• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.1
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• pp.95-102
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• 2015

Smartphone is very useful for use in the real world, but it has been exposed to a lot of crime by smartphone. Also, it occurs attempting to delete a data of smartphone memory by anti-forensic tools. In this paper, we implement an anti-forensic tool used in the Android. In addition, tests to validate the availability of the anti-forensic tool by the Oxygen Forensic Suite that is a commercial forensic tool.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.803-806
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• 2005

In this paper, we analyses of Architecture for High-speed authentication protocol in wireless communication. The rapid process in wireless communication systems, personal communications, and smartcard technologies has brought new opportunities and challenges to be met by engineers and researchers working on the security aspects of the new communication. In real world, we have restricted hardware environments with limited computational power and small memory, we meet more challenges. Then we analyes the need of consideration to implement the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1039-1042
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• 1995

In real time application such as motion control, it is hard to find the application of non-causal filtering due to its need for future position data, even though it shows wide usage in off-line digital signal processing. Recently, some of motion control areas such as learning and repetitive control use non-causal filtering technique in their application. these kinds of zero-lag non-causal filter application are very usful not only to reduce the machine vibration, but also to increase control accuracy with comparatively less work. In this paper, genuine method to implement zero-lag non-causal filter in a CNC is introduced. Also the variation of this implementation for the learning operation is suggested to give the NC better control performance for a specific job. By adopting the new NC architecture call Soft-NC, all these implementions are made possible here, and especially large memory requirement which hinders their usage for many years is no longer barrier in their real world application.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.205-210
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• 2021

최근 메타버스 산업체 pc 하드디스크 메모리 저장 데이타에 대한 사이버 침해 공격 및 기밀유출 사고대응 방안으로 원격기반 상시 실시간 감시 및 분석 보안 체계를 검토 및 발전 시 요구된다. 이러한 사유는 정보보안 유출 90% 이상은 Edge단 pc에서 발생 되는 것으로 메타버스 산업보안기밀 유출 1건당 평균 10억 2,000만원 등의 유형 및 무형 피해(2018보안과 관련된 가장 중요한 팩트와 수치통계 2018.10.16일 IT WORLD) 발생의 근본에 대한 대응과 동시에 IT인프라 구축 시 연동 통합으로 네트웍 시스템 웹 등 영역에서 발생되는 악성코드 공격 대상 등의 대상이 되므로 이에 대한 대응책으로 Deep-Acess기반 상시 실시간 원격 메모리 분석 및 감사(audit)체계 개념이 핵심이다.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.209-214
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• 2008

Set-valued attributes appear in many applications to model complex objects occurring in the real world. One of the most important operations on set-valued attributes is the set join, because it provides a various method to express complex queries. Currently proposed set join algorithms are based on block nested loop join in which inverted files are partitioned horizontally into blocks. Evaluating these joins are expensive because they generate intermediate partial results severely and finally obtain the final results after merging partial results. In this paper, we present an efficient processing of set join algorithm. We propose a new set join algorithm that vertically partitions inverted files into blocks, where each block fits in memory, and performs block nested loop join without producing intermediate results. Our experiments show that the vertical bitmap nested set join algorithm outperforms previously proposed set join algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• The Journal of Information Systems
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• v.25 no.3
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• pp.175-196
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• 2016

Purpose The development of new products in automobile parts industry plays an important role. Nowadays, automobile industry is one of the most important industries in the world, because automobile industry includes the industries of steel, machinery, electronic, etc. As a result, automobile industry embodies a country's level of development and competitiveness. The purpose of this study is to analyze the relationship between team characteristics such as communication, familiarity, and contiguity, and team performance through TMS in the process of new products development in automobile parts industry. Design/Methodology/Approach Based on real phenomenon of Korean automobile parts industry, this study tried to figure out the influence of TMS between team members for new product development on team performance. Thus, this study adopts communication, familiarity and contiguity as antecedents, and TMS with specialty, credibility and coordination as a mediating variable, and adopts team performance as a dependent variable. The data of questionnaire were collected from team managers who belong to new product development team in automobile parts firms. 171 questionnaires as data unit of group level from 80 Korean automobile parts firms were collected using face-to-face interview methods. Findings This study proved empirically that the relationship between antecedents and TMS has a positive influence, and TMS also has a positive influence on team performance. This study provides the managers an insight that automobile parts companies should pay more attention to set up TMS in order to improve higher performance between their team members for new product development of automobile parts.

• Journal of the Korea Convergence Society
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• v.4 no.1
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• pp.21-26
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• 2013

Due to a lack of research regarding disruptive technologies in domestic research, the purpose of this study is to aid in the understanding of disruptive technologies through empirical analysis of cases selected in the computer data storage industry. Analysis results have shown that SSDs, which threaten the existence of HDDs, adhere to the conditions of being a disruptive technology as first presented by Christensen(1992). SSDs are not only technologically superior to HDDs but can be mass produced due to its applicability in a vast array of product categories made possible by their miniaturization, weight reduction, and safety. This diversity of applicable fields makes it possible for mass production leading to further decrease in the unit price ultimately continuing the diffusion of this technology. By presenting empirical cases to aid in the understanding of disruptive technology, it is determined that the findings of this study contribute greatly to both academia and the business world.

• Spatial Information Research
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• v.23 no.3
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• pp.45-54
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• 2015

Since the V-World, the Spatial Information Open Platform service, has started in 2012, a lot of people have increased explosively every year with their interest. It is necessary to know the specific service status in order to serve as indicators of the improvement of user's environment and the service to be added in the future based on the user's increasing need. However, there is difficulty to figure out more specific service status, such as the usage of hardware resources for 2D / 3D / Portal services and the actual user usage patterns, because the current system does not have the real-time monitoring system. Therefore, in this paper, through the analysis of the usage of system resources for 2D / 3D / Portal services based on web server log and the usage of hardware resources such as CPU, Memory based on system log, we analyze the usage of service in 2015 and compare with the results of the 2014, to present problems of the current system and the solutions about the problems.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.625-641
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• 2015

Recent research shows that emotion is closely related to memory and learning. Although a growing number of educators have high interest in affective aspects of learning processes and outcomes, there are few studies to investigate systematically instructional strategies and learning environments based on learners' emotion. Despite the efforts to understand the role of emotion and to facilitate positive emotion for meaningful learning in face-to-face and online environments, it is still a challenging issue to measure emotion in a valid and reliable way. To implement emotion-based education, it is essential to overcome the limitation of self-report surveys on emotion, which rely on the memory of learners. The current study surveyed emotion measurement tools, which are recently developed in education and other domains, in terms of self-report, neurophysiology, and behavioral responses. This study also discussed how emotion measurement tools can be used in authentic learning and teaching situations. Particularly, this study focused on cutting-edge technologies that would enable educators to collect and analyze learners' emotion easily in real-world contexts. This study will contribute to the research about the role of emotion in education and the design of adaptive learning environments that consider the change of learners' emotion.