• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1710-1717
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• 2009

Flash memory has become the most important storage media in mobile devices along with its attractive features such as low power consumption, small size, light weight, and shock resistance. However, a flash memory can not be written before erased because of its erase-before-write characteristic, which lead to some garbage collection when there is not enough space to use. In this paper, we propose a novel garbage collection scheme, called block-level buffer garbage collection. When it is need to do merge operation during garbage collection, the proposed scheme does not merge the data block and corresponding log block but also search the block-level buffer to find the corresponding block which will be written to flash memory in the next future, and then decide whether merge it in advance or not. Our experimental results show that the proposed technique improves the flash performance up to 4.6% by reducing the unnecessary block erase numbers and page copy numbers.

• Journal of The Korean Astronomical Society
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• v.52 no.5
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• pp.207-216
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• 2019

In this paper we introduce a software package, the Very long baseline interferometry Network SIMulator (VNSIM), which provides an integrated platform assisting radio astronomers to design Very Long Baseline Interferometry (VLBI) experiments and evaluate the network performance, with a user-friendly interface. Though VNSIM is primarily motivated by the East Asia VLBI Network, it can also be used for other VLBI networks and generic interferometers. The software package not only integrates the functionality of plotting (u, v) coverage, scheduling the observation, and displaying the dirty and CLEAN images, but also adds new features including sensitivity calculations for a given VLBI network. VNSIM provides flexible interactions on both command line and graphical user interface and offers friendly support for log reports and database management. Multi-processing acceleration is also supported, enabling users to handle large survey data. To facilitate future developments and updates, all simulation functions are encapsulated in separate Python modules, allowing independent invoking and testing. In order to verify the performance of VNSIM, we performed simulations and compared the results with other simulation tools, showing good agreement.

• Korean Journal of Veterinary Research
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• v.59 no.2
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• pp.101-104
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• 2019

This study examined the disinfection conditions (exposure time, 0-30 min; exposure temperature, $4^{\circ}C-65^{\circ}C$) of hypochlorous acid water (HOCl) in automobile disinfection equipment. The study tested poliovirus type 1 (PV1), low pathogenic avian influenza virus (AIV, H9N2), and foot and mouth disease virus (FMDV, O type). As a result, the PV1 and FMD viruses were inactivated easily (virus titer 4 log value) by HOCl (> 100 ppm) but the AIV required higher exposure temperatures (> $55^{\circ}C$). In conclusion, the exposure temperature and time are important factors in deactivating AIV and FMDV.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.1-21
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• 2018

Due to the increase in the use of the Internet, it is becoming more common to provide or use a social login for registering for services. Herein, the purpose of this study is to analyze the influence of security, individual innovativeness, ubiquity and brand awareness on the use of social log-in service through the individuals' attitude who have memorized various IDs and passwords by using Technology Acceptance Model (TAM). In addition, the effect of individual innovativeness on the relationship between social login characteristic factors and attitudes toward social login services are examined. Based on the statistical results, it is found that the significant factors affecting the attitude toward the social login service are the security, ubiquity, brand awareness and perceived usefulness. Moreover, the individual innovativeness is found to have the moderating effects in the relationship between the three factors (the perceived usefulness, and perceived ease of use, and security) and attitude toward the social login service. Positive attitudes increase with the usefulness, ease of using social login serive when individual innovativeness is high. On the other hand, when individual innovativeness is low, the stronger the effect of security on attitude toward the social login services. In accordance with these results, the implications and limitations of this study are discussed.

• Microbiology and Biotechnology Letters
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• v.48 no.4
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• pp.429-438
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• 2020

