• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.1-10
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• 2023

This paper proposes a method to extend Inter-Carrier Interference (ICI) canceling Orthogonal Frequency Division Multiplexing (OFDM) receivers for 5G mobile systems to spatial multiplexing 2×2 MIMO (Multiple Input Multiple Output) systems to support high-speed ground transportation services by linear motor cars traveling at 500 km/h. In Japan, linear-motor high-speed ground transportation service is scheduled to begin in 2027. To expand the coverage area of base stations, 5G mobile systems in high-speed moving trains will have multiple base station antennas transmitting the same downlink (DL) signal, forming an expanded cell size along the train rails. 5G terminals in a fast-moving train can cause the forward and backward antenna signals to be Doppler-shifted in opposite directions, so the receiver in the train may have trouble estimating the exact channel transfer function (CTF) for demodulation. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceller is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number n to receiver sub-carrier number l is generated. In case of n≠l, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 8 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, 2×2MIMO QPSK and 16QAM modulation schemes, BER (Bit Error Rate) improvement was observed when the number of taps in the multi-tap equalizer was set to 31 or more taps, at a moving speed of 500 km/h and in an 8-pass reverse doppler shift environment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.1-15
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• 2023

Impact investment involves investing in companies that pursue both social value and financial returns. It focuses on addressing various social problems through innovative solutions while generating profits. The domestic impact investment ecosystem has experienced significant growth with the support of the government and public institutions. In 2021, it witnessed a 3.5-fold increase over three years, reaching a total of 700 billion won in operating assets. In order to foster qualitative growth alongside this quantitative expansion, it is crucial to conduct research specifically on impact investment, which sets it apart from conventional venture investment. This study aims to empirically analyze the unique factors that influence impact investment decisions. Firstly, the factors affecting investment decisions were identified through a literature analysis. Then, a consultation and Delphi survey involving 11 representatives and evaluators from impact investment companies was conducted to determine the major investment determinants. Subsequently, an AHP (Analytic Hierarchy Process) survey was carried out with 10 impact investment evaluators to ascertain the relative importance of these factors. The analysis revealed the following order of importance for the top factors: market>entrepreneur(team)>product/service>finance. Furthermore, the importance of specific factors was identified in the following order: market competition and entry barriers>new market creation>market growth and potential expansion>team expertise and capabilities. Unlike previous studies that primarily focus on general startup investment factors, this research demonstrates that impact investment places greater emphasis on market-related factors and considers the sustainability and profitability of the business model to be more important than the social impact of social ventures.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.1
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• pp.205-230
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• 2024

Recently, as user demand to receive various information through one integrated institution has increased, "Larchiveum," which integrates the functions and services of archives, libraries, and museums, has been established. Thus, web information services are provided in an integrated manner through the Larchiveum website. This study attempted to analyze the information services on the Larchiveum website in detail. To this end, the researchers developed a web information service evaluation index reflecting the characteristics of Larchiveum that are differentiated from information services offered by websites of general archives, libraries, and museums. Recognizing the importance of evaluation indicators, the researchers developed evaluation indicators, and an evaluation of the three institutions' websites was conducted. The assessment showed that the currently operating Larchiveum website provides ample basic business introduction and interface navigation, but the use of search results in the information search area was insufficient. Complementary points were presented in these areas, and measures that would be effective if additionally operated were also suggested. This research sought to provide practical assistance in configuring and providing web services for the newly established Larchiveum in hopes that the evaluation indicators used in this study will be applied, supplemented, and utilized well in the future.

• Information Systems Review
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• v.22 no.2
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• pp.25-41
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• 2020

Along with the expectation about 5G network commercialization, interests in realistic and immersive media industries such as virtual reality (VR) are increasing. However, most of studies on VR still focus on video technologies instead of factors for popularity and consumption. Thus, the main objective of this research is to identify meaningful factors, which affect the view counts of VR videos and to provide business implications of the content strategies for VR video creators and service providers. Using a regression analysis with 700 VR videos, this study tries to find major factors that affect the view counts of VR videos. As a result, user assessment factors such as number of likes and sicknesses have a strong influence on the view counts. In addition, the result shows that both general information factors (video length and age) and content characteristic factors (series, one source multi use (OSMU), and category) are all influential factors. The findings suggest that it is necessary to support recommendation and curation based on user assessments for increasing popularity and diffusion of VR video streaming.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.146-154
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• 2024

