• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1483-1498
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• 2022

While public interests in the metaverse are growing recently in the Korean media and research, its understanding has not been fully established yet. In this study, we aimed to probe whether the rapid growth in media attention about the metaverse has increased its usage as a buzzword accompanied by an absence of scientific context. We analyzed publications and online news containing "metaverse" from 2020 to 2022. The data analysis methods are 1) time series frequency, 2) keyword network, 3) natural language model. The findings indicate the perception gap about metaverse between research and news articles broadened as its popularity has grown. Research about metaverse gradually expanded its connections with related topics-virtual and augmented realities-focusing on social changes in a remote environment. However, media reporting frequently used "metaverse" as a buzzword rather than explaining its scientific background, stimulating the proliferation of related topics and the dispersion of news content. This study further discusses the need for a media strategy to improve public conception of the long-term development of the metaverse.

• Journal of Public Diplomacy
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• v.2 no.1
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• pp.69-96
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• 2022

How does a public health crisis play into the digital rhetoric of states? As Covid-19 is presenting a situation in which countries need to manage the international environment in a relatively short period, their practices could signal how digitization is going to influence public diplomacy in the longer run. This paper explores state public diplomacy in the context of a public health crisis. It develops a theoretical framework of public diplomacy on social media through how and what states communicated during the first year of the Covid-19 pandemic. Through keyword and hashtag analyses, we identify two patterns. First, states usually regard social media as an instrument for domestic communication rather than public diplomacy. The international impact of messaging has not been prioritized or well-recognized. Social media platforms such as Twitter have global outreach and messaging can be seen by audiences all over the world. Messages intended for the domestic audience could have an international impact. Thus, any communication on digital platforms should consider their public diplomacy outcomes. Second, while social media platforms are claimed to be for networking at different levels, states tend to connect with other states rather than with international organizations during the pandemic. States do not like to mention international organizations like the WHO and the UN on Twitter. Instead, they were either busy dealing with internal problems or cooperating with another state to combat the virus.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.91-113
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• 2024

In the wake of the COVID-19 pandemic, unsubstantiated information concerning vaccines and the coronavirus has proliferated on various social media platforms. Consequently, we have considered viable actions to mitigate the impact of such unverified content, enabling individuals to use social media platforms more effectively and minimize any ensuing confusion. Recent measures in this area have included YouTube's practice of labeling vaccine or corona videos as authoritative when emanating from reputable organizations and Twitter's practice of flagging vaccine-related content as potentially misleading or taken out of context. This study seeks to explore how such contrasting labeling practices influence users' believability and engagement differentially, while also examining the moderating impact of regulatory focus. The results indicate that authoritative labeling positively influenced users' believability and engagement, whereas misleading labeling adversely affected users' believability and engagement. Additionally, our findings revealed that authoritative labeling has a stronger impact on promotion-focused individuals, while misleading labeling has a more pronounced effect on prevention-focused individuals. Our findings offer insights into how social media platforms can design and present information to their users, taking into account their regulatory focus.

• Structural Engineering and Mechanics
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• v.73 no.6
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• pp.725-735
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• 2020

The present investigation deals with the thermomechanical interactions in an orthotropic thermoelastic homogeneous body in the context of fractional order theory of thermoelasticity due to time harmonic sources. The application of a time harmonic concentrated and distributed sources has been considered to show the utility of the solution obtained. Assuming the disturbances to be harmonically time dependent, the expressions for displacement components, stress components and temperature change are derived in frequency domain. Numerical inversion technique has been used to determine the results in physical domain. The effect of frequency on various components has been depicted through graphs.

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.183-187
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• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.51-52
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• 2013

Recent advances in WSN and the wide use of social sensing technologies have been changing our daily lives. In the process of creating intertwining connections and interconnections greatly influencing on the way we communicate with other people, WSN and social communication media have a number of important capabilities that support their utilization in distress broadcast during emergency situations. This paper proposes a system model that makes better utilization of WSN and social sensing capabilities in sending out distress messages to the intended recipients more efficiently and effectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.97.1-102
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• 1999

This paper porposes and efficient coding scheme for remote medical communication systems, or“telemedicine systems”. These systems require a technique which is able to transfer large volume of data such as X-ray images effectively. We have already developed a hierarchical image coding and transmission scheme (HITS), which achieves an efficient transmission of medical images simply[1]. In this paper, a new coding scheme for HITS is proposed, which used hierarchical context modeling for the purpose of improving the coding efficiency. The hierarchical context modeling divides wavelet coefficients into several sets by the value of a correspondent coefficient in their higher class, or“a parent”, optimizes a Golomb-Rice (GR) code parameter in each set, and then encodes the coefficients with the parameter. Computer simulation shows that the proposed scheme is effective with simple implementation. This is due to fact that a wavelet coefficient has dependence on its parent. As a result, high speed data transmission is achieved even if the telemedicine system consists of simple personal computers.

• Fashion & Textile Research Journal
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• v.21 no.4
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• pp.432-451
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• 2019

Companies and brands that practice sustainability pay attention to New Media due to its ability to build a sustainable relationship between companies and consumers. The need for research on specific roles, characteristics, and social media effects on eco-friendly advertising has had rapid growth in marketing programs for sustainable activities especially shown through social media. Information about sustainable fashion has spread to consumers through social media, and multifarious efforts have been made to attract the attention of youth. Despite the dramatic increase in eco-friendly marketing through social media as a part of sustainability, there is a lack of research on the major influences of emotional factors such as ad color and expression in social media. In this context, it is meaningful to identify relationships between emotional responses, advertising value and consumer behavior of sustainable fashion brands in Instagram and implement a suitable advertising type (color vs expression) for consumers. We used 366 responses for the final analysis. Data were analyzed by factor analysis, structural equation modeling using SPSS 18.0 and AMOS 18.0. The results of this study suggest that emotional responses, advertising value have a significant effect on the flow. This study expands on a previously limited research field by verifying consumer responses to image advertising on Instagram, rather than general sustainable fashion marketing. The study results also provide meaningful implications for a relation formation between customers and fashion brands vis-${\grave{a}}$-vis sustainable social media marketing.

• Nutrition Research and Practice
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• v.15 no.sup1
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• pp.110-121
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• 2021

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.