprocesses user images and detects skin color with OpenCV and machine learning algorithms. The system then compares the performance of the training model using XGBoost, Gradient Boost, Random Forest, and Adaptive Boost (AdaBoost), based on 550 datasets collected as essential bestsellers in the United States. Based on the comparison results, this paper implements a recommendation system using the highest performing machine learning model. As a result of the experiment, our system can effectively recommend a suitable skin color foundation. Thus, our system model is 98% accurate. Furthermore, our system can reduce the selection trials of foundations against the user's skin color. It can also save time in selecting foundations.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.99-113
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences (i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions