• Journal of Digital Convergence
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• v.20 no.1
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• pp.203-211
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• 2022

This study is to examine the causes of research misconduct such as plagiarism, forgery, redundant publication, unfair author expression, and incapacitation of the research ethics system of university researchers and to suggest improvement plan. It basically relied on literature research. In order to supplement the deficiencies in literature research, I sought advice from an expert professor who had experience working in a research-related field in university or who is currently in a position related to research ethics through the delphi-method. As a result of the study, from the perspective of individual researchers, the complacent attitude, dishonesty, and greed for research funds were identified as the main reasons. In terms of organization, it was analyzed for reasons such as lack of detail and application of regulations, lack of verification system, and performance-oriented research environment. In order to overcome research misconduct caused by the researcher's personal reasons, regularization, increase in the number of research ethics education, and strengthening personal penalties were suggested. As a way to overcome irregularities arising from institutional reasons, the reinforcement of the verification system, the reinforcement of the whistle-blower's personal protection system, the omission of promotion, and the quality and quantitative balance of research evaluation was suggested.

• Journal of the Korean Applied Science and Technology
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• v.39 no.2
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• pp.246-260
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• 2022

This study was conducted to analyze the creativity education contents of dance activities in physical education textbooks for the 3rd and 4th grades of elementary school. For this purpose, 16 types of textbooks and auxiliary data for physical education in the 3rd and 4th grades of elementary school were collected and analyzed using the creative education content analysis frame of the physical education textbook based on the 4P model. In order to secure the integrity of the research, expert consultation was operated. The results of this study are as follows. First, from the viewpoint of creative person, 'inquiry' was the most common in creative mind, and the rest of the elements appeared relatively evenly. As for the subject of activity, 'individual' and 'colleague (team)' showed similar frequencies. Second, from the viewpoint of the creative process, all activity areas appeared as 'learning', and most of the elements of the activity purpose appeared evenly, and the creative process was explored. Third, from the viewpoint of creative output, physical activity performance was the most common activity method, and two or three activity methods were used together. In the creativity factor, all factors appeared evenly, and sensitivity and sophistication were the most common with 4 factors. Fourth, from the viewpoint of the creative environment, most of the activity spaces were no restrictions, and the activity media consisted of many educational contents using the body. Through this study, it was requested that creativity education in dance activities should be expanded quantitatively and intensified in quality, and the necessity of spreading creativity education contents of dance activities to other areas was explored.

• Informatization Policy
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• v.30 no.4
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• pp.40-61
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• 2023

The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• Information Systems Review
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• v.19 no.3
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• pp.23-45
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• 2017

The ROK military is making a great effort and investment in establishing network-centric warfare, a future battlefield concept, as a major step in the establishment of a basic plan for military innovation. In the military organization level, an advanced process is introduced to shorten the command control time of the military and the business process is improved to shorten the decision time. In the information system dimension, an efficient resource management is achieved by establishing an automated command control system and a resource management information system by using the battle management information system. However, despite these efforts, we must evaluate the present level of informatization in an objective manner and assess the current progress toward the future goal of the military by using objective indicators. In promoting informatization, we must systematically identify the correct areas of improvement and identify policy directions to supplement in the future. Therefore, by analyzing preliminary research, workshops, and expert discussions on the major informatization level evaluation models at home and abroad, this study develops an evaluation model and several indicators that systematically reflect the characteristics of military organizations. The developed informatization level evaluation model is verified by conducting a feasibility test for the troops of the operation class or higher. We expect that this model will be able to objectively diagnose the level of informatization of the ROK military by putting budget and resources into the right place at the right time and to rapidly improve the vulnerability of the information sector.

• Korean Journal of Radiology
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• v.20 no.8
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.1-17
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• 2024

Forest management is known to beneficially influence stand structure and wood production, yet quantitative understanding as well as an illustrative depiction of the effects of different management approaches on tree growth and stand dynamics are still scarce. Long-term management of beech forests must balance public interests with ecological aspects. Efficient forest management requires the reliable prediction of tree growth change. We aimed to develop a novel hybrid simulation approach, which realistically simulates short- as well as long-term effects of different forest management regimes commonly applied, but not limited, to German low mountain ranges, including near-natural forest management based on single-tree selection harvesting. The model basically consists of three modules for (a) natural seedling regeneration, (b) mortality adjustment, and (c) tree growth simulation. In our approach, an existing validated growth model was used to calculate single year tree growth, and expanded on by including in a newly developed simulation process using calibrated modules based on practical experience in forest management and advice from the local forest. We included the following different beech forest-management scenarios that are representative for German low mountain ranges to our simulation tool: (1) plantation, (2) continuous cover forestry, and (3) reserved forest. The simulation results show a robust consistency with expert knowledge as well as a great comparability with mid-term monitoring data, indicating a strong model performance. We successfully developed a hybrid simulation that realistically reflects different management strategies and tree growth in low mountain range. This study represents a basis for a new model calibration method, which has translational potential for further studies to develop reliable tailor-made models adjusted to local situations in beech forest management.

• Land and Housing Review
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• v.15 no.3
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• pp.79-95
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• 2024

Over the past decade, urban regeneration projects have been actively promoted nationwide in South Korea, leading to the establishment of numerous community facilities aimed at fostering community engagement and revitalization. Recently, the need for diagnostic tools to provide appropriate support and guidance for the sustainable operation of these facilities has been increasingly emphasized. However, a comprehensive manual or nationwide standard approach remains lacking. As such, this study aims to propose evaluation criteria and their application methods for the periodic operational assessment of those facilities. First, through a review of previous studies, candidate indicators were identified. After that, preliminary indicators and frameworks were determined through expert consultations, followed by surveys conducted with 65 experts to finalize the evaluation indicators. The survey results highlighted the importance of 'Value Creation (VC)' and 'Operation Management (OM)' as key aspects of facility evaluation. In assessing VC, key factors include enhancing community vitality, strengthening local capacity, providing tailored functions for the community, and contributing to the local economy. In constructing OM, the focus lies on operational system, financial management, operational personnel, and economic viability and sustainability. This study proposes an evaluation framework that includes the final evaluation indicators and available data sources for each indicator, and a pilot test was conducted to verify its applicability. Moreover, the study suggests directions for utilizing the evaluation criteria and methods for applying indicators tailored to the characteristics of individual facilities.

• Journal of The Korean Association For Science Education
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• v.44 no.4
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• pp.343-360
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• 2024

The purpose of this study is to analyze the difficulties with scientific research faced by leading Korean scientists and suggest implications for science education. Data were collected through semi-structured interviews with 13 leading Korean scientists and were qualitatively analyzed using constructivist grounded theory. The results of the study showed that the leading scientists encountered 11 subcategories of difficulties, which were grouped into three main categories: uncharted territory, unexpected situations, and a lack of resources in domestic research environments. 'Uncharted territory' comprised anxiety due to uncertainty about research performance, insufficient knowledge accumulation in the field of research, and the burden of maintaining research influence as an academic leader. 'Unexpected situations' included encountering new phenomena that cannot be explained by existing theories, an inability to utilize research results, and repeated failures. 'A lack of resources in domestic research environments' included inadequate research funding support systems, a shortage of expert networks, limitations on employment and career opportunities for students, poor research equipment, and insufficient support policies for retired researchers. This study provides science educators with implications for the direction of science education and R&E. For students, it can serve as career education material, their attitudes towards science and their understanding of its nature. Lastly, the study may contribute to finding ways to improve scientific research policies and to developing a culture that fosters expertise in science.