• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권9호
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• pp.315-320
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• 2015

클라우드 컴퓨팅에서 서버 가상화는 한 대의 물리적인 서버를 다수의 가상머신으로 분할하여 다양한 운영체제 및 애플리케이션을 구동하는 기술이다. 가상머신의 마이그레이션은 현재 실행 중인 가상머신을 소스 호스트에서 다른 물리적인 장치인 타깃 호스트로 이동하는 것이다. 가상머신의 라이브 마이그레이션은 작업 수행 성능의 최적화와 저전력 지원 및 에너지 절감, 결함포용, 노드들 간의 부하 균형을 제공하기 위한 필수적인 요소이다. 본 논문에서는 오픈소스 기반의 적응적 VM 라이브 마이그레이션 기법을 제안한다. 이를 위해 적응적 VM 마이그레이션 시점을 결정하는 VM 모니터링 모듈을 제안하고 오픈소스 기반 전가상화를 지원하는 하이퍼바이저를 설계하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2468-2487
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• 2015

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

• Smart Structures and Systems
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• 제13권1호
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• pp.81-98
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• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5271-5289
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• 2016

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

• 인터넷정보학회논문지
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• 제12권3호
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• pp.37-47
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• 2011

클라우드 시스템에 대한 현재의 연구들은 대규모 시스템 구현에 있어서 클라우드 구성요소들 간의 적절한 상호작용에 집중되어 있다. 그러나 이러한 시스템들은 속성을 기반으로 한 유사한 서비스 제공자들을 그룹화 하거나 효율적인 자원공유를 향상시키기 위한 지능적인 부하분산과 같은 지능적 기법을 제공하지 않는다. 본 논문은 클라우드 제공자를 그룹화하여 효율적인 서비스 가상화를 제공하여 서비스 프로비저닝을 향상시킨다. 클러스터 분석에 기반한 클라우드 서비스 제공자의 그룹화는 유사하거나 관련된 서비스를 하나의 그룹으로 만든다. 동적인 부하 균형화는 클라우드 시스템의 서비스 프로비저닝을 지원하며 동적인 기법을 사용하여 그룹내에서 부하분산을 담당한다. 제안한 가상화 기법(GRALB)은 다른 기법에 비해 메시지 오버헤드나 성능 면에서 좋은 결과를 보였다.

• 한국전자거래학회지
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• 제18권4호
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• pp.25-43
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• 2013

최근 글로벌 시장의 변화를 대표할만한 주요 키워드는 초연결(Hyper-Connection)과 지역적 세계화(Glocalization)으로 요약될 수 있다. 이러한 변화에 대응하고 경쟁 우위를 확보하기 위해서는 서비스 관리를 위한 표준화된 조직, 정책, 프로세스를 구축하는 것이 필요하다. 또한 서비스 관리 시스템은 연구개발 프로젝트 관리를 효과적으로 지원할 수 있어야만 한다. 본 연구에서는 클라우드 컴퓨팅 기술을 이용한 연구개발 프로젝트 관리 프로세스 설계를 위한 적응형 프레임워크와 시스템 구조를 제안한다. 이를 통해 전통적인 서비스 관리 기법이 가지는 한계점을 극복하고 시장의 요구에 빠르게 대응할 수 있을 것이다. 이와 함께 본 연구에서 제안하는 시스템을 통해서 국제 시장에서 경쟁우위를 확보함과 동시에 고객이 원하는 것 이상의 가치를 제공할 수 있을 것이다.

• 인터넷정보학회논문지
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• 제24권4호
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2008년도 추계학술발표논문집
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• IEIE Transactions on Smart Processing and Computing
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• 제5권1호
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• pp.49-54
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• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• Journal of Computing Science and Engineering
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• 제4권2호
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• pp.153-172
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• 2010

Wireless mobile multimedia communications have been greatly increased in the number of users, diversity of applications and interface technologies. Wireless mobile networks are being evolved and integrated into IP based core network, so it is necessary to provide sufficient QoS (Quality of Service) mechanism to provide enhanced user's satisfaction. In this paper, we propose a new adaptive QoS mechanism based on utility function borrowed from the field of microeconomics, call setup and handover signaling mechanism integrating QoS and mobility management. Through a simulation, we show that adaptive resource allocation based on user preferences can be realized in the wireless mobile network with some considerations.