• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1051-1077
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• 2021

The secure communication of information is a major concern over the internet. The information must be protected before transmitting over a communication channel to avoid security violations. In this paper, a new hybrid method called compressed encrypted data embedding (CEDE) is proposed. In CEDE, the secret information is first compressed with Lempel Ziv Welch (LZW) compression algorithm. Then, the compressed secret information is encrypted using the Advanced Encryption Standard (AES) symmetric block cipher. In the last step, the encrypted information is embedded into an image of size 512 × 512 pixels by using image steganography. In the steganographic technique, the compressed and encrypted secret data bits are divided into pairs of two bits and pixels of the cover image are also arranged in four pairs. The four pairs of secret data are compared with the respective four pairs of each cover pixel which leads to sixteen possibilities of matching in between secret data pairs and pairs of cover pixels. The least significant bits (LSBs) of current and imminent pixels are modified according to the matching case number. The proposed technique provides double-folded security and the results show that stego image carries a high capacity of secret data with adequate peak signal to noise ratio (PSNR) and lower mean square error (MSE) when compared with existing methods in the literature.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.872-884
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• 2022

With the development of the IoT industry, different types of time series data are being generated in various industries, and it is evolving into research that reproduces and utilizes it through re-integration. In addition, due to data processing speed and issues of the utilization system in the actual industry, there is a growing tendency to compress the size of data when using time series data and integrate it. However, since the guidelines for integrating time series data are not clear and each characteristic such as data description time interval and time section is different, it is difficult to use it after batch integration. In this paper, two integration methods are proposed based on the integration criteria setting method and the problems that arise during integration of time series data. Based on this, integration framework of a heterogeneous time series data was constructed that is considered the characteristics of time series data, and it was confirmed that different heterogeneous time series data compressed can be used for integration and various machine learning.

• Journal of Fashion Business
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• v.26 no.4
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• pp.112-122
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• 2022

The purpose of this study is to investigate the effect of using socks combined with EMS on ankle pain reduction and ankle function improvement in home training participants. In this study, the conductive fabric was combined using socks that can properly compress the ankle. First, VAS was measured during EMS training after fatigue was induced and compared with fatigue during rest. It was confirmed that the level of VAS after EMS training was lower than after rest and fatigue. It was also confirmed that EMS training, which combines EMS with socks, was effective in reducing pain. The experimental action is a measurement action of WBLT and lying posture, and the situation before and after EMS training was compared by performing 30 minutes on the treadmill to cause delayed muscle pain during exercise. As a result of this study, it was found that pain reduction and ROM function were improved when electrical stimulation was performed using EMS socks. It was also confirmed that the application of electrical stimulation to EMS socks effected on ankle fatigue and function improvement. From the study results, it is expected that wearing socks equipped with EMS significantly reduces ankle injuries and improves functional recovery for home training participants.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.42-54
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• 2023

It is an important issue to compress huge holographic data in a digital format. In particular, research on the compression of phase-only holograms for commercialization is noteworthy. Conventional video coding standards optimized for natural images are not suitable for compressing phase signals, and neural network-based compression model that can be optimized for phase signals can achieve high performance, but has a memory issue in learning high-resolution holographic data. In this paper, we show that by applying a block-based learned image compression model that can solve memory problems to phase-only holograms, the proposed method can demonstrate significant performance improvement over standard codecs even under the same conditions as block-based. Block-based learned compression model can provide compatibility with conventional standard codecs, solve memory problems, and can perform significantly better against phase-only hologram compression.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.2
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• pp.141-148
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• 2023

The thermodynamic performance of the electrochemical hydrogen compressor was analyzed to perform a comparative analysis with the performance of the mechanical compressor. The performance was analyzed through the applied current and the measured voltage value. The test results showed that the efficiency of the electrochemical hydrogen compressor was high in the low current density range. In addition, it was confirmed that the amount of increasing compress work of the electrochemical hydrogen compressor is smaller than that of the mechanical compressor. Therefore, it is expected to have higher efficiency than mechanical compression when compressed with a sufficiently high-pressure range.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.236-239
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• 2021

With the growth of AI technology, the scale of Knowledge Graphs continues to be expanded. Knowledge Graphs are mainly expressed as RDF representations that consist of connected triples. Many RDF storages compress and transform RDF triples into the condensed IDs. However, if we try to transform a large scale of RDF triples, it occurs the high processing time and memory overhead because it needs to search the large ID mapping table. In this paper, we propose the method of converting RDF triples using Hash-based ID mapping tables with MapReduce, which is the software framework with a parallel, distributed algorithm. Our proposed method not only transforms RDF triples into Integer-based IDs, but also improves the conversion speed and memory overhead. As a result of our experiment with the proposed method for LUBM, the size of the dataset is reduced by about 3.8 times and the conversion time was spent about 106 seconds.

