• Korea Science and Art Forum
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• v.31
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• pp.21-32
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• 2017

Local festivals form the regional cultures and atmosphere of communication; they increase the demand of domestic tourism businesses and thus, have an important role in ripple effects (e.g. regional image improvement, tourist influx, job creation, regional contents development, and local product sales) and economic revitalization. IoT (Internet of Thing) technologies have been developed especially, beacon-one of the IoT services has been applied as plenty of types and forms both domestically and internationally. However, notwithstanding expansion of current digital mobile technologies, it still remains as difficult for the individual to track the information about all the local festivals and to fulfill the tourists' needs of enjoying festivals given the weak strategic approaches and advertisement activities. Furthermore, current festival-related mobile applications don't function well as delivering information and have numerous contents issues (e.g. ways of information delivery within the festival places, independent application usage for each festival, one time usage due to one time event). This research, based on the background mentioned above, aims to develop the local festival mobile application and data analysis system applying beacon technology. First of all, three algorithms were developed, namely, 'festival crowding algorithm', 'visitor stats algorithm', and 'customized information algorithm', and then beta test was followed with the developed application and data analysis system. As a result, they could form the database of visitors' types and behaviors, and provide functions and services, such as personalized information, waiting time for festival contents, and 'hot place' function. Besides, in Google Play store, they also got the titles given with more than 13,000 downloads within first three months and as the most exposed application related with festivals; and, thus, got credited with their marketability and excellence. This research follows this order: chapter 2 shows the literature review of local festival related with technology development, beacon service, and festival application. In Chapter 3, design plans and conditions are described of developing local festival mobile application and data analysis system with beacon. Chapter 4 evaluates the results of the beta performance test to verify applicability of the developed application and data analysis system, and lastly, chapter 5 explains the conclusion and suggests the future research.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.2
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• pp.147-157
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• 2023

Centella asiatica extract is widely used as a raw material for cosmetics due to its various effects, but it is difficult to expect penetration into the skin due to its high molecular weight and low solubility. In order to solve these problems, lipid-based liposomes of various types were developed to increase skin absorption. Therefore, in this study, we tried to increase the skin absorption rate by preparing transfersomes using surfactants as edge activators in existing liposomes. Liposome and transfersomes containing Span 80 and Tween 20, 60, 80, and 85, respectively, were prepared using a high-pressure homogenizer, and we evaluated the particle size, polydispersity index, zeta potential, and skin absorption rate. As a result, there was almost no change in the physical properties of particle size, polydispersity index and zeta potential from 25 ℃ to 60 d, and the particle size of transfersomes containing Tween 20, 60, and 80 increased after 60 d at 45 ℃. Madecassoside, main substances of the Centella asiatica extract was used as an standard and madecassoside was measured and calculated when measuring the skin absorption rate using Franz diffusion cells. As a result, formulations containing Tween 20 were the most, whereas formulations containing Span 80 were the least. According to the skin absorption coefficient (Kp) value, all formulations showed 'very fast', and the absorption rate was similar or greater than that of liposomes, except for formulations containing Span 80. Through this, it was confirmed that the larger the HLB value of the nonionic surfactant, the smaller the particle size of the transfersome, and the increased skin absorption rate due to the increased flexibility of the vesicle membrane. Through this study, transfersome using surfactant as an edge activator can be expected to solve local skin problems not only as a cosmetic raw material or product, but also by increasing skin absorption.

• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.

• Journal of the Korean Society of Food Culture
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• v.23 no.2
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• pp.222-227
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• 2008

This study examined the chemical composition of lotus root and functionally evaluated a fermented lotus root drink. Electron-donating ability using DPPH along with nitrite-scavenging ability were used to compare the antioxidative activities of unfermented and fermented lotus root drinks. The electron-donating abilities of the unfermented lotus root drink (1%) and fermented lotusroot drink (1%) were 22.55% and 23.88%, respectively. At pH 6.0, the nitrite-scavenging abilities of the unfermented lotus root drink and the fermented lotus root drink (100%) were 27.64% and 40.3%, respectively, and their scavenging ability increased in a dose-dependent manner at all pH values. In order to study the anti-obesity effects of the two drinks, male Sprague-Dawley rats were divided into four groups (A: basal diet, B: high fat diet, C: high fat diet+unfermented lotus root drink, D: high fat diet+fermented lotus root drink). Net weight gains were not significantly different among the four groups. Plasma total cholesterol concentrations significantly decreased in the groups receiving the unfermented and fermented lotus root drinks. Also, plasma total lipid and triglyceride contents were lower in the groups receiving the unfermented and fermented lotus root drinks as compared to the high fat diet group; however, the differences among the three groups were not significant.

