• Journal of the Korean earth science society
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• v.43 no.1
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• pp.224-235
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• 2022

This study aimed to confirm the status of media reports on space hazards using topic modeling analysis of media articles that are related to space hazards for the past 12 years. Therefore, Latent Dirichlet Allocation (LDA) analysis was performed by collecting over 1200 space hazards articles between 2010 and 2021 on solar storm, artificial space objects, and natural space objects from BIGKins news platform. The articles related to solar storm focused on three topics: the effect of solar explosion on satellites; effect of solar explosion on radio communication in Korea, centered on the Korean Space Weather Center; and relationship between aircrew and space radiation. The articles related to artificial space objects focused on three topics: the threat of space garbage to satellite and space stations and the transition of useful objects into space junk; the relationship between space garbage and humanity as shown in movies; and the effort of developed countries for tracking, monitoring, and disposing of space garbage. The articles related to natural space objects focused on two topics: International Space Agency's tracking and monitoring of near-Earth asteroids and the countermeasures of collisions, and the evolution and extinction of dinosaurs and mammals, with a focus on the collisions of asteroids or comets. Therefore, this study confirmed that domestic media play a role in conveying dangers of space hazards and arousing the attention of public using a total of eight themes in various fields such as society and culture, and derived education method and policy on space hazards.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.873-879
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• 2022

In the future, AI and the metaverse are becoming so powerful that their application areas and influences are swallowing up the world. The advertising field is no exception, and it is becoming more important to predict, analyze, and strategize these future changes. In order to study the future change of advertising according to the development of artificial intelligence and metaverse, literature research related to the development of artificial intelligence and metaverse technology and the resulting change in the advertising environment, in-depth interviews with future and advertising experts, and Delphi technique research method I want to study change. First, through this study, we would like to examine the opinions of experts through in-depth interviews on the development of artificial intelligence and metaverse technology and the changes in the advertising sector in the post-coronavirus era of civilizational transformation. In addition, the Delphi technique is used to determine how important the change is by future advertising technology area, future advertising media area, future advertising form area, future advertising effect area, future advertising application area, and future advertising process area, and at what point in the future it will change. In addition, we want to study how the future advertising form will change in detail. Also, based on this, we would like to propose a countermeasure for the advertising industry.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.123-136
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• 2021

Teddy bear is a typical rag doll which has been loved by people all over the world for more than 100 years based on its cute and friendly image. In addition, as it has been together for a long time with us, it is considered as a friend of people with memories of all ages and sexes, not just animal doll. Teddy bear has been developing its appearance and character continually playing a role as the symbol of society and issues of an era beyond toys, however it still remains in the image of stuffed toys. Therefore, more advanced teddy bear characters should be created in line with the current environment and market conditions that are undergoing major changes based on the Internet and smart phones. Thus, the concept of the character and the recent development of the market were reflected and the meaning and current image of teddy bears were analyzed to develop new teddy bear stories, worldviews, and characters through design process. And it was created 3D characters, videos, and SNS channels through the developed 3D character design and motion design. Furthermore, we want to take a look at the direction in which Korea's character business can develop in accordance with global changes and suggest the possibility of entering as a character powerhouse.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Journal of Dental Anesthesia and Pain Medicine
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• v.22 no.3
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• pp.205-216
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• 2022

Background: People with special needs tend to require diverse behavioral management in dentistry. They may feel anxious or uncomfortable or may not respond to any communication with the dentists. Patients with medical, physical, or psychological disorders may not cooperate and therefore require sedation (SED) or general anesthesia (GA) to receive dental treatment. Using the healthcare big data in Korea, this study aimed to analyze the trends of SED and GA in special needs patients undergoing dental treatment. It is believed that these data can be used as reference material for hospitals and for preparation of guidelines and related policy decisions of associations or governments for special needs patients in dentistry. Methods: The study used selected health information data provided by the Korean National Health Insurance Service. Patients with a record of use of one of the eight selected drugs used in dental SED between January 2007 and September 2019, those with International Classification of Diseases-10 codes for attention deficit hyperactivity disorder (ADHD), phobia, brain disease, cerebral palsy, epilepsy, genetic disease, autism, mental disorder, mental retardation, and dementia were selected. The insurance claims data were analyzed for age, sex, sedative use, GA, year, and institution. Results: The number of special needs patients who received dental treatment under SED or GA from January 2007 to September 2019 was 116,623. Number of SED cases was 136,018, performed on 69,265 patients, and the number of GA cases was 56,308, implemented on 47,257 patients. In 2007, 3100 special needs patients received dental treatment under SED while in 2018 the number of cases increased 6 times to 18,528 SED cases. In dentistry, ADHD was the most common disability for SED cases while phobia was the most common cause of disability for GA. The male-to-female ratio with respect to SED cases was higher for males (M : F = 64.36% : 35.64%). Conclusion: The application of the SED method and GA for patients with special needs in dentistry is increasing rapidly; thus, preparing guidelines and reinforcing the education and system are necessary.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.569-575
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• 2022

It is most desirable to build a crane training program in the same evvironment as the actual port, but it has problem such as time constraint and cost. To overcome these limitations, next-generation training programs based on AR/VR are receiving a lot of attention. In this paper, a prototype of a harbor crane training system based on virtual reality was designed and implemented. The system implemented in this paper consists of two elements: an Arduino-based IoT terminal and an HMD equipped with a Unity application program. The IoT terminal consists of 2 controllers, 2 toggle switches, and 8 button switches to process data generated according to the user's operation. The HMD uses Oculus Quest2 and is connected to the IoT terminal through wireless communication to provide user convenience. The training system implemented in this paper is expected to provide trainees with a training environment independent of time and place through virtual reality and to save time and money.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.233-260
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• 2022

In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.353-376
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• 2023

The global pandemic and the development of virtual and augmented reality technologies have led a metaverse boom that enables a lot of interactions in virtual worlds, and is being utilized in various fields such as business, government, and education etc. Despite the growing interest in the metaverse, its scope and definition are still unclear and the concept is still evolving, making it challenging to establish its governance. Governmental entities are also investing intensively in public metaverses to make public value and promote social welfare, but they are underutilized due to lack of systematic governance. Therefore, in this study, we propose a public metaverse governance framework and identify the relative importance of the factors. Furthermore, since a public metaverse should be accessible to anyone who wants to use, we explore the factors of shadow work and examine the ways to minimize it. Based on the socio-technical system theory, we derived public metaverse governance factors from previous literature and topic modeling and then generate a framework with 23 factors through expert interviews. We then tested relative priority of the factors using the analytic hierarchical process (AHP) from the experts. As a result, the top five overall rankings are: 'roles and responsibilities', 'standardization/modularization', 'collaboration and communication', 'law and policies', and 'availability/accessibility'. The academic implications of this study are that it provides a comprehensive framework for public metaverse governance, and then the practical implications include suggesting prioritized considerations for metaverse operations in the public sector.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.