• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.308-327
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• 2019

The concept of sharing economy has received rich attention in recent years. As a typical type of business model in the sharing economy, online short rent has been paid attention by both industry and academia. In this study, we find trust to be a critical determinant to the success of online short rent platforms. Based on three dimensions of trust theory, i.e., ability, benevolence and integrity, we investigate the factors influencing tenant' willingness to participate in online short rent. We further examine the extent to which trust can influence the number of sales and comments of rooms listed at online short-term rent platforms, which can represent tenant' willingness to participate in the sharing economy. The results show that the trust dimensions represented by a landlord's personal characteristics have significant positive correlations with the number of sales and comments. For example, the real name authentication and the sesame score can represent the trust integrity; online replay ratio and the average confirmation time representing the trust sincerity, and the order acceptance ratio representing the trust ability. On this basis, we proposed some recommendations for both platforms and landlords. For example, the landlords can improve the tenants' trust by authenticating his/her real name, replying actively and timely. For platforms, when they make housing list ranking rules, they can take the landlord's personal attributes that may affect trust into consideration. Moreover, platforms can also allow landlords to supply value-added services to improve service quality and ultimately promote the virtuous circle of the platform ecosphere. Through conducting the empirical research on a particular application of the sharing economy, we aim to fill the research gap of this field in China and provide theoretical and practical contributions to the future development of online short rent.

• Journal of Fashion Business
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• v.28 no.2
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• pp.1-14
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• 2024

Within the metaverse platform, users engage in communication with others through 'avatars' reflecting their own identities. Users experience various virtual fashion items through avatars, and the fashion industry anticipates avatars wearing virtual fashion items as an emerging business opportunity. Many fashion brands are currently releasing virtual fashion items specifically designed for avatars. In this study, we examined the impact of user identification with their avatar on their perception of the attributes of virtual fashion items (investment attractiveness, scarcity, playfulness, and aesthetics) and its influence on behavioral intentions. The research involved a survey of 250 females with prior knowledge of the metaverse. Structural equation modeling analysis was conducted to examine research hypotheses and validate the model. The results confirmed that as users within the metaverse perceive greater identification with their avatar, they also perceive the attributes of virtual fashion items more favorably. This finding affirms the self-reference effect, where users positively evaluate objects associated with themselves. Additionally, perceiving the attributes of virtual fashion items was found to be positively linked to purchase intentions for virtual products and actual interest in the brand. Lastly, a higher intention to purchase virtual fashion items was associated with forming a more favorable attitude toward the respective brand. Consequently, this study provides academic and practical implications for marketing strategies within the metaverse, emphasizing the active utilization of avatars and elements that facilitate user-avatar identification for effective engagement.

• Industry Promotion Research
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• v.9 no.3
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• pp.175-183
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• 2024

The purpose of this study is to derive a UX that can help the use of fintech applications by senior users in their 60s or older, which has naturally increased with the increase in the elderly population. The study extracted five factors commonly considered important in fintech applications and was conducted based on a survey of general users and in-depth interviews with senior users. The research factors were extracted through a literature review into three general user experience research factors and two senior user experience research factors. After a survey targeting general users, in-depth interviews were conducted with senior users. As a result, we found notable differences in concerns about responsiveness and perceived usefulness between general and senior users. Based on the characteristics derived from the results of this study, we designed UX to help senior users use fintech applications.

• Journal of Environmental Health Sciences
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• v.50 no.4
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• pp.237-256
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• 2024

Background: Human health and environment exposure assessments of chemicals are essential part for REACH and BPR as well as K-REACH and K-BPR. Several useful exposure assessment tools have been developed and updated to become extensively used during more than fifteen years of chemical registrations under REACH and their updates are still going on. Objectives: Evolution of regulatory tools for environment, workers and consumers exposure assessments under REACH is investigated focusing on why and how tools have been developed and updated for the future regulatory tools in Korea. Methods: REACH regulatory tools including EUSES, ECETOC TRA and CHESAR as well as built-in models SimpleTreat and SimpleBox were chosen with other frequently used Tier 1 and 2 tools. Available resources such as homepage information, background documents, related scientific reports, relevant journal publications, up-to-date lists of new version publications, release notes and user guides were reviewed extensively and summarized using easy-readable Tables and Figures. Results: SimpleTreat and SimpleBox are built-in models both for EUSES and ECETOC TRA (Environment). ECHA's CHESAR contains ECETOC TRA (Workers) and ECETOC TRA (Consumers) as well as EUSES and ECETOC TRA (Environment) for environment exposure assessment while results of Tier 2 Stoffenmanager and ConsExpoWeb can be imported. Evolution of CHESAR from version 1 (2010) to 3.8 (2023) has focused on the compatibility of frequent updates of IUCLID, importing functionality, editability, updated use maps, harmonised conditions of use as well as updates of the built-in tools evolved according to scientific development, refinements of the tool, increased conservatism and user-friendliness. CHESAR Platform 1.0 will soon be published to serve both for REACH and BPR. Conclusions: Updates of the tools can be successfully continued by transparency of the tools, participation of industry sectors for tool refinements and tool developers'/authorities' encouragements of partners/users to jointly innovate tools through scientific researches, tool validations and user feedback.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.379-385
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• 2014

