• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.3-14
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• 2021

In the Chinese construction industry, the utilization of Building Information Modeling (BIM) aims to increase the total output of the construction industry by solving the problem of inefficient interoperability in the construction industry. In 2011, the Chinese Ministry of Housing and Urban-Rural Development despite the technical advantages of BIM and the government policy, the BIM adoption rate in China is lower than 45%. Meanwhile, as the South Korean construction industry is a step ahead of its Chinese counterpart in introducing and utilizing BIM, it is expected that BIM is more actively utilized and accepted in South Korea than in China. According to a comparative study based on the hype-cycle theory, South Korea is at a more advanced stage of introducing BIM, than in China. This study aimed to suggest how to increase BIM utilization rates in China. To this end, this study comparatively analyzed factors affecting BIM acceptance between China and South Korea. For the comparative analysis of the BIM acceptance factors between China and South Korea, literature reviews on the technology acceptance model (TAM) and BIM acceptance model were carried out, and based on that, the BIM acceptance factors were classified. Other BIM acceptance factors were also added and considered, as they reflected Chinese national characteristics and construction industry. As for the derived BIM acceptance factors, construction project participants, especially actual BIM users in China and South Korea, were targeted for the survey. A t-test using SPSS 22.00 was carried out to identify significant differences in data. Finally, based on the t-test results, this study suggested ways of improving the BIM utilization rate in China. Based on the findings, this study is expected to contribute to activating BIM adoption in the Chinese construction industry and also to set a theoretical foundation for future studies on BIM utilization in the industry.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.125-133
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• 2021

This study investigated to explore the broadened concept of the subscription service market and categorize of the subscription market and its consumer behavior. We examined the satisfaction of the service users and the reasons for terminating the subscription. Survey respondents were 443 people in their 20s and 30s, who actively use subscription services. As a result of the survey it was found that users in their 20s were more satisfied with the overall subscription service than those in their 30s, and that user's residential areas were evenly distributed regardless of metropolitan area or non-metropolitan area. As a reason for the cancellation of subscription service: the lower the novelty of subscription, the less personalization tailored to consumer, the lack of feeling self-growth while using the service, and the more termination is made. Our findings have magnified the understanding of consumers behaviors in the age of 20s and 30s of using and terminating subscription service and hopefully be used for future studies of subscription services.

• The Korean Journal of Archival Studies
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• no.28
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• pp.123-153
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• 2011

In Asia, when cotton and cotton fabrics cultivated and produced in India of Southern Asia had spread to the whole Asia area by land and by sea, the Cotton Road and cotton fabric cultural area could be formed. In Korea, the traditional cotton (Gossypium arboreum) brought by Moon Ik-Jeom in 1363 was cultivated and then the Upland cotton (Gossypium hirsutum) brought via Japan could be produced from 1904. Especially, Gwangju/Jeonnam was the most active place in producing traditional cotton, and eventually became the center of cotton cultivation and fabric production after bringing in Upland cotton. In order to collect and record the cotton cultural resources in the broad area, the Cultural Resources Set, classified its component parts should be made first and then the collecting objects should be investigated. The collecting areas are selected based on the spreading paths and the regional significance of cotton. Since its difficulty of collecting the relevant resources from all of the places in Asia, it should be planned to share the resources through exchanges and cooperation among private, institution and organization. The relevant experts from the various fields should participate in the interdisciplinary researches which are necessary for collecting and recording of wide area cultural resources. Considering the collecting limitation of genuine relics, the digital archives should be established and then offered through a web site that everyone can use them freely by remote. It also needs to plan to display on and off-line for users to perceive the similarity, difference and interconnections of the resources with ease.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.278-280
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• 2019

Flow cytometer is one of the instrument that can measure various optical properties of a single cell or microparticle. These parameters including size, granularity, and fluorescence intensity are determined by the physical and optical interaction of the cells with excitation light source. However, users have some difficulties such as high cost, size of instrument, and limited fluorescence selectivity. In addition, abundant data is also unintentionally acquired even though user wants to have a single optical parameter. For these reasons, the use of flow cytometer is more challenging for researchers to apply their study. Therefore, the proposed study aims to develop a low-cost portable fluorescence acquisition system using a commercially available light-emitting diode and photodiode. It is designed by a 3D printer, and fluorescence selectivities are increased by changing of the light source / optical filter / detection sensor. Various number sets of fluorescently labeled cells were measured, and its feasibility was evaluated through the proposed system. As a result, acquried fluorescence intensities were proportional to the concentration of the cells and showed high linearity.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.61-72
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• 2020

Social problems, such as economic instability, aging population, heightened competition, and changes in personal values, might become more serious in the near future. Affective computing has received much attention in the scholarly community as a possible solution to potential social problems. Accordingly, we examined domestic and global knowledge structure, major keywords, current research status, international research collaboration, and network for each major keyword, focusing on keywords related to affective computing. We searched for articles on a specialized academic database (Scopus) using major keywords and carried out bibliometric and network analyses. We found that China and the United States (U.S.) have been active in producing knowledge on affective computing, whereas South Korea lags well behind at around 10%. Major keywords surrounding affective computing include computing, processing, affective analysis, research, user modeling categorizing recognitions, and psychological analysis. In terms of international research collaboration structure, China and the U.S. form the largest cluster, whereas other countries like the United Kingdom, Germany, Switzerland, Spain, and Canada have been strong collaborators as well. Contrastingly, South Korea's research has not been diverse and has not been very successful in producing research outcomes. For the advancement of affective computing research in South Korea, the present study suggests strengthening international collaboration with major countries, including the U.S. and China and diversifying its research partners.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.19-26
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• 2021

The purpose of this study is to provide an environment that can support the development of the virtual asset industry. The limiting factors and countermeasures currently possessed by the virtual asset industry were considered in terms of legal and institutional aspects, technical aspects, and market aspects.Small businesses classified as virtual asset operators have difficulty meeting the government's requirements.Accordingly, SMEs with insufficient funds and manpower are withdrawn from the market, creating an environment where only large-scale enterprises with capital power survive.It is difficult to develop desirable technologies and markets in the virtual asset technology industry. In addition, small and medium-sized companies may be expelled from the market, causing damage to current users. Therefore, in terms of legal and institutional aspects, there is a lack of an exact scope of virtual asset providers, and thus it is necessary to respond to the controversial elements of virtual asset providers. In terms of technology, it is necessary to cope with the slowdown of the P2P method, the difficulty in recovering errors, and the absence of operational experts. Therefore, technology standardization and stabilization are required, and efforts must be made to cultivate operational technical personnel who can support them.In terms of the market, it is necessary to prepare measures to protect users of virtual assets and to establish countermeasures for companies operating virtual assets against weak user protection, inadequate application of the AML method, and limitations of taxation. This study is expected to contribute to active utilization support or related policies in the virtual asset industry.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.131-142
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• 2009

While users of information security products require high-quality products that are secure and have high performance, there are neither examples for evaluating the quality of information security products nor studies on the quality model and metrics for the quality evaluation. In this paper, information security products are categorized into three different types and the security and performance of various information security products are analyzed. Through this process and after consideration of information security products' security and performance, a new quality model that possesses 7 characteristics and 24 sub-characteristics has been defined. In addition, metrics consisting of 62 common and 45 extended metrics that can be used to evaluate the quality of information security products are introduced, and a proposition for a method of generating the quality evaluation metrics for specific information security products is included. The method of generating metrics proposed in this paper can be extended in order to be applied to a variety of information security products, and by generating and verifying the quality evaluation metrics for firewall, intrusion detection systems and fingerprint systems it is shown that it applicable on a variety of information security products.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.