• Bulletin of the Korean Chemical Society
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• v.18 no.7
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• pp.715-719
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• 1997

In order to study the dielectric properties of the oriental lacquer films, three different films have been prepared differing purification and curing procedures. Dielectric properties were measured in the frequency range of 1 Hz to 105 Hz at various temperatures between - 50 ℃ and 150 ℃. The DEA using 1 Hz showed that glass transition and secondary relaxation temperatures of oriental lacquer film are very time dependent. In addition, the frequency-independent negative peak between 25 ℃ and 45 ℃ was observed, which could represent the formation of crosslink by laccase enzyme during heating. On the contrary, the high temperature cured film showed a hardly noticeable negative peak at the temperature range. The relationship between thermodynamic properties and chemical structures has been discussed based on the analysis of the dielectric relaxation behavior using the Cole-Cole plot and the dielectric relaxation intensity.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.39-48
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• 2023

The purpose of this study is to revitalize ESG tourism in Bhutan by comparing and analyzing the ESG tourism status in Bhutan and the ESG tourism status in Korea. Big data analysis using text mining was performed by selecting "Bhutan ESG Tourism" and "Korea ESG Tourism" as keywords. The top 30 keywords were extracted through word purification, and based on this, data visualization was conducted through network analysis and Concor analysis between each keyword. As a result of the analysis, it was confirmed that Bhutan, unlike Korea, did not utilize it even though it had elements to incorporate ESG and the tourism industry into the country itself. As a result, since it is necessary to combine ESG elements owned by Bhutan and combine them with the tourism industry, we would like to suggest the direction of combining ESG and the tourism industry through this study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.550-551
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected.

• Journal of Marine Bioscience and Biotechnology
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• v.12 no.2
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• pp.81-90
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• 2020

Sialic acid, which is contained in about 60-160 mg/kg in the edible bird's nest (EBN), is known to facilitate in the proper formation of synapses and improve memory function. The objective of this study is to extract effectively the sialic acid from edible bird's nest using affinity bead technology (ABT). After preparing the non-polar polymeric bead "KJM-278-28A" having a porous network structure, and then desorbed sialic acid was concentrated and dried. The analysis of the physicochemical properties of bead "KJM-278-28A" showed that the particle size was 400-700 ㎛, the moisture holding capacity was 67-70%, the surface area (BET) was 705-900 ㎡/g, and the average pore diameter 70-87 Å. The adsorption capacity of the bead "KJM-278-28A" for sialic acid was shown a strong physical force to bind sialic acid to the bead surface of 400 mg/L. In addition, as a result of analyzing the adsorption and desorption effects of sialic acid on water, ethanol, and 10% ethanol on the bead, it was confirmed that desorption effectively occurs from the beads when only ethanol is used. As a result of HPLC measurement of the separated sialic acid solution, a total of four sialic acid peaks of N-acetyl-neuraminic acid (Neu5Ac), α,β-anomer of Neu5Ac and N-glycoly-neuraminic acid were identified. Through these results, it was confirmed that it is possible to separate sialic acid from EBN extract with efficient and high yield when using ABT.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• The Journal of Society for e-Business Studies
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• v.11 no.3
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• pp.1-12
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• 2006

In this study, a value framework for ubiquitous computing is developed and presented. Using 'value focused thinking' approach suggested by Keeney, twenty-two potential users of ubiquitous computing were interviewed and 435 statements were obtained from these interviews. Subsequent purification and redundancy removal process reduces these 435 statements into 166 objectives users have in their mind when thinking about ubiquitous computing. These 166 objectives were again simplified into 37 objectives by clustering. Through a focus group interview, these objectives were again classified into a means-ends network diagram by analyzing reciprocal relationships among them. Resulting means-ends network reveals the value framework inherent within the perception of users. This framework will be useful as a reference in developing new business models in ubiquitous computing as well as developing technologies themselves.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.267-275
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• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

• Journal of Korean Academy of Rural Health Nursing
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• v.18 no.2
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• pp.113-124
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• 2023

Purpose: The number of households vulnerable to isolation increases rapidly as social ties decrease, raising concerns about the associated increase in lonely deaths. This study aimed to identify issues related to lonely deaths by analyzing South Korean news articles; and to provide evidence for their use in preventing and managing lonely deaths via community nursing. Methods: This exploratory study analyzed the structure and trends of meaning of lonely deaths by identifying the association between keywords in news articles and lonely deaths. In this study, we searched for all news articles on lonely deaths, covering the period from January 1, 2010, to May 31, 2023. Data preprocessing and purification were conducted, followed by top-keyword extraction, keyword network analysis and topic modeling. The retrieved articles were analyzed using R and Python software. Results: Four main topics were identified: "discovering and responding to lonely death cases", "lonely deaths ending in lonely funerals", "supportive policies to prevent lonely deaths among of older adults", and "local government activities to prevent lonely deaths and support vulnerable populations." Conclusion: Based on these findings, it can be concluded that lonely death is a complex social phenomenon that can be prevented if society shows concern and care. Education related to lonely deaths should be included in nursing curricula for concrete action plans and professional development.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.