• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.33-42
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• 2016

Recently, the investment and study competition regarding machine running is accelerating mainly with Google, Amazon, Microsoft and other leading companies in the field of artificial intelligence. The security weakness of virtualization technology security structure have been a serious issue continuously. Also, in most cases, the internal data security depend on the virtualization security technology of platform provider. This is because the existing software, hardware security technology is hard to access to the field of virtualization and the efficiency of data analysis and processing in security function is relatively low. This thesis have applied user significant information to machine learning algorithm, created security authentication vector able to learn to provide with a method which the security authentication can be conducted in the field of virtualization. As the result of performance analysis, the interior transmission efficiency of authentication vector in virtualization environment, high efficiency of operation method, and safety regarding the major formation parameter were demonstrated.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.495-509
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• 2011

According to the rapid technological development such as ubiquitous environments, there has been growing interest in learning with social media as known as social learning. This study was conducted to analyze various participation types of social media contents aiming to explore strategies for facilitating learning. Specifically, the research model was established by two aspects in using social media contents. First was classified by writings and readings in contents, which consists of prosumers, producers, consumers, and non-participants. Second criterion was categorized by instruction-related and instruction-nonrelated, which is learning contents, learning management, emotional expression, and social activities. In order to acquire empirical data, a set of fourteen undergraduate students participated in this research for eight weeks using a microblog. Based on the analyses on the data through learning activities, three learning strategies were suggested to facilitate social media based learning: analysis on learners, role of the instructor, and instructional model design.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1237-1246
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• 2020

Recently, several cache side channel attacks have been proposed to extract secret information by exploiting design flaws of the microarchitecture. The Flush+Reload attack, one of the cache side channel attack, can be applied to malicious application attacks due to its properties of high resolution and low noise. In this paper, we proposed a detection system, which detects the cache-based attacks using the PCM(Performance Counter Monitor) for monitoring CPU cache activity. Especially, we observed the variation of each counter value of PCM in case of two kinds of attacks, Spectre attack and secret recovering attack during AES encryption. As a result, we found that four hardware counters were sensitive to cache side channel attacks. Our detector based on machine learning including SVM(Support Vector Machine), RF(Random Forest) and MLP(Multi Level Perceptron) can detect the cache side channel attacks with high detection accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.71-82
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• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.245-254
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• 2022

Proteins are the basic unit of all life activities, and understanding them is essential for studying life phenomena. Since the emergence of the machine learning methodology using artificial neural networks, many researchers have tried to predict the function of proteins using only protein sequences. Many combinations of deep learning models have been reported to academia, but the methods are different and there is no formal methodology, and they are tailored to different data, so there has never been a direct comparative analysis of which algorithms are more suitable for handling protein data. In this paper, the single model performance of each algorithm was compared and evaluated based on accuracy and speed by applying the same data to CNN, LSTM, and GRU models, which are the most frequently used representative algorithms in the convergence research field of predicting protein functions, and the final evaluation scale is presented as Micro Precision, Recall, and F1-score. The combined models CNN-LSTM and CNN-GRU models also were evaluated in the same way. Through this study, it was confirmed that the performance of LSTM as a single model is good in simple classification problems, overlapping CNN was suitable as a single model in complex classification problems, and the CNN-LSTM was relatively better as a combination model.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.30-35
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• 2019

An unattended recording system based on smartphone based image image tracking is rapidly developing. Among the existing products, a system that automatically tracks and rotates the object to be photographed using an infrared signal is very expensive for general users. Therefore, this paper proposes a mobile unattended recording system that enables automatic recording by anyone who uses a smartphone. The system consists of a commercial mobile camera, a servomotor that moves the camera from side to side, a microcontroller to control the motor, and a commercial wireless Bluetooth Earset for video audio input. In this paper, we designed a system that enables unattended recording through image tracking using smartphone.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.3
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• pp.77-85
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• 2022

In this study, to diagnose the grinding state of a micro drill bit, a sensor attachment location was selected through random vibration analysis of the grinding unit of the micro drill-bit grinding system. In addition, the vibration data generated during the drill bit grinding were collected from the grinding unit for the grinding wheels under the steady and worn conditions, and data feature extraction and dimension reduction were performed. The wear of the micro-drill-bit grinding wheel was diagnosed by applying KNN, a machine-learning algorithm. The classification model showed excellent performance, with an accuracy of 99.2%. The precision, recall and f1-score were higher than 99% in both the steady and wear conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.499-501
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• 2021

Recently, artificial intelligence and SW have occupied an important position worldwide as the foundation technology of the era of the 4th industrial revolution, and web browser-based programming learning systems are becoming common due to changes in the learning environment caused by COVID-19. In accordance with this trend, this paper proposes a functionally scalable microservice-based system structure for an online evaluation system as a tool for learning algorithms that are the basis of artificial intelligence and SW. In addition, a functional structure for applying machine learning to automatic evaluation functions under the proposed system structure is also proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.986-988
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• 2022

본 연구는 전기전자기기를 비접촉 ON/OFF제어와 기기의 수명연장을 개선 시키기위해 전기전자기기에 다용도로 활용되는 제어컨트롤러 모듈인 PLC(Programmable Logic Controller)의 입력측에 마이크로컨트롤러와 AI 비젼카메라를 설치하여, 비접촉 ON/OFF 제어에 관한 아이디어 제시하고, 이를 기반으로 구현하였다. 구현 결과 단순 I,O 신호에 의한 제어와는 다르게 이미지 인식을 구체적으로 구분하여 센싱하고, 다양한 인식 구분을 위해 머신러닝 기반으로 AI 비젼카메라를 학습시킨 결과 물체 및 색깔 구분에 따라서 전기전자기기를 제어 할 수 있었으며, 접촉이 아닌 비접촉 ON/OFF 제어가 간단하게 구현되어, 전기전자기기 수명연장도 기대 할 수 있게 되었다..

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.435-452
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• 2023

This study experimented with automatic classification of subject headings using BERT-based transfer learning model, and analyzed its performance. This study analyzed the classification performance according to the main class of KDC classification and the category type of subject headings. Six datasets were constructed from Korean national bibliographies based on the frequency of the assignments of subject headings, and titles were used as classification features. As a result, classification performance showed values of 0.6059 and 0.5626 on the micro F1 and macro F1 score, respectively, in the dataset (1,539,076 records) containing 3,506 subject headings. In addition, classification performance by the main class of KDC classification showed good performance in the class General works, Natural science, Technology and Language, and low performance in Religion and Arts. As for the performance by the category type of the subject headings, the categories of plant, legal name and product name showed high performance, whereas national treasure/treasure category showed low performance. In a large dataset, the ratio of subject headings that cannot be assigned increases, resulting in a decrease in final performance, and improvement is needed to increase classification performance for low-frequency subject headings.