The emergence of multi-drug resistant, pathogenic methicillin-resistant Staphylococcus aureus (MRSA) is a threat to global health and has created a need for novel functional therapeutic agents. In this study, we evaluated the underlying mechanisms of the anti-MRSA effect of an azaphilone pigment, sclerotiorin (SCL) from Penicillium sclerotiorum. The antimicrobial activity of SCL was evaluated using agar disc diffusion, broth microdilution, time-kill assays and biophysical studies. SCL exhibits selective activity against Gram positive bacteria including MRSA (range, MIC = 128-1028 ㎍/ml) and exhibited rapid bactericidal action against MRSA with a > 4 log reduction in colony forming units within three hours of administration. Biophysical studies, using fluorescent probes and laser or electron microscopy, demonstrated a SCL dose-dependent alternation in membrane potential (62.6 ± 5.0.4% inhibition) and integrity (> 95 ± 2.3%), and the release of UV260 absorbing materials within 60 min (up to 3.2 fold increase, p < 0.01) of exposure. Further, SCL localized to the cytoplasm and hydrolyzed plasmid DNA. While in vitro checkerboard studies revealed that SCL potentiated the antimicrobial activity of topical antimicrobials such as polymixin, neomycin, and bacitracin (Fractional Inhibitory Concentration Index range, 0.26-0.37). Taken together these results suggest that SCL targets the membrane and DNA of MRSA to facilitate its anti-MRSA antimicrobial effect.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.87-96
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• 2021

As the industry develops, the technology used for games is also being advanced. In particular, AI technology is used to game automation and intelligence. These game player patterns are widely used in online games such as player matchmaking, generation of friendly or hostile NPCs, and balancing of game worlds. This study proposes a model generation method for game players. For model generation, attributes such as hunting, collection, movement, combat, crisis management, production, and interaction were defined, and patterns were extracted and modeled using decision tree method. To evaluate the proposed method, we used the game log of a commercial game and confirmed the meaningful results.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.509-515
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• 2022

As the 4th industrial revolution based on ICT is progressing in the manufacturing field, interest in building smart factories that can be flexible and customized according to customer demand is increasing. To this end, it is necessary to maximize the efficiency of factory by performing an automated process in real time through a network communication between engineers and equipment to be able to link the established IT system. It is also necessary to collect and store real-time data from heterogeneous facilities and to analyze and visualize a vast amount of data to utilize necessary information. Therefore, in this study, four types of controllers such as PLC, Arduino, Raspberry Pi, and embedded system, which are generally used to build a smart factory that can connect technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, are configured. This study was conducted for the development of a program that can collect and store data in real time to visualize and manage information. For communication verification by controller, data communication was implemented and verified with the data log in the program, and 3D monitoring was implemented and verified to check the process status such as planned quantity for each controller, actual quantity, production progress, operation rate, and defect rate.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.60-66
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• 2021

Blockchain is a technology that enables trust-based consensus and verification based on a decentralized network. Distributed ID (DID) is based on a decentralized structure, and users have the right to manage their own ID. Recently, interest in self-sovereign identity authentication is increasing. In this paper, as a method for transparent and safe sovereignty management of data, among data pseudonymization techniques for blockchain use, various methods for data encryption processing are examined. The public key technique (homomorphic encryption) has high flexibility and security because different algorithms are applied to the entire sentence for encryption and decryption. As a result, the computational efficiency decreases. The hash function method (MD5) can maintain flexibility and is higher than the security-related two-way encryption method, but there is a threat of collision. Zero-knowledge proof is based on public key encryption based on a mutual proof method, and complex formulas are applied to processes such as personal identification, key distribution, and digital signature. It requires consensus and verification process, so the operation efficiency is lowered to the level of O (log_eN) ~ O(N²). In this paper, data encryption processing for blockchain DID, based on zero-knowledge proof, was proposed and a one-way encryption method considering data use range and frequency of use was proposed. Based on the content presented in the thesis, it is possible to process corrected zero-knowledge proof and to process data efficiently.

• Educational Technology International
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• v.18 no.2
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• pp.193-213
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• 2017

This study aimed to explore online learning profiles of in-service teachers in South Korea, focusing on video lecture and discussion activities. A total of 269 teachers took an online professional development course for 14 days, using an online learning platform from which web log data were collected. The data showed the frequency of participation and the initial participation time, which was closely related to procrastinating behaviors. A cluster analysis revealed three online learning profiles of in-service teachers: procrastinating (n=42), passive interaction (n=136), and active learning (n=91) clusters. The active learning cluster showed high-level participation in both video lecture and discussion activities from the beginning of the online course, whereas the procrastinating cluster was seldom engaged in learning activities for the first half of the learning period. The passive interaction cluster was actively engaged in watching video lectures from the beginning of the online course but passively participated in discussion activities. As a result, the active learning cluster outperformed the passive interaction cluster in learning achievements. The findings were discussed in regard to how to improve online learning environments through considering online learning profiles of in-service teachers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.