• Tribology and Lubricants
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• v.39 no.6
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• pp.268-272
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• 2023

This study explores the characteristics of tilting pad journal bearings used in the high-speed rotating shaft systems of centrifugal compressors. A centrifugal compressor is a high-speed rotating machine that is widely used to compress gases or vapors employed in various industrial applications. It transfers the centrifugal force of a fast-spinning impeller to the fluid and compresses it under high pressure. Many high-speed rotating shaft systems, which require high stability, use tilting pad journal bearings. The characteristics of these bearings can vary depending on several properties, and identifying the appropriate characteristics is essential to optimize the design on a case-to-case basis. In this study, the authors perform a time-dependent analysis of the properties of tilting pad journal bearings and the rotordynamics of the rotating shaft system using COMSOL Multiphysics software. Specifically, the authors analyze the characteristics of the tilting pad journal bearings by performing a parametric sweep using parameters such as pad clearance, maximum tilting angle, preload, number of pads, and pad pivot offset. The authors then use the results of the bearing-characteristics analysis to evaluate the vibration of the rotating shaft and verify its operation within a desirable range. The understanding gained from this study will allow us to determine the optimal properties of these bearings and the limiting operational speed using COMSOL Multiphysics software.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.379-392
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• 2024

With the rapid increase in data, saving storage space and improving the efficiency of data transmission have become critical issues, making the research on the efficiency of data compression technologies increasingly important. Lossless algorithms can precisely restore original data but have limited compression ratios, whereas lossy algorithms provide higher compression rates at the expense of some data loss. There has been active research in data compression using deep learning-based algorithms, especially the autoencoder model. This study proposes a new side-channel analysis data compressor utilizing autoencoders. This compressor achieves higher compression rates than Deflate while maintaining the characteristics of side-channel data. The encoder, using locally connected layers, effectively preserves the temporal characteristics of side-channel data, and the decoder maintains fast decompression times with a multi-layer perceptron. Through correlation power analysis, the proposed compressor has been proven to compress data without losing the characteristics of side-channel data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.99-107
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• 2024

The recent digital transformation in many industries has led to an explosion of data, which has exponentially increased the amount of data that companies need to generate and process. As a result, enterprises are leveraging ERP systems to manage and analyze large amounts of data in real time. However, due to cost and time issues in processing large amounts of data in existing ERP systems, it is essential to apply data archiving technology that can compress and store data in real time in existing systems. Therefore, this paper aims to identify the trends of the target technology by utilizing patent data on ERP-based real-time data archiving technology, analyze the core patents, and create new core patents based on them.

• Journal of Korean Medicine Rehabilitation
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• v.34 no.2
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• pp.71-83
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• 2024

Objectives The purpose of this study is to analyze the trends in domestic and international clinical research on the Korean medicine for triangular fibrocartilage complex (TFCC) injuries and to contribute to future domestic research. Methods In this study, clinical studies on TFCC injuries treated with Korean medicine were searched through databases such as Research Information Sharing Service, ScienceON, DBpia, China National Knowledge Infrastructure, PubMed, Medline, and Embase. Fifteen clinical studies were selected, of which publication years, intervention methods, assessment tools, and various other factors were analyzed, including potential side effects. Results Among the selected 15 studies, total of 13 intervention methods were used. Electroacupuncture was the most frequently used method (40%), followed by herbal medicine (33%), acupuncture (26.7%), chuna manual therapy (26.7%), pharmacopuncture (20%), acupotomy (13%), herbal ointment (13%), hot medicinal compress therapy (13%) and etc. Most studies showed that Korean medicine treatments effectively alleviated TFCC injuries, with visual analogue scale being the most commonly used assessment tool. Conclusions Korean medicine treatments for TFCC injuries appears to be effective; however, further research is needed to enhance understanding. Large-scale clinical trials comparing the efficacy of Korean medicine treatments, along with investigations into side effects and recurrence rates are necessary. By doing so, the safety and efficacy of Korean medicine treatments for TFCC injuries can be further established.