• Korean Journal of Culture and Social Issue
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• v.23 no.2
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• pp.131-157
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• 2017

Loving-kindness & compassion meditation (LCM) is one of the popular intervention on clinical setting to cultivate loving-kindness & compassion for self and other. Shame is known for unconscious and implicit emotion including negative self-concept. Some researchers suggest that people with high shame have difficulty in cultivating loving-kindness & compassion toward oneself because of shame including the negative self-critic. In this research, it is explored the influence of shame for the object of LCM. There are 2 experiments to find out the answer to this question. In experiment 1, participants (N = 108) are randomly assigned to two conditions. The one condition is loving-kindness meditation for self, and the other is loving-kindness meditation for positive others. Dislike and engagement from participants about loving-kindness meditation instruction are measured after meditation for 8 minutes. From the experiment 2, loving-kindness meditation is replaced with compassion meditation from the design of experiment 1. This experiment is conducted because of psychological differences between loving-kindness and compassion. Participants (N = 116) are randomly assigned to two conditions, compassion meditation for self and positive others, respectively. The results of experiment 1 show that dislike of loving-kindness meditation for self seems to high when people have high shame, but shame does not have an influence on engagement. For loving-kindness meditation for positive others, shame seems to not affect on dislike and engagement about loving-kindness meditation instruction. The results of experiment 2 show that dislike is higher for self than for positive others about compassion meditation for people with high shame, but shame does not affect on engagement. For discussion, it is suggested that shame has special features of emotion. For the future, we discuss the therapeutic strategy for people with negative self-concept.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.4
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• pp.313-321
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• 2023

Platycodon grandiflorus (P. grandiflorus) flower is a perennial plant belonging to the family Campanulaceae and has many excellent pharmacological effects, so it has been used as a medicinal ingredient since ancient times. In addition, anthocyanin is a purple or blue natural pigment contained in plant flowers and fruits, and is known as a powerful antioxidant. The purpose of this study was to confirm the dermatological functionality of P. grandiflorus flower extract and the value of the bluish anthocyanin contained in flowers as a cosmetic material as a natural pigment. Firstly, 50% ethanol and 80% ethanol were added to the P. grandiflorus flower and extracted under reflux for 4 h at 25, 60, and 80 ℃, and the pH of each treatment group was similar. Based on the anthocyanin content and chromaticity (E^*ab), 50% ethanol 60 ℃ extraction conditions showing the color development most similar to the natural color of the P. grandifloras flower were selected, and a sample was prepared by concentrating and lyophilizing. The analysis results showed that the total phenol, total flavonoid, and total anthocyanin contents were in the ranges of 23 ㎍/mL, 16 ㎍/mL, and 0.17 ㎍/mL, respectively. The P. grandiflorus flower extract suppressed the production of nitric oxide (NO) and interleukin-6 (IL-6) in lipopolysaccharide (LPS) induced RAW264.7 cells. Furthermore, the P. grandiflorus flower extract showed wound healing effects through the promotion of skin cell migration in TNF-α stimulated human keratinocytes. The stability of anthocyanin and extract color was studied during a storage period of 50 days at various temperatures (4 ℃, 25 ℃, and 45 ℃). Color values (L, a, and b) of the P. grandiflorus flower extract changed over 50 days, whereas the bluish-purple color of the extract was stabilized using 5% maltodextrin. These results suggest that P. grandiflorus flower extract may be useful as a natural cosmetic pigment.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.15-23
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• 2023

In a situation where the necessity and importance of ESG management is increasing recently, it is judged that selecting important ESG-related variables for container terminals, which are the bases of export and import logistics, among various variables of ESG evaluation agencies will help to establish ESG management strategies for container terminals which led us to proceed with this study. The results of word network analysis are summarized as follows. The weighed degree, that is, the AWD of Environmental management(E) variables, is obtained in the order of Environmental Protection Investment(54), Environmental Awareness Education(45), Work Team Structure(31), Environmental certification(32). Page Ranks, the order of centrality and connectivity index is Environmental Awareness Education(0.0765), Employee Engagement(0.0765), Environmental Protection Investment(0.0761), Work Team Composition(0.0761), and Environmental certification(0.0761). The AWD(Average Weighed Degree) of the Social Responsibility Management(S) variables, followed by Protecting workers' human rights and contributing to local communities(68), Safety Education(63), Safety certification(59), and Responding to infectious diseases(40). Orders by Page Ranks, centrality and connectivity Index, are Protecting workers' human rights and contributing to local communities(0.165), Safety Education(0.153), Safety Certification(0.144) and Responding to infectious diseases(0.102). The AWD of Governance and Ethical management(G) variables, followed by Anti-corruption(27), Transparent management(24), Mutual cooperation between stakeholders(19), and Sustainability reporting(9). Page Ranks, the order of centrality and connectivity index is the Anti Corruption(0.241), Transparent management(0.216), Mutual cooperation between stakeholders(0.174), Directors' roles and responsibilities(0.105), Shareholder protection(0.097) and Sustainability Report(0.096).

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.