• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.64-69
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• 2022

Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.

• The Korean Journal of Franchise Management
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• v.13 no.4
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• pp.1-12
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• 2022

Purpose: In 2020, the franchise industry accomplished a significant growth compared to the previous year, as the number of franchise companies increased by 9.0% while the number of franchise brands increased by 12.5%. Despite growth in size, the Korean franchise industry underwent many negative incidents, such as franchise ownership sales to private equity funds, that led to deterioration of businesses. From this point of view, this study aims to make various proposals to help policy makers develop franchise industry policies by analyzing trends of the current and previous presidential administrations' franchise policies and regulations using newspaper articles. Research design, data and methodology: A total of 7,439 articles registered in Naver API from February 25, 2013 to November 29, 2021 were extracted. Among them, 34 unrelated video articles were deleted, and a total of 7,405 articles from both administrations were used for analysis. The R package was used for word frequency analysis, word clouding, word correlation analysis, and LDA (Latent Dirichlet Allocation) topic modeling. Results: The keyword frequency analysis shows that the most frequently mentioned keywords during the previous administration include 'no-brand', 'major company', 'bill', 'business field', and 'SMEs', and those mentioned during the current administration include 'industry' and 'policy'. As a result of LDA topic modeling, 9 topics such as 'global startups' and 'job creation' from the previous administration, and 10 topics such as 'franchise business' and 'distribution industry' from the current administration were derived. The results of LDAvis showed that the previous administration operated a policy based on mutual growth of large and small businesses rather than hostile regulations in the franchise business, whereas the current administration extended the regulation related to franchise business to the employment sector. Conclusions: The analysis of past two administrations' franchise policy, it can be suggested that franchisors and franchisees may complement each other in developing the Fair Transactions in Franchise Business Act and achieving balanced growth. Moreover, political support is needed for sound development of franchisors. Limitations and future research suggestions are presented at the end of this study.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.37-43
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• 2022

The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.

• The korean journal of orthodontics
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• v.53 no.2
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• pp.125-136
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• 2023

Before progress was recently made in the application of temporary anchorage devices (TADs) in bio-mechanical design, orthodontists were rarely able to intrude molars to reduce upper posterior dental height (UPDH). However, TADs are now widely used to intrude molars to flatten the occlusal plane or induce counterclockwise rotation of the mandible. Previous studies involving clinical or animal histological evaluation on changes in periodontal conditions after molar intrusion have been reported, however, studies involving human histology are scarce. This case was a Class I malocclusion with a high mandibular plane angle. Upper molar intrusion with TADs was performed to reduce UPDH, which led to counterclockwise rotation of the mandible. After 5 months of upper molar intrusion, shortened clinical crowns were noticed, which caused difficulties in oral hygiene and hindered orthodontic tooth movement. The mid-treatment cone-beam computed tomography revealed redundant bone physically interfering with buccal attachment and osseous resective surgeries were followed. During the surgeries, bilateral mini screws were removed and bulging alveolar bone and gingiva were harvested for biopsy. Histological examination revealed bacterial colonies at the bottom of the sulcus. Infiltration of chronic inflammatory cells underneath the non-keratinized sulcular epithelium was noted, with abundant capillaries being filled with red blood cells. Proximal alveolar bone facing the bottom of the gingival sulcus exhibited active bone remodeling and woven bone formation with plump osteocytes in the lacunae. On the other hand, buccal alveolar bone exhibited lamination, indicating slow bone turnover in the lateral region.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.80-92
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• 2017

The popularity of cloud computing has led to the emergence of various types of cloud services, and the hybrid cloud, a deployment model that integrates public cloud and private cloud and offset their shortcomings, is in the spotlight recently. However, the complexity of different clouds integration and the lack of related integration solutions have delayed the adoption of hybrid cloud and cloud strategy by companies and organizations. Therefore, in this paper, we propose a cloud integration mechanism to solve the integration complexity problem. The cloud integration mechanism proposed in this paper consists of integration script that solves the cloud integration by the script based on the hybrid cloud function, a process of creating and executing it, and a script creation model applying the software design pattern. By integrating the various cloud services, we can quickly generate scripts that meet the user's needs. It is expected that the introduction of hybrid cloud and the acquisition of cloud strategy can be accelerated through this proposed integration mechanism.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.41-58
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• 2023

