• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.113-128
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• 2021

In this study, we aimed to understand the public opinion on COVID-19 vaccine. To achieve the goal, we analyzed COVID-19 vaccine-related Twitter posts. 45,413 tweets posted from March 16, 2020 to March 15, 2021 including COVID-19 vaccine names as keywords were collected. The 12 vaccine names used for data collection included 'Pfizer', 'AstraZeneca', 'Modena', 'Jansen', 'NovaVax', 'Sinopharm', 'SinoVac', 'Sputnik V', 'Bharat', 'KhanSino', 'Chumakov', and 'VECTOR' in the order of the number of collected posts. The collected posts were analyzed manually and automatedly through keyword analysis, sentiment analysis, and topic modeling to understand the opinions for the investigated vaccines. According to the results, there were generally more negative posts about vaccines than positive posts. Anxiety about the aftereffects of vaccination and distrust in the efficacy of vaccines were identified as major negative factors for vaccines. On the contrary, the anticipation for the suppression of the spread of coronavirus following vaccination was identified as a positive social factor for vaccines. Different from previous studies that investigated opinions about COVID-19 vaccines through mass media data such as news articles, this study explores opinions of social media users using keyword analysis, sentiment analysis, and topic modeling. In addition, the results of this study can be used by governmental institutions for making policies to promote vaccination reflecting the social atmosphere.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.60-69
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• 2015

Labor attendance management has traditionally been carried out by writing a table for checking an attendance of labor, which requires a lot of time and effort. As electronic devices with additions such as barcodes, Quick Response codes, and Radio Frequency Identification(RFID) have been developed, however, automated labor attendance management systems have appeared. Recently, various types of labor recognition devices converged with biometrics (fingerprint, vein, face recognition, etc.) have been released. However, although these devices can be used to check attendance automatically, there is insufficient guidance when it comes to selecting the appropriate labor attendance management system for construction sites. Therefore, this study proposed a decision framework to determine which labor attendance management system would be suitable for a construction site and to select the labor recognition device. This study investigated different labor recognition devices, focusing on how they worked, and tested the performance of devices and their usability for construction labor attendance management. The test results showed that RFID is most suitable when verifying the attendance of many laborers over a short period of time. The devices for hand vein and fingerprint recognition did not function when there was a foreign material such as cement or paint on the laborer's hand, except for a deformed finger. Reflecting these test results, this study suggested a framework for selecting a labor attendance system and recognition device; this is expected to contribute to the development of more efficient labor management systems.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.689-699
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• 2022

Recently, high productivity capabilities have been improved due to the application of advanced technologies in various industries, but in the construction industry, productivity improvements have been relatively low. Research on advanced technology for the construction industry is being conducted quickly to overcome the current low productivity. Among advanced technologies, 3D scan technology is widely used for creating 3D digital terrain models at construction sites. In particular, the 3D digital terrain model provides basic data for construction automation processes, such as earthwork machine guidance and control. The quality of the 3D digital terrain model has a lot of influence not only on the performance and acquisition environment of the 3D scanner, but also on the denoising, registration and merging process, which is a preprocessing process for creating a 3D digital terrain model after acquiring terrain scan data. Therefore, it is necessary to improve the terrain scan data processing performance. This study seeks to solve the problem of density inhomogeneity in terrain scan data that arises during the pre-processing step. The study suggests a 'pixel-based point cloud comparison algorithm' and verifies the performance of the algorithm using terrain scan data obtained at an actual earthwork site.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.53-59
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• 2013