N-Screen era is held due to the cloud computing technology which access to contents anytime, anywhere without any persistent. In the broadcast industry, this broadcast contents is rapidly serviced by variety of media devices such as PC, Smart phone, Tablet, App, IPTV. To Increase the usefulness and usability of the platform, same brand identity have to be maintain by devices and integrated guide which can encompass a variety of media are needed. This study tries to figure out the need of New Media Style Guide to keep brand identity in a variety of new media beyond previously Web style guide which limited in the Web pages. First, Integrated Guide GEL of BBC's and Web style guide of KBS was analyzed. Through the analysis it was found that the limitations and problem of the current web style guide and then suggested the improvement direction. In addition, this study tried to find which design elements should be made for new media style guides through in-depth interview with practitioners who work in broadcast media industry for more than three years. Through the research it was understood the current status of integrated brand identity and found a way to improve forward to new media platforms of KBS.

• Journal of Distribution Science
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• v.15 no.10
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• pp.93-103
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• 2017

Purpose - Sports and the media have been developed together through a close relationship. During the past decade, the media landscape and the coverage of sports events have been changed. Sports fans can use the sports content at the time they want, on the platform they prefer. Furthermore, thanks to the advanced information technology, sports fans are likely to be more engaged in sport in communication technology-friendly stadium. However, the literature on the relationship between sports and the media has heavily focused on the differences of media types, clear distinction between media suppliers and consumers, and the limited media extension. Given the limitation of prior research, therefore, it has not fully reflected the change in society and culture, the importance of media recipients or consumers, and the mediating characteristic of the media. In order to generate further insights for sport media related industry and its society, it is necessary to comprehend the contemporary phenomena of real world situation in new media and sport and to discuss how new media influence sport and how their relationship is changing in managerial context. The purpose of this study is to identify new media cases as distribution channels in sports context and is to develop insights by discussing its role and meaning of new media in sport society. Research design, data, and methodology - The study employed the theory-centered review and case analysis. In order to explain phenomena of the role of new media in contemporary sport and to generate related insights in sport context, the study reviewed the new media cases applied in sport industry and interpreted their meaning by employing medium theory, remediation theory, and new media theory. Results - The study discussed the limitation of prior sport media research and identified the characteristics of sport as new media such as remediating, extending sensory organs, reiterating physical space and electronic space. Conclusions - The study derived the characteristics of sport as new media, in a sport setting, and through sports settings. Findings would assist to comprehend the role of new media in spectating sport and provide insights for sport media study.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.597-615
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• 2016

Since 1997 the United Kingdom has pursued creative industry and creative city development in accordance with the New Labor Party policy, strengthening its cluster policy by assigning creative city policies to traditional manufacturing-oriented regions. Tech City in London, one of the most successful examples of digital clusters, is an area in which diverse ecosystems for venture business integration have been established, as the once barren space began to spontaneously develop. For this region, systematic linkages including universities, private companies, start-ups, and accelerators have been added, along with the UK government's active support system. As a result of this opportunity, the scale of the UK start-up ecosystem has significantly grown, the number of local companies has surged, and brand effect has greatly improved. Tech City is an example of a well-balanced combination of public effort and private governance, based on the region's historical background and its potential for growth. It is an effective coordination of public policy and private active investment, services, research, and education. The market platform for institutional technology and commercialization, and aggressive investment shares in the risk, have lead to its growth as a start-up and an innovative city. Britain's efforts to expand the nationwide cluster for the future-oriented digital economy is most noteworthy.