The ship management industry in South Korea has been growing steadily, leading the government to implement policies to support its development in response to changing environmental conditions. These policies aim to improve the competitiveness of South Korea's ship management industry by recognizing the importance of skilled professionals in determining its success. Plans and policies have been put in place to cultivate these professionals, but ship management companies are currently facing a serious shortage of manpower. To enhance the industry's competitiveness, it is essential to attract and retain competent ship management professionals. Therefore, this study investigates the reasons for turnover among these professionals. The research results identified four factors contributing to turnover: Work Environment, Economic Compensation and Welfare Benefits, Self-Development, and Promotion and Career Advancement. Subsequent multiple regression analysis based on these factors revealed the need to strengthen economic rewards and benefits in order to reduce turnover rates among ship management professionals. This study provides foundational data for the development of stable human resource management policies for the future of the ship management industry.

• Information Systems Review
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• v.21 no.4
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• pp.125-141
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• 2019

With the advent of deep learning technology, which is represented by AlphaGo, artificial intelligence (A.I.) has quickly emerged as a key theme of digital transformation to secure competitive advantage for businesses. In order to understand the trends of A.I. based digital transformation, a clear comprehension of the A.I. business ecosystem should precede. Therefore, this study analyzed the A.I. business ecosystem from the multi-actor network perspective and identified the A.I. platform strategy type. Within internal three layers of A.I. business ecosystem (infrastructure & hardware, software & application, service & data layers), this study identified four types of A.I. platform strategy (Tech. vertical × Biz. horizontal, Tech. vertical × Biz. vertical, Tech. horizontal × Biz. horizontal, Tech. horizontal × Biz. vertical). Then, outside of A.I. platform, this study presented five actors (users, investors, policy makers, consortiums & innovators, CSOs/NGOs) and their roles to support sustainable A.I. business ecosystem in symbiosis with human. This study identified A.I. business ecosystem framework and platform strategy type. The roles of government and academia to create a sustainable A.I. business ecosystem were also suggested. These results will help to find proper strategy direction of A.I. business ecosystem and digital transformation.

• Journal of Trauma and Injury
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• v.37 no.1
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• pp.60-66
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• 2024

Purpose: This study investigated changes in interpersonal violence and utilization of trauma recovery services during the COVID-19 pandemic. At an urban level I trauma center, trauma recovery services (TRS) provide education, counseling, peer support, and coordination of rehabilitation and recovery to address social and mental health needs. The COVID-19 pandemic prompted considerable changes in hospital services and increases in interpersonal victimization. Methods: A retrospective analysis was conducted between September 6, 2018 and December 20, 2020 for 1,908 victim-of-crime patients, including 574 victims of interpersonal violence. Outcomes included length of stay associated with initial TRS presentation, number of subsequent emergency department visits, number of outpatient appointments, and utilization of specific specialties within the year following the initial traumatic event. Results: Patients were primarily female (59.4%), single (80.1%), non-Hispanic (86.7%), and Black (59.2%). The mean age was 33.0 years, and 247 patients (49.2%) presented due to physical assault, 132 (26.3%) due to gunshot wounds, and 76 (15.1%) due to sexual assault. The perpetrators were primarily partners (27.9%) or strangers (23.3%). During the study period, 266 patients (mean, 14.9 patients per month) presented before the declaration of COVID-19 as a national emergency on March 13, 2020, while 236 patients (mean, 25.9 patients per month) presented afterward, representing a 74.6% increase in victim-of-crime patients treated. Interactions with TRS decreased during the COVID-19 period, with an average of 3.0 interactions per patient before COVID-19 versus 1.9 after emergency declaration (P<0.01). Similarly, reductions in length of stay were noted; the pre-COVID-19 average was 3.6 days, compared to 2.1 days post-COVID-19 (P=0.01). Conclusions: While interpersonal violence increased, TRS interactions decreased during the COVID-19 pandemic, reflecting interruption of services, COVID-19 precautions, and postponement/cancellation of elective visits. Future direction of hospital policy to enable resource and service delivery to this population, despite internal and external challenges, appears warranted.