• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.823-835
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• 2023

Recently, due to the increase in abnormal climate, rainfall intensity is increasing and drought periods are continuing. These environmental changes lead to prolonged drought conditions and difficulties in real-time recognition. In general, drought can be judged by the amount of precipitation and the number of days without rainfall. In determining the impact of drought, it is divided into meteorological drought, agricultural drought, and hydrological drought and evaluation is made using the drought index, but environmental drought evaluation is insufficient. The river water quality managed through the total water pollution cap system is vulnerable to the effects of such drought. In this study, we aim to determine the drought impact on river water quality and quantify the impact of prolonged drought on water quality. The impact of rain-free days and accumulated precipitation on river water quality was quantitatively evaluated. The Load Duration Curve (LDC), which is used to evaluate the water quality of rivers, was used to evaluate water pollution occurring at specific times. It has been observed that when the number of consecutive rainless days exceeds 14 days, the target water quality in the mid-basin is exceeded in over 60% of cases. The cumulative rainfall is set at 28 days as the criteria, with an annual average rainfall of 3%, which is 32.1 mm or less. It has been noted that changes in water quality in rivers occur when there are 14 or more rainless days and the cumulative rainfall over 28 days is 32.1 mm or less in the Gamcheon Mid-basin. Based on the results of this study, it aims to quantify the drought impact and contribute to the development of a drought water quality index for future environmental droughts.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.107-120
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• 2022

The purpose of this study is to analyze the net effect and the combined effect of the determinants of the likelihood of start-up success of researchers at public research institutes. Based on the existing literature, the determinants of the researcher's likelihood of start-up success were reviewed, and a conceptual relationship between the determinants of the likelihood of start-up success was established. Data collection was conducted through a survey targeting researchers at public research institutes, and a total of 114 data were collected. The partial least squares (PLS) analysis method was used to analyze the net effect of the likelihood of start-up success determinant, and the fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze the combined effect of the likelihood of start-up success determinant. In the PLS analysis results, it was found that technology commercialization probability and creative self-efficacy had a significant positive effect independently on the likelihood of start-up success. In the fsQCA results, we found a combined effect of increasing the likelihood of start-up success when the technology commercialization probability, technology commercialization capability, and creative self-efficacy were high. These research results provide academic implications for understanding the determinants of the likelihood of start-up success of researchers in public research institutes.

• Information Systems Review
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• v.23 no.3
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• pp.97-125
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• 2021

Blockchain is a core technology to solve personal information leakage and data management issues, which are limitations of existing Genomic Sequencing services. Due to continuous cost reduction and deregulation, the market size of Genomic Sequencing has been increasing, also the potential of services is expected to increase when Blockchain's security and connectivity are combined. We created our research model by combining the Technology Acceptance Model (TAM) and the Innovation Resistance Theory also analyzed the factors affecting the acceptance intention and innovation resistance of the Blockchain Based Genomic Sequencing Information Platform. A survey was conducted on 150 potential users of Blockchain and Genomic Sequencing services. The analysis was conducted by setting the four Blockchain variables: Security, transparency, availability, and diversity). Also, we set the Perceived Usefulness, Perceived risk, and Perceived Complexity for Technology Acceptance and Innovation Resistance variables and analyzed the effect of the characteristics of the Blockchain on acceptance intention and innovation resistance through these variables. Through this analysis, key variables that need to be considered important to reduce resistance and increase acceptance intention could be identified. This study presents innovation factors that should be considered in companies preparing a new Blockchain Based Genomic Sequencing Information Platform.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.64-70
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• 2024

The demand for safety certification of Light Unmanned Aerial Vehicles (LUAVs), weighing between 25kg and 150kg, is rapidly increasing in Korea. Unfortunately, the number of LUAV safety certification failures is also on the rise, with manufacturing quality issues being identified as the main culprit. However, there is a lack of quality system standards for manufacturers within the LUAV safety certification system. As a result, this paper aims to analyze the domestic safety certification system and the quality standards set by the American Society for Testing and Materials (ASTM) for small Unmanned Aerial Systems (sUAS). The goal is to establish quality system inspection standards specifically tailored for LUAV manufacturers. To achieve this, we propose additional inspection items that reflect the characteristics of the manufacturing quality system. These items will be identified through on-site inspections of LUAV manufacturers, ensuring that the resulting quality system standard aligns with the actual situation of domestic manufacturers. In order to gauge the feasibility and effectiveness of the proposed quality system standard, we conducted a survey of seven domestic LUAV manufacturers.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1036-1044
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• 2020