Since 1990s, the major recycling methods for mechanical recycling of FRP(Fiber Reinforced Plastics)boats has involved shredding and grinding of the scrap FRP in a new recycled product. But still it leads to secondary problem such as air pollution, unacceptable shredding noise level and few limited applications. This study is to propose a newly advanced method which is more efficient and environment friendly waste FRP regenerating system. As extracting FRP layer and making the recycled fiber for recycled-fiber reinforced concrete(RFRC) from waste FRP, the recycling process has some merits in a sense of the recycling energy and the environmental effects. In this study, for those tasks, spectro-chemical differentiation method and coloring water-soluble dye treatment makes the roving layer more distinguishable photophysically. Also that has remarkably reduced safety hazards and energy. Using the mechanical properties of polymers and composite, FRP with the orthotropic and laminated plastic structure has been easily separated in the new extracting system. Also the new method has introduced five kind of separating manuals for the some different compositions of FRP boats. The roving fiber of laminated glass-fiber layer is as good as the polyvinyl fiber which is cost-high commercial fiber to increasing strength of concrete products. The early study has shown the effectiveness of laminated glass-fiber layer which also is chemical-resistant due to the resin coating. These results imply that more efficient and environment friendly recycled glass fiber can be better applied to the fiber reinforced concrete(FRC) substitute and this study also has shown wide concrete applications with RFRC from the waste FRP boat.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.139-147
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• 2018

This study starts with the consciousness of what should be the public pension service that meets the 4th Industrial Revolution era. To do this, we review the current status of non-facing services of domestic and foreign public institutions, and draw out implications for revitalizing non-facing services of the National Pension over the medium to long term. First, we reviewed the status of non-face-to-face service and the authentication method of the National Pension Service. Next, we reviewed the status of non-facing services in public pension and private agencies in the United States, the United Kingdom, Canada, and Australia. Based on the results of the analysis, we needs to analyze the impact of the 4th Industrial Revolution on the National Pension Service and extract future strategies, to expand channels of non-facing by business, to introduce PinTech as a non-facing authentication method, and to propose a unified service channel through the construction of an internet integrated portal. In the 4th industrial revolution era, it is possible to secure the connectivity of government portal for civil affairs and intelligence and automation introducing artificial intelligence robots.

• Korean Security Journal
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• no.51
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• pp.11-35
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• 2017

The third industrial revolution, characterized by factory automation and informatization, are moving toward the fourth industrial revolution which is the era of superintelligence and supernetworking through rapid technology innovation. The most important resources in the fourth industrial revolution are information or data since the most of industrial and economic activities will be affected by information in the fourth industrial revolution. Therefore we can expect that more information will be utilized, shared and transfered through the networks or systems in real time than before so the significance of information management and security will also increase. As the importance of information resource management and security which is the core of the fourth industrial revolution increases, the threats on information security are also growing so security incidents such as data breeches and accidents take place more often. Various and thorough solutions are highly needed to protect information resources from security risks because information accidents or breaches seriously damage brand image and cause huge financial damage to organization. The purpose of this study is to research general trends on data breaches and accident that can be serious threat of information security. Also, we will provide resonable solutions to protect data from nine attack patterns or other risk factors after figuring out each characteristic of nin attack patterns in data breaches and accidents.

• The Journal of Korean Association of Computer Education
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• v.21 no.3
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• pp.11-20
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• 2018

One major change in the 2015 revised national curriculum is that computational thinking ability is becoming an essential competency for students. Computational thinking is divided into abstraction, automation, and creative convergence in the curriculum for secondary schools' Information subject. And, the curriculum contains problem solving and programming area. Among the components of computational thinking, data representation emphasizes the ability to structure data and information for problem solving of learners. Pre-service teachers of Information subject at secondary schools also learn how to structure information through diagramming. There are differences in the ability to structure diagrams among students, but the studies on learning methods that help students develop their structuring abilities have rarely been performed. The purpose of this paper is to analyze the differences of abstract thinking disposition and gender perspective among college students. As a result, female students had more concrete thinking disposition than male students. Also, there were gender differences according to the characteristics of diagrams. Differences in abstract thinking disposition also made a difference in structuring diagrams. It is useful for achieving the education purpose of improving computational thinking ability by finding out the differences in thinking tendency between males and females and finding the education method that can complement them.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.