• Annual Conference of KIPS
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• 2009.04a
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• pp.770-771
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• 2009

리눅스 커널을 VMM(Virtual Machine Monitor)로 만들어 주는 KVM의 메모리 관리 기법을 분석한다. Xen과의 차이점과 KVM의 구조를 알아보고 KVM에서의 메모리 관리 기법에 대해 분석하였다. 또한 CPU의 가상화 기능인 Intel VT-x가 어떻게 적용되었는지 분석한다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.377-382
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• 2005

This paper describes the design of micro machine tools system for mechanical machining of micro/meso scale mechanical parts. The micro machining systems such as $\mu-Late$, $\mu-milling/drilling$ machine and $\mu-grinding$ machine are the basic elements constructing $\mu-factory$ which gains more attention recently because of increasing needs of mico and nano-parts in various industrial and medical area. A miniaturized 3-axis milling machine with VCM stage and air spindle and palm-top size micro-late are designed, and air bearing stage and stepwise linear motion system with PZT are studied for motion system. The micro cutting characteristics are investigated experimentally, and reconfigurable machine structures are also considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.467-479
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• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.11-19
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• 2017

In this paper, we propose a hypervisor for embedded systems based on ARM microprocessor. The proposed hypervisor makes it possible to run several real-time kernels concurrently on a single embedded system by virtualizing its microprocessor. With assistance of MMU, it supports virtual memory which enables each guest operating system has its own address space. Exploiting the fact that most embedded systems use memory-mapped I/O device, it provides a mechanism to distribute an external interrupt to virtual machines properly. It also achieves load balancing through live migration which moves a running virtual machine to other embedded system. Unlike other para-virtualization techniques, minor modifications are needed to run it on the hypervisor. Extensive performance measurement studies are conducted to show that the proposed hypervisor has enough potentiality of its real-world application.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.75-79
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• 2001

In the recent years, as the research and the development of micro and precision machinery become active, the interest of micro actuators using SMA(Shape Memory Alloy) has been increased. The dynamic characteristic analysis of SMA is necessary for actuator application and many common researches report the material characteristics of SMA sufficiently. However, the research on dynamic characteristics is very deficient. In this paper, the helical spring are fabricated with NiTi SMA wire of high resistivity, The force, response speed, temperature, and displacement are measured by digital force gauge, infrared thermometer, and laser displacement sensor so that the dynamic characteristics of this SMA is analyzed. Also, bidirectional actuator was fabricated and experimented for its performance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.147-152
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• 1999

The strengthening of a metal matrix composite(MMC) by the shape memory effect(SME) of dispersed TiNi particles was theoretically studied. An analytical model was constructed for the prediction of the average residual stress(<$\sigma$>/sub/m) on the base of the Eshelby's equivalent inclusion method. The analysis was performed on the TiNi particle/Al metal matrix composites with varying volume fractions and prestrains of the particle. The residual stress caused by the shape memory of predeformed fillers has been predicted to contribute significantly to the strengthening of this composite.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.339-341
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• 2024

본 논문에서는 전기 자동차 배터리 팩 설계에서 성능 예측을 위해 전산유체해석 및 Long Short-Term Memory (LSTM)를 활용한다. 두 계산 모두의 예측이 상당한 유사성을 나타내며, 전산유체해석은 시스템 유체 역학을 고려한 상세한 물리 모델을 제공하고, LSTM은 시계열 데이터를 기반으로 한 딥러닝 모델로 효과적으로 패턴을 파악, 향후 온도 상승을 예측한다. 결과는 두 접근 모두가 효과적인 예측을 제공하며 향후 전기 자동차 배터리 팩 설계 및 최적화에서 종합적인 접근의 필요성을 강조한다. 특히, LSTM 기반 예측에 소요되는 시간은 계산 유체 역학의 약 25%로, 약 일주일 정도로 빠르게 확인 가능하다. 이는 현대 산업 환경에서 시간적 효율성이 중요한 측면을 강조하며, 계산 유체 역학의 상세한 물리 모델링과 LSTM의 빠른 예측 속도를 결합한 설계 방법론을 제안한다.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.