• International journal of advanced smart convergence
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• 제11권4호
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• pp.149-154
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• 2022

With the development of information and communication technology, information literacy and utilization are emerging as basic skills necessary for modern people. Accordingly, information education is becoming a basic literacy education for a nation. Unplugged computing is in the spotlight as a major educational method of information education. The main advantage of unplugged computing is that it is easy to convey basic theories or principles of computer science to students through play activities without the help of special information devices such as computers and tablet PCs. However, studies on student evaluation on unplugged computing have been very insufficient. In this study, students' evaluation standards are developed to maximize the educational effect of unplugged computing. The evaluation standards consist of four areas: participation, interest, satisfaction, and understanding of concepts. The results of this study can be used as a basic study for student evaluation of unplugged computing in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권6호
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• pp.630-640
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• 2001

본 논문에서는 공유 객체 모델 기반의 P2P(Peer-to-Peer) 애플리케이션을 개발하기 위한 프레임워크인 ObjectPeerWork를 설계 및 구현한다. 공유 객체 모델은 자원 관리 기능들을 자원에 포함시킴으로써 관리를 위한 컴퓨팅 파워의 저하를 막고, 보안 문제를 개선함으로써 공유 자원에 대한 신뢰도를 향상시킬 수 있다. 또한, 공유 객체 모델은 분산 컴포넌트 기반의 요청 중계 관리자 및 모듈 컨테이너를 통하여 확장이 가능한 모델이다. 이러한 공유 객체 모델에 기만한 ObjectPeerWork는 일반적인 P2P 모델의 문제점들을 개선하여 기업 내 정보시스템 구축과 컴퓨팅 파워의 분산 및 자원의 효율적인 관리를 가능하게 하는 프레임워크이다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 춘계학술발표대회
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• pp.1325-1328
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• 2006

본 논문에서는 RTOS 기반 임베디드 소프트웨어 개발에 적합한 모델기반 방법론을 제안하고 이와 함께 개발된 자동코드생성 도구를 기술한다. 현재까지 알려진 대표적인 모델기반 방법론으로는 OMG (Object Management Group)의 MDA (Model-Driven Architecture)가 있으며, MDA 에서는 EJB, 웹서비스,.NET, 그리고 CORBA 와 같은 미들웨어 플랫폼을 대상으로 하는 응용 소프트웨어의 개발을 지원한다. 하지만, 통상적인 임베디드 시스템은 실시간성에 대한 요구조건은 물론 성능과 자원활용에 있어 많은 제약을 가짐에 따라 상당수의 임베디드 시스템은 미들웨어를 사용하지 않고 RTOS 상에서 직접 수행되도록 개발되고 있다. 이에 따라 본 연구에서는 MDA 방법론을 확장하여 플랫폼 의존적인 모델 (PSM, Platform Specific Model) 단계에서 추상화된 RTOS 행위를 표현할 수 있도록 추상 RTOS API (Generic RTOS API)를 정의하고, 아울러 추상화된 RTOS 행위를 자동으로 변환하여 C 코드를 생성해주는 도구인 TransPI 를 함께 제시한다.

• Computers and Concrete
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• 제16권1호
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• pp.67-97
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• 2015

This article presents a computing procedure developed to predict the torsional strength of axially restrained reinforced concrete beams. This computing procedure is based on a modification of the Variable Angle Truss Model to account for the influence of the longitudinal compressive stress state due to the axial restraint conditions provided by the connections of the beams to other structural elements. Theoretical predictions from the proposed model are compared with some experimental results available in the literature and also with some numerical results from a three-dimensional nonlinear finite element analysis. It is shown that the proposed computing procedure gives reliable predictions for the ultimate behaviour, namely the torsional strength, of axially restrained reinforced concrete beams under torsion.

• Coupled systems mechanics
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• 제7권2호
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• pp.211-232
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• 2018

In this work we present an upscaling technique for multi-scale computations based on a stochastic model calibration technique. We consider a coarse-scale continuum material model described in the framework of generalized standard materials. The model parameters are considered uncertain, and are determined in a Bayesian framework for the given fine scale data in a form of stored energy and dissipation potential. The proposed stochastic upscaling approach is independent w.r.t. the choice of models on coarse and fine scales. Simple numerical examples are shown to demonstrate the ability of the proposed approach to calibrate coarse scale elastic and inelastic material parameters.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.52-56
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• 2004

Computing today is becoming ubiquitous. In such ubiquitous computing environments, entities expect to access resources and services at any time from anywhere. Therefore, the question rises of how to establish trust relationship in previously unknown devices or resources with such environment. After reviewing a model to delegate trust to communicating entities in ubiquitous environment and its security problems, we present a new model for secure delegation over communication entities. We adopt two-party signature scheme as cryptographic primitives. Additionally, we apply threshold cryptosystems to our model for more secure enhancement.

• International Journal of Internet, Broadcasting and Communication
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• 제7권1호
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• pp.70-74
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• 2015

The cloud computing are provided various ways for accessing resources and services through collaboration. In this paper, we present a cloud computing model for collaboration in cloud environment. By introducing a model, it is possible to introduce and develop an application required for the database and business services. SaaS model can be applied overall or partially. In particular, business operations need various software. Since cost reduction and applying immediate service are available, it is possible to realize the business environment and high quality service.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.644-672
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• 2019

Negative consequences incurred from employees' non-work-related computing (NWRC) have been one of the security-related issues in information intensive organizations. While most studies have focused on the factors that motivate employees to engage in NWRC, this study examines the mediating effect of moral beliefs on the relationship between sanctions and NWRC using a moral beliefs-based model. The research model posits that the formal (i.e., punishment severity and detection certainty) and informal sanctions (subjective norms and descriptive norms) enhance employees' moral beliefs against NWRC intention. From a cross-sectional scenario-based survey involving 176 employees working at banks in Mongolia, our results indicate that moral beliefs fully mediate the relationship between detection certainty/subjective norms and NWRC intention and act as a partial mediator in the relationship between descriptive norms and NWRC. The findings from this study present empirical evidence that both informal and formal sanctions could be an effective deterrent for NWRC intention through employees' moral beliefs.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.