• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.10
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• pp.797-808
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• 2019

In a closed workplace, the management of the workplace is important because the environmental data at the workplace has a great influence on the safety of workers. Today's industrial sites are transformed into data-based factories that collect and analyze data through sensors in those sites, requiring a management system to ensure safety. In general, a safety management system stores and manages data on a central server associated with a database. Since such management system introduces high possibility of forgery and loss of data, workers often suspect the reliability of the information on the management system. In this paper, we present a worker safety management system based on the EOS blockchain which is considered as third-generation blockchain technology. The developed system consists of a set of smart contracts on the EOS blockchain and 3 decentralized applications associated with the blockchain. According to the roles of users, the worker and manager applications respectively perform the process of initiating or terminating tasks as blockchain transactions. The entire transaction history is distributed and stored in all nodes participating in the blockchain network, so forgery and loss of data is practically impossible. The system administrator application assigns the account rights of workers and managers appropriate for performing the functions, and specifies the safety standards of IoT data for ensuring workplace safety. The IoT data received from sensor platforms in workplaces and the information on initiation, termination or approval of tasks assigned to workers, are explicitly stored and managed in the EOS smart contracts.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.611-618
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• 2020

In this paper, we propose a design and implementation method of speed quiz content that can be driven by a social robot capable of interacting with humans, and a method of developing an intelligent module necessary for implementation. In addition, we propose a method of implementing speed quiz content through the process of constructing a map by arranging and connecting intelligent module blocks. Recently, software education has become mandatory and interest in programming is increasing. However, programming is difficult for students without basic knowledge of programming languages to directly access, and interest in block-type programming platforms suitable for beginners is growing. The block-type programming platform used in this paper is a platform that supports immediate and intuitive programming by supporting interactions between humans and robots. In this paper, the intelligent module implemented for the speed quiz content was used by blocking it within a block-type programming platform. In order to implement the scenario of the speed quiz content proposed in this paper, we implement a total of three image-based artificial intelligence modules. In addition to the intelligent module, various functional blocks were placed to implement the speed quiz content. In this paper, we propose a method of designing a speed quiz content scenario and a method of implementing an intelligent module for speed quiz content.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.470-476
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• 2020

In this paper, we present the design of modular replaceable AI server for image deep learning that separates the server from the Edge Device so as to drive the AI block and the method of data transmission and reception. The modular replaceable AI server for image deep learning can reduce the dependency between social robots and edge devices where the robot's platform will be operated to improve drive stability. When a user requests a function from an AI server for interaction with a social robot, modular functions can be used to return only the results. Modular functions in AI servers can be easily maintained and changed by each module by the server manager. Compared to existing server systems, modular replaceable AI servers produce more efficient performance in terms of server maintenance and scale differences in the programs performed. Through this, more diverse image deep learning can be included in robot scenarios that allow human-robot interaction, and more efficient performance can be achieved when applied to AI servers for image deep learning in addition to robot platforms.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.31-40
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• 2021

Blockchain technology is also actively studied in the real estate transaction field, and real estate transactions have various ways. In this paper, we propose a model that simplifies the authentication procedure of auction systems using Ethereum's Ether to solve the problem of offline court auctions. The proposed model is written in Ethereum's Solidity language, the court registers the sale date and the sale date with the DApp browser, and the bidder accesses the address of the individual's wallet created through Metamask's private key. The bidder then selects the desired sale and enters the bid price amount to participate in the auction. The bidder's record of the highest bid price for the sale he wants is written on the Ethereum test network as a smart contract. and creates a block. Finally, smart contracts written on the network are distributed by the court auction manager to all nodes in the blockchain network, and each node in the blockchain network can be viewed and contract verified. As a result of analyzing the smart contracts of the proposed model and the performance of the system, there are fees incurred due to the creation and use of Ether on platforms using Ethereum, and participation. Ether's changes in value affect the price of the sale, resulting in inconsistent fees in smart contracts each time. However, in future work, we issue our own tokens to solve the market volatility problem and commission problem with the value change of Ether, and refine complex court auction systems.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.31-47
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• 2022

While the ship finance industry has long been struggling with diminishing involvements from the private sector, government-run banks have consistently increased their presence in maritime finance. To address such concerns, this research conceptually explores the creation of blockchain technology-driven security token offering (STO) platforms. To suggest a sound platform model, this piece first examines key design principles. Based on the integral perspective on the digital platform, this paper exhibits three core design principles to create a virtuous platform ecosystem, then sets out STO platform design guidelines. This paper further explores an STO platform model by considering conventional ship finance systems and practices in Korea. The STO platform has three main effects; 1) the wider availability of STOs can enlarge both the scope and size of ship finance users, 2) the activation of security token transactions leads to an increase in participation, and 3) possibilities to create complementary innovative financial services can further encourage the participation of private investors. The STO ecosystem may contribute to the shipping, shipbuilding, and ship finance industries by enhancing its attractiveness to the general public and by creating positive externalities for Busan as a maritime finance center.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Journal of Korea Entertainment Industry Association
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• v.15 no.7
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• pp.1-15
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• 2021

With the prolonged COVID-19 pandemic situation, the attention on Metaverse has been soared throughout media and publications. Recently the entertainment industry is also changing to actively utilize the virtual world with artists working through Metaverse platforms. With the recent high interests on Metaverse, this study attempts to illuminate how the public perceives and accepts Metaverse experiences. While research on Metaverse has been mainly focused on specific technologies or technical objects, this study focuses on exploring how fans recognize and understand the concept of Metaverse through newly debuted idol group 'aespa', composing of 4 human members and 4 AI avatar members that integrate real and virtual world. For this, this study seeks to analyze the reaction and recognition of fans by exploring online community 'DC Inside'. As a result of analyzing the reactions of fans in the early days of aespa's debut, the perspectives of understanding AI members of aespa are categorized into three groups: first, the type of equating and accepting the real and the virtual world; second, the type of recognizing the real world and the virtual world separately and recognizing the virtual member as an avatar or concept, not a genuine member; and finally, a type of recognizing virtual worlds separately from reality. Based on these results, the implications that Metaverse contents may have in the entertainment industry are suggested.