• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.803-809
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• 2023

This paper analyzes and evaluates to optimally build a data collection system essential for factory energy management systems on an edge-based lightweight platform. A "Sensor/OPC-UA simulator" was developed based on sensors in an actual food factory and used to evaluate the storage engine of edge devices. The performance of storage engines in edge devices was evaluated to suggest the optimal storage engine. The experimental results show that when using the RocksDB storage engine, it has less than half the memory and database size compared to using InnoDB, and has a 3.01 times faster processing time. This study enables the selection of advantageous storage engines for managing time-series data on devices with limited resources and contributes to further research in this field through the sensor/OPC simulator.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1299-1306
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• 2023

In this paper, we present a system for undesignated cultural heritage management and real-time theft chase, which uses flash-based large-capacity storage. The proposed system is composed of 3 parts, such as a cultural management device, a flash-based server, and a monitoring service for managing cultural heritages and chasing thefts using IoT technologies. However flash-based storage needs methods to overcome the limited lifespan. Therefore, in this paper, we present a system, which uses the disk buffer in flash-based storage to overcome the disadvantage, and evaluate the system performance in various environments. In our experiments, LRU policy shows the number of direct writes in the flash-based storage by 10.7% on average compared with CLOCK and FCFS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• IMMUNE NETWORK
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• v.24 no.1
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• pp.9.1-9.21
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• 2024

The cytokine IL-7 plays critical and nonredundant roles in T cell immunity so that the abundance and availability of IL-7 act as key regulatory mechanisms in T cell immunity. Importantly, IL-7 is not produced by T cells themselves but primarily by non-lymphoid lineage stromal cells and epithelial cells that are limited in their numbers. Thus, T cells depend on cell extrinsic IL-7, and the amount of in vivo IL-7 is considered a major factor in maximizing and maintaining the number of T cells in peripheral tissues. Moreover, IL-7 provides metabolic cues and promotes the survival of both naïve and memory T cells. Thus, IL-7 is also essential for the functional fitness of T cells. In this regard, there has been an extensive effort trying to increase the protein abundance of IL-7 in vivo, with the aim to augment T cell immunity and harness T cell functions in anti-tumor responses. Such approaches started under experimental animal models, but they recently culminated into clinical studies, with striking effects in re-establishing T cell immunity in immunocompromised patients, as well as boosting anti-tumor effects. Depending on the design, glycosylation, and the structure of recombinantly engineered IL-7 proteins and their mimetics, recombinant IL-7 molecules have shown dramatic differences in their stability, efficacy, cellular effects, and overall immune functions. The current review is aimed to summarize the past and present efforts in the field that led to clinical trials, and to highlight the therapeutical significance of IL-7 biology as a master regulator of T cell immunity.

• Journal of IKEEE
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• v.28 no.2
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• pp.168-173
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• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• Korean Journal of Materials Research
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• v.34 no.6
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• pp.283-290
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• 2024

The aggressive scaling of dynamic random-access memory capacitors has increased the need to maintain high capacitance despite the limited physical thickness of electrodes and dielectrics. This makes it essential to use high-k dielectric materials. TiO₂ has a large dielectric constant, ranging from 30~75 in the anatase phase to 90~170 in rutile phase. However, it has significant leakage current due to low energy barriers for electron conduction, which is a critical drawback. Suppressing the leakage current while scaling to achieve an equivalent oxide thickness (EOT) below 0.5 nm is necessary to control the influence of interlayers on capacitor performance. For this, Pt and Ru, with their high work function, can be used instead of a conventional TiN substrate to increase the Schottky barrier height. Additionally, forming rutile-TiO₂ on RuO₂ with excellent lattice compatibility by epitaxial growth can minimize leakage current. Furthermore, plasma-enhanced atomic layer deposition (PEALD) can be used to deposit a uniform thin film with high density and low defects at low temperatures, to reduce the impact of interfacial reactions on electrical properties at high temperatures. In this study, TiO₂ was deposited using PEALD, using substrates of Pt and Ru treated with rapid thermal annealing at 500 and 600 ℃, to compare structural, chemical, and electrical characteristics with reference to a TiN substrate. As a result, leakage current was suppressed to around 10^-6 A/cm² at 1 V, and an EOT at the 0.5 nm level was achieved.

• Clinical and Experimental Vaccine Research
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• v.12 no.1
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• pp.60-69
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• 2023

Purpose: Although the fast development of safe and effective messenger RNA (mRNA) vaccines against severe acute respiratory syndrome coronavirus 2 has been a success, waning humoral immunity has led to the recommendation of booster immunization. However, knowledge of the humoral immune response to different booster strategies and the association with adverse reactions is limited. Materials and Methods: We investigated adverse reactions and anti-spike protein immunoglobulin G (IgG) concentrations among health care workers who received primary immunization with mRNA-1273 and booster immunization with mRNA-1273 or BNT162b2. Results: Adverse reactions were reported by 85.1% after the first dose, 94.7% after the second dose, 87.5% after a third dose of BNT162b2, and 86.0% after a third dose of mRNA-1273. They lasted for a median of 1.8, 2.0, 2.5, and 1.8 days, respectively; 6.4%, 43.6%, and 21.0% of the participants were unable to work after the first, second, and third vaccination, respectively, which should be considered when scheduling vaccinations among essential workers. Booster immunization induced a 13.75-fold (interquartile range, 9.30-24.47) increase of anti-spike protein IgG concentrations with significantly higher concentrations after homologous compared to heterologous vaccination. We found an association between fever, chills, and arthralgia after the second vaccination and anti-spike protein IgG concentrations indicating a linkage between adverse reactions, inflammation, and humoral immune response. Conclusion: Further investigations should focus on the possible advantages of homologous and heterologous booster vaccinations and their capability of stimulating memory B-cells. Additionally, understanding inflammatory processes induced by mRNA vaccines might help to improve reactogenicity while maintaining immunogenicity and efficacy.

• The Korean Journal of Archival Studies
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• no.41
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• pp.175-212
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• 2014

In 2001 a large amount of labor record have been donated from Jeontaeil Labor Archive-Institute to SungKongHoe University(SKHU). Institute of Labor History in SKHU was established in the wake of the installation of the labor archive. Development of oral archive raised the awareness of the various relationships between the use and production of labor record. Interviewees of oral testimony expressed dissatisfaction and the role of the researchers was not sufficiently exhibited. Examining the main cases of Korea union movement history, we can find contradictions between the use and production of labor record clearly. Interval of interpretation and memory was too big between the parties of 'democratic' union movement in the 1970s. While among the parties who took part in Guro Alliance Strike of 1985, there is a group that remains in the "winner" in history on the one hand, but "loser" on the other without any reasonable criterion. Active intervention of the record users(researchers) is very limited. Among citizens or workers how will be resolved such "struggle of memory" in due process can not be seen. This is one of the reasons why labor archive is not rooted in the region. In this paper, I present a methodological alternatives for the production and use of records through the construction of participatory labor archive. Further, the reconstituted contents of the "documenting locality" strategy by complementing the theoretical part of the method of participation. The study of local and locality requires a "scale" dimension that will make up the identity recognition space, a memory and identity, a social relationship rather than the dimension of the physical space. Alternative "documenting locality" strategy will be able to contribute to solve the problems that occur between the production and use of the recording in labor archive.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.