This study identifies the commercialization success factors that can be an important indicator for the transfer and commercialization of national R&D results in the agricultural sector. Unlike other industries, the agricultural sector has a non-systematically scaled and processed industrial structure, and R&D is led by government rather than the private sector. Although the quantitative performance of national agricultural R&D, especially the number of patents and publications, has increased rapidly with the quantitative expansion of the government R&D budget, the technology commercialization of the results of agricultural R&D has been accompanied by difficulties for SMEs. Therefore, this study summarized the success factors for commercialization of state-owned technologies presented in previous studies, and based on them, analysed the success factors for commercialization specific to the agricultural sector. It also conducted a questionnaire survey using Delphi and focus group interviews (FGI) with experts from academia, research and industry, and a survey of agricultural companies to derive success factors for commercialization in the agricultural sector using logistic regression analysis. As a result, five indicators with positive correlation and three indicators with negative correlation within technology characteristics, suppliers, adopters, policy and market factors were finally derived as key factors for agricultural commercialization. In the future, it is expected that independent factor analysis of the food and seed sectors, which have independent industry characteristics from the agricultural sector, will be needed.

• Current Photovoltaic Research
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• v.11 no.4
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• pp.134-143
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• 2023

Efforts are being made to respond to global warming. Interest in and demand for the private sector-led RE100 campaign is also increasing. Self-built solar power generation, one of the implementation tools for RE100, is not expanding. However, it can be an economical means of implementation in the long run. In this study, we intend to analyze the impact on the optimal ratio of self-solar power generation using HOMER simulation. OPR defines the optimal solar power generation ratio and looks into what changes there are in the optimal solar power ratio when self-power consumption increases and external power purchase price changes. As a result, the optimal rate of self-solar power generation has a low impact even if self-power consumption increases. As the external power unit price increases, the optimal ratio increases, and at a power unit price of 100 KRW/kWh, OPR is 24%; at 200 KRW/kWh OPR is 31%; and at 300 KRW/kWh OPR is 34%. This shows that the electricity price replaced during the life cycle has a high impact on the economic feasibility of solar power generation. However, when the external power unit price reached a certain level, the increase in OPR decreased. This shows that it is difficult for domestic companies to achieve RE100 based on the economic feasibility of solar energy alone. Therefore, efforts are needed to supply renewable energy in the public sector.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.51-61
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• 2023

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

• Journal of KIBIM
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• v.13 no.4
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• pp.1-12
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• 2023

In Korea, there are many attempts to automate architectural design tasks, focusing on government-led national R&D projects and private operators, in order to enhance global competitiveness and productivity of the building service industry. However, according to a survey of architectural design office practitioners, only 25% (9 out of 37) have used it, suggesting that there are fewer cases of practical use in the field compared to research and investment in automation technology development, and there are discrepancies between automation and technology development items required in the field. In this study, the priority of automation of planning and design work of architectural design office practitioners is derived, and a comparative analysis is conducted with domestic architectural design automation service items based on the priority. A survey was conducted on practitioners of domestic architectural design offices to derive automation priorities for 19 items of architectural planning and design work. Based on the derived priorities, the degree of reflection of the working-level automation needs of domestic services was confirmed by comparing them with the domestic architectural planning and design automation service items. As a result, it was confirmed that domestic architectural planning and design automation services did not properly reflect the priority of planning and design work automation of architectural design office practitioners. This suggests that it is necessary to reflect the priorities derived in this study in technology development in order to increase the cases of practical use of the automation technology in the working environment and improve the productivity of service.