• Journal of Information Technology Services
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• v.20 no.5
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• pp.89-104
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• 2021

Food delivery service combined with IT technology and HMR (Home Meal Replacement) are rapidly growing due to the COVID-19. Recently, the demand for salads along with HMR has increased among office workers in their 20s and 30s who are interested in health and beauty. Freshcode is a food startup with 6 years of experience that started selling salad products through O2O service. Freshcode applied for a patent for a service that collects orders from nearby areas and delivers them on the same day to a designated delivery address 'FCOSPOT' to save shipping costs. In March 2021, in recognition of the growth potential of the regular delivery service, Freshcode received an investment of 6 billion won in Series A. This study may have practical implications to early-stage startups and scale-up stage startups through a longitudinal case study on the growth of a single company. As for the research method, the lean startup methodology and lean canvas were used in the early stage of startup. In particular, the process of the build-measure and learn feedback-loop, which is the core of lean startup methodology, was applied to each major decision-making step. In the scale-up stage after 5 years, the business model canvas was used to schematize the growth as a food online O2O platform to verify continuous innovation. This case study has three main findings. First, the idea of 'FCOSPOT' was successfully implemented through the Lean Startup methodology. Second, Freshcode demonstrated the scalability of the differentiated business model of shared base delivery O2O. Third, a key factor of success was the digital integrated communication operation strategy that maximizes the experience for the created customers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.2
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• pp.93-110
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• 2022

The purpose of this study is to explore business investment and operation of O2O (Online-To-Offline) combined service. The study will analyze the necessary factors for growing the business by dividing the O2O service by industry. The Online-to-Offline is a method of inducing purchases of products and services by connecting between online and offline This research methodology organized the four stages of the analysis process. The analysis of all stages was performed with association rules in big data techniques. It is divided into the start-up period, growth period, maturity period, and decline period, and analysis is conducted on the business investment, expenditure cost, business operation, and conflict factors. As the research result, the first analysis has shown commonality with government subsidies, bank loans, and personal funds in all industries. The second analysis showed a lot of expenditure on labor costs of internal employees, marketing/sales, facility facilities, equipment, and equipment purchase costs. The third analysis showed difficulty in raising the investment resources necessary for business operations in all industries. The last analysis showed conflicts in the industry, businesses license, legal systems, and small business owners in all industries. This study contributed to the abundance and diversity of research methodologies in management information systems using association rules. In addition, the description of organizational development theory was updated while explaining the business investment and operation of O2O combined services. In practical implication, the O2O services include environmental factors that cause convergence between industries. Accordingly, this is required for new O2O services through new laws and systems and reorganization of existing laws and regulations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.565-573
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• 2022

Current mobile communication system is in the mid-process of conversion from 4G LTE to 5G network. According to the generalization of mobile communication services, personal information such as user's identifiers and location information is transmitted through a mobile communication network. The importance of security technology is growing according to the characteristics of wireless mobile communication networks, the use of wireless shared channels is inevitable, and security technology cannot be applied to all network system elements in order to satisfy the bandwidth and speed requirements. In particular, for security threat analysis, researches are being conducted on various attack types and vulnerability analysis through rogue base stations or attacker UE to make user services impossible in the case of 5G networks. In this paper, we established a 5G network testbed using open sources. And we analyzed three security vulnerabilities related to NAS COUNT and confirmed the validity of two vulnerabilities based on the testbed or analyzing the 3GPP standard.

• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.397-404
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• 2021

The aim of this study is to explore the educational experience of nursing students after designing an action learning class suitable for basic nursing science class and applying it. A total 100 freshmen nursing students taking a basic nursing science class of K university in S city participated in this study. Data was collected from May 2019 to June 2020. The action learning class consisted of 5-6 people per team, a total of 9 teams, reflection diaries were collected and analyzed using the qualitative content analysis method of Krippendorff (2004). The analysis produced 45 significant statements in total, 8 themes and 4 categries for the experience of basic nursing science class based on action learning. The 4 categories were 'confidence in anatomy', 'growing teamwork', 'learned how to study', 'difficulties in the process'. The action learning applied class was found to be effective in problem-solving ability, teamwork, and self-directed learning. Therefore, it is proposed to evaluate the effect of action learning in other nursing subjects as well.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Textile Coloration and Finishing
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• v.33 no.4
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• pp.202-209
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• 2021

Natural dyes are more expensive than synthetic dyes and the dyeing process, which is mainly immersion of dye, is complicated. For this reason, relatively small-scale production methods were predominant. However, awareness and interest in environmental sustainability is rising globally, and the use of synthetic dyes causes various environmental problems such as wastewater and CO₂ emission, so the consumption of natural dyes is increasing. In addition, interest in digital textile printing, an eco-friendly dyeing method that can produce products of various designs and uses less water, is growing. In this study, natural indigo dye (Indigofera tinctoria) was used as a raw material for Digital Textile Printing ink, and ¹⁴C (Biocarbon) present in it was measured to confirm whether it was derived from natural ingredients. The performance was confirmed by testing the pH, viscosity, electrical conductivity, surface tension, and particle size analysis of natural indigo ink. In addition, the performance of natural indigo DTP ink and printing fabric was evaluated by inspecting the change in color fastness and corresponding index substances before and after digital printing with natural indigo DTP ink on textiles. Through this, the possibility of commercialization of DTP ink and printing fabric using natural indigo was confirmed.

• Journal of Fashion Business
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• v.25 no.5
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• pp.114-130
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• 2021

The purpose of this study is to analysis BTS's image based on Peirce's semiotics. Methodologies of this study are literature study to analyze the structure of idol image based on Peirce's theory of signs, case study and FGI analyzing the semiotic characteristics of BTS images. The research results of analyzing the semiotic features of BTS images by period of the BTS album are as follows. First, in the early days of their debut, they emphasized the image of hip-hop and expressed their will to resist and rebel against the older generation with a challenging and strong image. Second, during the 'The most beautiful day of life' period, image of wandering, rebellion, growth, and youth of teenagers was expressed. Third, in the 'Love Yourself' period, BTS showed various image changes between natural and pure image to splendid image by expressing the process of finding confidence during chaotic moment of growing. Fourth, during the 'Map of the Soul' period, the exploration and reflection on themselves were expressed in an outwardly splendid and bright manner, while at the same time expressing the inner darkness in a contrasting manner. Fifth, in the 'pandemic period', they expressed hopeful energy and willness with the image of mature and attractive man and bright and casual image. Their growth and change have been directly linked to the change of their image, and their image showed a successful signification with complementary combination of icons, indexes, and symbols.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.583-589
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• 2022

Due to COVID-19, which has thrown the whole world into chaos, it has become an era where many technologies and contents are made non-face-to-face. At the same time, the popularity of the metaverse service is also increasing day by day, and the virtual costume (avatar) industry, one of the sub-industries, is also growing. In this study, we designed and developed a system for creating virtual costumes to be uploaded to ZEPETO, a mobile metaverse service. Unlike the existing service that requires a program that operates in a PC environment, it can be produced only by shooting and simple operation through a mobile device. With the advantage of being able to process all tasks of this system in a mobile environment, small businesses and individual operators who are not familiar with external programs will be able to more easily access the 3D virtual clothing industry.