• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.361-373
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• 2023

In plant-pathogen interactions, Magnaporthe oryzae causes blast disease on more than 50 species of 14 monocot plants, including important crops such as rice, millet, and most 15 recently wheat. M. oryzae is a model fungus for studying plant-microbe interaction, and the main source for fungal pathogenesis in the field. Here we report that MoJMJD6 is required for conidium germination and appressorium formation in M. oryzae. We obtained MoJMJD6 mutants (ΔMojmjd6) using a target gene replacement strategy. The MoJMD6 deletion mutants were delayed for conidium germination, glycogen, and lipid droplets utilization and consequently had decreased virulence. In the ΔMojmjd6 null mutants, global histone methyltransferase modifications (H3K4me3, H3K9me3, H3K27me3, and H3K36me2/3) of the genome were unaffected. Taken together, our results indicated that MoJMJD6 function as a nuclear protein which plays an important role in conidium germination and appressorium formation in the M. oryzae. Our work provides insights into MoJMJD6-mediated regulation in the early stage of pathogenesis in plant fungi.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.381-388
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• 2023

Through artificial neural network education using spreadsheets, non-major undergraduate students can understand the operation principle of artificial neural networks and develop their own artificial neural network software. Here, training of the operation principle of artificial neural networks starts with the generation of training data and the assignment of correct answer labels. Then, the output value calculated from the firing and activation function of the artificial neuron, the parameters of the input layer, hidden layer, and output layer is learned. Finally, learning the process of calculating the error between the correct label of each initially defined training data and the output value calculated by the artificial neural network, and learning the process of calculating the parameters of the input layer, hidden layer, and output layer that minimize the total sum of squared errors. Training on the operation principles of artificial neural networks using a spreadsheet was conducted for undergraduate non-major students. And image training data and basic artificial neural network development results were collected. In this paper, we analyzed the results of collecting two types of training data and the corresponding artificial neural network SW with small 12-pixel images, and presented methods and execution results of using the collected training data for Orange machine learning model learning and analysis tools.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.125-132
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• 2024

The purpose of this study is to discover a drone utilization model tailored to local characteristics, propose directions for building a drone demonstration city based on demand surveys for drone activation, and suggest ways to utilize and support a drone application system. First, according to the survey results, there was a high understanding of and necessity for drone demonstration projects, particularly in addressing urban issues, which were deemed to have a significant impact. Second, based on the analysis of priorities and short- and long-term approaches, disaster-related tasks were evaluated as a priority, requiring an approach through medium- to long-term strategies. Third, it was noted that budgetary considerations emerged as the most critical issue during project implementation. Practitioners and experts expressed willingness to actively introduce drone-based technologies into their work when budget and technology were ready. Budgetary constraints were identified as the most significant obstacle to proper implementation, emphasizing the need for resolution. Fourth, the necessity of demand surveys during project development was identified in certain areas. Demand surveys were deemed essential for drone-based demonstration city construction, and a survey indicated that public leadership in this regard was also necessary. Fifth, concerning approaches in specific areas, the field of safety and disaster management was highlighted as the most crucial for application.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

• Knowledge Management Research
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• v.25 no.1
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• pp.67-88
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• 2024

Smart tourism aims to maximize the utilization of local tourism resources, effectively manages cities and contributes to improving communication and quality of life between tourists and residents. Therefore, smart tourism emphasizes synergistic collaboration, considering both residents and tourists. This study explores smart tourism interaction and roles in enhancing regional competitiveness. By conducting thorough examination, focusing on integrating the four key elements of smart tourism city (smart experience, smart convenience, smart accessibility, and smart platform) with local residents, local businesses, regional resources, and ecosystem to foster positive synergies, Incheon smart tourism city project was employed as a single case study design. Research results indicate that the collaborative model of a smart tourism city positively impacts service satisfaction and strengthens regional tourism competitiveness. Building upon these results, this study aims to contribute to the development of smart tourism cities by proposing directions for future development and emphasizing the enhancement of regional competitiveness through the integration of smart technology and local tourism.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.170-186
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• 2024

This study was conducted to identify the usage characteristics of the DMZ Punchbowl Trail (DPT) as a national forest trail (NFT) and to estimate its ripple effects on the local economy. The objective of this study is to provide policy implications for sustainable operational management. Out of the 500 questionnaires distributed, 215 respondents provided their complete travel itineraries and expenditures. The respondents, mainly aged 50 and above and residing in the Seoul Metropolitan Area, spend 3.5 hours of travel time to the DPT. Together with their families, the respondents typically spend approximately 4 hours for leisurely activities, primarily appreciation of scenic views and relaxation by visiting the "O-yubatgil." Furthermore, they extend their travels to other parts of Gangwon Province, where the DPT is situated. Within Gangwon Province, Yanggu County is the most visited destination. The respondents reported a notably higher average expenditure per visitor compared with the typical local walking tourists. Estimates show that the DPT generates an annual average of KRW 2.1 billion in direct expenditure (based on an average of 10,000 visitors for over five years), KRW 2.8 billion in production, and KRW 1.3 billion in added value, and it has created 40 jobs in Gangwon Province. The results of this study lies in empirically determining the specific economic scale and ripple effects of DPT as an NFT in the major sector, which occupies a significant portion of the Gangwon Province's local economy. The results will be instrumental in validating NFT policies and informing policy making for sustainable forest utilization.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.101-116
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• 2024

As the spread of smartphones has become more common and the utilization rate has increased, the mobile shopping market is also growing and expectations for related industries are also increasing. Mobile shopping apps are converging with various industries such as fashion, beauty, and lifestyle, and competition among companies to increase the number of users is intensifying with the activation of non-face-to-face. Accordingly, in this study, a study on the perceived value and intention to use mobile shopping apps was conducted based on a VAM. In order to test the hypothesis of this study, a questionnaire was conducted on 266 people who had used a mobile shopping app and it was used for analysis. Looking at the results, it was confirmed that both usefulness and enjoyment among the perceived benefit of mobile shopping apps have a positive (+) effect on the perceived value. However, it was found that the technicality and perceived risk among the perceived sacrifices of mobile shopping apps did not significantly affect the perceived value. Finally, it was confirmed that the perceived value of the mobile shopping app had a positive (+) effect on the intention to use. Through this study, we would like to examine the factors that can affect perceived value and usage intention in the mobile shopping app industry, which is increasingly competitive among companies along with the rapid growth of mobile technology and market, and suggest practical implications for related companies and officials to establish efficient strategies to further increase mobile shopping app users.