• Journal of Elementary Mathematics Education in Korea
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• v.16 no.1
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• pp.145-166
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• 2012

The objective of this study is to analyze the cases related to the lingual and non-lingual metaphors used in the math class at elementary school and consider the values of metaphors as a teaching method for the subject of mathematics. Throughout this study, teachers' gestures are analyzed as lingual and non-lingual metaphors shown between teachers and students in the class for the topic of the inverse proportion in quartic equations for direct and inverse proportions in Chapter 7 for the first semester of the 6th grade at elementary school in terms of the amended curriculum for the year of 2007. According to the results of the analysis, it can be concluded that there are mechanical and hypothetical movement metaphors in the mathematical metaphors observed in this study. Also, in terms of gestures, iconic, metaphoric and deixis gestures are found. Such metaphors seem to be evenly distributed throughout the math class and expressed in various forms. Based on the results of the analysis, the educational meaning given by the utilization of metaphors is considered for the math class.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.7
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• pp.41-52
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• 2003

In this paper, three-dimensional thermo-mechanical properties of carbon-phenolic 8-hamess satin weave composites were predicted considering geometric parameters of microstructures. The effective properties were calculated by a series of numerical experiments based on unit cell analysis. The microstructural details were modeled through macro-elements, and the periodic boundary conditions were derived for corresponding un it cell types. The Monte Carlo method was employed to consider the random phase shift between the layers, and the results were investigated on the effect of the geometric parameters of shift, number of layers and waviness ratios. Experimental tests were also performed and the results were compared.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.356-365
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• 2002

Test systems verify and classify the various kinds of semiconductor products. So test systems need programs that can test the various special functions of hardware modules and products. Programs can be modified, compiled and executed by engineers. Consequently, the systems needs programming languages that can be easily programmed by engineers and their compilers that can compile and execute teat programs. In this paper we discuss the environment of programming languages and their compilers for the existing domestic teat systems. We design a programming language and implement its compiler that can be conveniently used by the experienced engineers in the industry field. Experimental results show that a newly designed test system with our programming language and compiler can teat products faster than the existing test system.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.103-111
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• 2016

Offloading tasks from mobile devices to available cloud servers were improved since the introduction of the cloudlet. With the implementation of dynamic offloading algorithms, mobile devices can choose the appropriate server for the set of tasks. However, current task distribution approaches do not consider the number of VM, which can be a critical factor in the decision making. This paper proposes a dynamic task distribution on clustered data centers. A proportional VM migration approach is also proposed, where it migrates virtual machines to the cloud servers proportionally according to their allocated CPU, in order to prevent overloading of resources in servers. Moreover, we included the resource capacity of each data center in terms of the maximum CPU in order to improve the migration approach in cloud servers. Simulation results show that the proposed mechanism for task distribution greatly improves the overall performance of the system.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.21-30
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• 2016

Recently manufacturing companies have used PLC control programs popularly for their automated production systems. Since the life cycle of production process is not so long, the change of the production lines occur frequently. Most of changes happen with modification of the position information and control process of the equipment. PLC control program is also modified based on the fundamental process. Therefore, to verify new PLC program by configuring virtual space according to real environment is needed. In this paper we show a logical modeling method, based on Timed-FSA useful for sequence control and dead-lock prevention. There is a problem wasting user's labor and time when defining a variety of states in a device. To overcome this problem, we propose an extended I-O model based on existing methods by adding a token concept of Petri Nets. Also we will show the usability of the extended I-O modeling through user study.

• Journal of Software Engineering Society
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• v.25 no.1
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• pp.1-9
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• 2012

RFID systems have been widely used in various fields such as logistics, distribution, food, security, traffic and others. A RFID middleware, one of the key components of the RFID system, perform an important role in many functions such as filtering, grouping, reporting tag data according to given user specifications and so on. However, manual test data generation is very hard because the inputs of the RFID middleware are generated according to the RFID middleware standards and complex encoding rules. To solve this problem, in this paper, we propose a black box test technique based on RFID middleware standards. Firstly, we define ten types of input conversion rules to generate new test data from existing test data based on the standard specifications. And then, using these input conversion rules, we generate various additional test data automatically. To validate the effectiveness of generated test data, we measure coverage of generated test data on actual RFID middleware. The results show that our test data achieve 78% statement coverage and 58% branch coverage in the classes of filtering and grouping, 79% statement coverage and 64% branch coverage in the classes of reporting.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.

• Convergence Security Journal
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• v.23 no.2
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• pp.67-74
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• 2023

An elevator is a mechanical and electronic device composed of about 20,000 various parts, and is systematically operated with a close relationship with each other. By intuitively understanding this complex elevator structure and efficiently recognizing the operating model, it will be possible to increase the understanding of the elevator system and the efficiency of maintenance. The existing elevator management system is a process of collecting and understanding information based on data generated from elevators, and has a structure that lacks efficiency in expressing and managing real-world information during elevator maintenance. Therefore, this paper proposes a simulator framework that can operate efficiently based on the actual elevator model. By constructing the recognition of specific objects through a 3D-based service model and visualizing the operation process, it will be possible to enhance the understanding of the structure and operation required for elevator operation. To this end, the core components of the elevator system are identified, the relationship between them and the operating method are visualized, and a simulator is implemented. Based on this, it will be possible to provide a realistic information management and operating environment in virtual space and real platform.