Objective: To investigate the clinical feasibility of synthetic diffusion-weighted imaging (sDWI) at different b-values in patients with breast cancer by assessing the diagnostic image quality and the quantitative measurements compared with conventional diffusion-weighted imaging (cDWI). Materials and Methods: Fifty patients with breast cancer were assessed using cDWI at b-values of 800 and 1500 s/mm² (cDWI₈₀₀ and cDWI₁₅₀₀) and sDWI at b-values of 1000 and 1500 s/mm² (sDWI₁₀₀₀ and sDWI₁₅₀₀). Qualitative analysis (normal glandular tissue suppression, overall image quality, and lesion conspicuity) was performed using a 4-point Likert-scale for all DWI sets and the cancer detection rate (CDR) was calculated. We also evaluated cancer-to-parenchyma contrast ratios for each DWI set in 45 patients with the lesion identified on any of the DWI sets. Statistical comparisons were performed using Friedman test, one-way analysis of variance, and Cochran's Q test. Results: All parameters of qualitative analysis, cancer-to-parenchyma contrast ratios, and CDR increased with increasing b-values, regardless of the type of imaging (synthetic or conventional) (p < 0.001). Additionally, sDWI₁₅₀₀ provided better lesion conspicuity than cDWI₁₅₀₀ (3.52 ± 0.92 vs. 3.39 ± 0.90, p < 0.05). Although cDWI₁₅₀₀ showed better normal glandular tissue suppression and overall image quality than sDWI₁₅₀₀ (3.66 ± 0.78 and 3.73 ± 0.62 vs. 3.32 ± 0.90 and 3.35 ± 0.81, respectively; p < 0.05), there was no significant difference in their CDR (90.0%). Cancer-to-parenchyma contrast ratios were greater in sDWI₁₅₀₀ than in cDWI₁₅₀₀ (0.63 ± 0.17 vs. 0.55 ± 0.18, p < 0.001). Conclusion: sDWI₁₅₀₀ can be feasible for evaluating breast cancers in clinical practice. It provides higher tumor conspicuity, better cancer-to-parenchyma contrast ratio, and comparable CDR when compared with cDWI₁₅₀₀.

• Knowledge Management Research
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• v.25 no.1
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• pp.135-154
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• 2024

As the digital transformation accelerates, the demand for professionals with competencies in various digital technologies such as artificial intelligence, big data is increasing in the industry. In response, the government is developing various educational programs to nurture talent in these emerging technology fields. However, the lack of a clear definition of competencies, which is the foundation of curriculum development and operation, has posed challenges in effectively designing digital technology education programs. This study systematically reviews the definitions and characteristics of competencies presented in prior research based on a literature review. Subsequently, in-depth interviews were conducted with 30 experts in emerging technology fields to derive a definition of competencies suitable for technology education programs. This research defines competencies for the development of technology education programs as 'a set of one or more knowledge and skills required to perform effectively at the expected level of a given task.' Additionally, the study identifies the elements of competencies, including knowledge and skills, as well as the principles of competency construction. The definition and characteristics of competencies provided in this study can be utilized to create more systematic and effective educational programs in emerging technology fields and bridge the gap between education and industry practice.

• Clean Technology
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• v.30 no.1
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• pp.28-36
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• 2024

As demand for electric vehicles increases, the market for lithium-ion batteries is also rapidly increasing. The battery life of lithium-ion batteries is limited, so waste lithium-ion batteries are inevitably generated. Accordingly, lithium was selectively preleached from waste lithium iron phosphate (LiFePO₄, hereafter referred to as the LFP) cathode material powder among lithium ion batteries, and iron phosphate (FePO₄) powder was recovered. The recovered iron phosphate powder was mixed with alkaline sodium carbonate (Na₂CO₃) powder and heat treated to confirm its crystalline phase. The heat treatment temperature was set as a variable, and then the leaching rate and powder characteristics of each ingredient were compared after water leaching using Di-water. In this study, lithium showed a leaching rate of approximately 100%, and in the case of powder heat-treated at 800 ℃, phosphorus was leached by approximately 99%, and the leaching residue was confirmed to be a single crystal phase of Fe₂O₃. Therefore, in this study, lithium, phosphorus, and iron components were individually separated and recovered from waste LFP powder.

• Journal of Korean Home Economics Education Association
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• v.35 no.4
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• pp.1-12
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• 2023

At a time when interest in the educational use of ChatGPT is increasing, it is necessary to investigate the perception of ChatGPT among college students. A survey was conducted to compare the current status of internet and interactive artificial intelligence use and perceptions of ChatGPT after using it in the following courses in Spring 2023; 'Family Life and Culture', 'Fashion and Museums', and 'Fashion in Movies' in the first semester of 2023. We also looked at comparative analysis reports and reflection diaries. Information for coursework was mainly obtained through internet searches and articles, but only 9.84% used interactive AI, showing that its application to learning is still insufficient. ChatGPT was first used in the Spring semester of 2023, and ChatGPT was mainly used among conversational AI. ChatGPT is a bit lacking in terms of information accuracy and reliability, but it is convenient because it allows students to find information while interacting easily and quickly, and the satisfaction level was high, so there was a willingness to use ChatGPT more actively in the future. Regarding the impact of ChatGPT on education, students said that it was positive that they were self-directed and that they set up a cooperative class process to verify information through group discussions and problem-solving attitudes through questions. However, problems were recognized that lowered trust, such as plagiarism, copyright, data bias, lack of up-to-date data learning, and generation of inaccurate or incorrect information, which need to be improved.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.559-565
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• 2024

Research on autonomous vehicles is being actively conducted. As autonomous vehicles emerge, there will be a transitional period in which traditional and autonomous vehicles coexist, potentially leading to a higher accident rate. Currently, when a traffic accident occurs, the fault ratio is determined according to the criteria set by the General Insurance Association of Korea. However, the time required to investigate the type of accident is substantial. Additionally, there is an increasing trend in fault ratio disputes, with requests for reconsideration even after the fault ratio has been determined. To reduce these temporal and material costs, we propose a deep learning model that automatically determines fault ratios. In this study, we aimed to determine fault ratios based on accident video through a image classification model based on ResNet-18 and video action recognition using TSN. If this model commercialized, could significantly reduce the time required to measure fault ratios. Moreover, it provides an objective metric for fault ratios that can be offered to the parties involved, potentially alleviating fault ratio disputes.