• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.139-144
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• 2016

There exist many threats in cyber space, however current anti-virus software and other existing solutions do not effectively respond to malware that has become more complex and sophisticated. It was shown experimentally that it is possible for the proposed approach to provide an automatic execution environment for the detection of malicious behavior of active malware, comparing the virtual-machine environment with the real-machine environment based on user interaction. Moreover, the results show that it is possible to provide a dynamic analysis environment in order to analyze the intelligent malware effectively, through the comparison of malicious behavior activity in an automatic binary execution environment based on real-machines and the malicious behavior activity in a virtual-machine environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.86-96
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• 2016

Recently, virtualization is becoming the critical issue in the cloud computing due to its advantages of resource utilization and consolidation. In order to efficiently use virtualization services, several issues should be taken into account, including data reliability, security, and performance. In particular, a high write bandwidth on the virtual machine must be guaranteed to provide fast responsiveness to users. In this study, we implemented a way of visualizing comparison results between the block write pattern of KVM-based virtual machine and that of the real machine. Our final objective is to propose an optimized virtualization environment that enables to accelerate the disk write bandwidth.

• 전기의세계
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• v.24 no.5
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• pp.38-41
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• 1975

기술 및 소개한 내용을 아래와 같이 요약할 수 있다. 1)전력계통의 계산기제어를 위한 하아드웨어시스템은 온라인리얼시스템의 구비 조건인 고속성, 신뢰성, 경제성을 기저요건으로 하여 동시에 다중프로그래밍, 다중처리가 가능한 범용전자계산기를 모체로 하고 다수의 위성계산기에 의하여 국지적 제어도 가능한 계층제어 시스템의 구성을 이루고 있는 것이 현 추세이다. 2)전력계통의 계산기제어는 전력계통의 안정제어가 그 주내용이나, 전력공급의 질을 일정수준으로 유지하면서 가장 경제적으로 전력을 안정된 상태로 공급하는 것이 더욱 바람직하다. 그러나 현단계에서는 긴급상태 및 회복상태에서는 전력의 안정공급에만 주안점을 두고 있는 실정이다. 3)전력계통의 안정제어의 기능은 상태의 감시, 해석 및 안전최적화의 3기능이며 안정최적화 기능중의 제어지령도 리얼타임으로 자동화하는 것이 궁극적 목표이나 현단계로서는 CRT 표시장치등의 맨머신 인터훼이스를 통하여 제어지령의 결정과정에서 조작원이 개입하는 것이 상례이다. 즉, 전력계통의 안정제어는 아직까지는 프로세스제어의 단계에 이르지 못하고 오퍼레이션제어의 특징을 다분히 지니고 있다. 4)조작원의 판단이나 개입을 줄이는 첩경은 하아드웨어의 개선보다는 전력계통 리얼타임 제어에 충분할 정도로 처리시간에 단축되는 전력계통제어용 각종 응용프로그램의 개발에 있으며 그 시기는 아직 먼 것으로 전망된다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.297-298
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• 2010

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.528-530
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• 2011

Xen 가상머신에서 실시간적 성격을 가지는 게스트 도메인들의 워크로드가 가변적으로 변하는 상황에서 각 도메인들이 데드라인내에 작업을 처리하기 위해서는 효율적인 CPU 할당 기법이 필요하다. 본 연구에서는 리얼타임 도메인이 데드라인을 만족하는데 필요한 최소한의 CPU를 미리 할당하고 다른 게스트들에 부하가 발생하여 실시간 처리가 불가능한 상황이 발생하면 게스트 도메인의 수행 결과를 VMM 스케줄러에게 스케줄링 피드백을 주는 시스템을 제안한다. 또한, 다른 게스트간의 자원을 효율적으로 할당하고 반납할 수 있는 방법에 대해서 기술한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.207-218
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• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.