• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Composites Research
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• v.36 no.6
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• pp.441-447
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• 2023

Recently, as demand for high-strength, lightweight materials has increased, there has been great interest in joining with metals. In the case of mechanical bonding, such as bolting and riveting, chemical bonding using adhesives is attracting attention as stress concentration, cracks, and peeling occur. In this paper, surface treatment was performed to improve the adhesive strength, and the change in adhesive strength was analyzed. For the adhesive strength test were conducted with Carbon Fiber Reinforced Plastic(CFRP), CR340(Steel), and Al6061(Aluminum), and laser and plasma surface treatment were used. After plasma surface treatment, the adhesive strength improved by 7.3% and 39.2% in CFRP-CR340 and CFRP-Al6061, respectively. CR340-Al6061 was improved by 56.2% in laser surface treatment. Surface free energy(SFE) was measured by contact angle after plasma treatment, and it is thought that the adhesion strength was improved by minimizing damage through a chemical reaction mechanism. For laser surface treatment, it is thought that creates a rough bonding surface and improves adhesive strength due to the mechanical interlocking effect. Therefore, surface treatment is effect to improve adhesive strength, and based on this paper, the long-term fatigue test will be conducted to prevent fatigue failure, which is a representative cause of actual structural damage.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• Information Systems Review
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• v.20 no.3
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• pp.17-31
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• 2018

In recent years, success of information system projects to possess competitive advantage in business has become very important for stakeholders. Stakeholders who are interested in the success of information system projects typically consist of users who need the system, developers who build it, and project managers who are responsible for project success. However, recently, there has been increasing in the number of business analysts engaged in bridging relationships among these stakeholders in information system projects. So far, there have been many researches on the competence of users, developers or project managers. But, the research on the competencies of business analysts has not been done much. So, in this study, what competencies are needed for business analysts who are engaged in information system projects are researched, and the level and difference of stakeholders' expectations and satisfaction with them are identified, using IPA techniques. The results of this study are expected to contribute greatly to providing basic information on the development of competency models or training programs needed for recruitment, evaluation and training of business analysts who are or will be engaged in information system projects.

• Information Systems Review
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• v.21 no.4
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• pp.47-68
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• 2019

As cloud services and deployment models become diverse, there are a growing number of cloud computing selection options. Therefore, financial companies need a methodology to select the appropriated cloud for each financial computing system. This study adopted the Balanced Scorecard (BSC) framework to classify factors for the introduction of cloud computing in financial companies. Using Analytic Hierarchy Process (AHP), the evaluation items are layered into the performance perspective and the cloud consideration factor and a comprehensive decision model is proposed. To verify the proposed research model, a system of financial company is divided into three: account, information, and channel system, and the result of decision making by both financial business experts and technology experts from two financial companies were collected. The result shows that some common factors are important in all systems, but most of the factors considered are very different from system to system. We expect that our methodology contributes to the spread of cloud computing adoption.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.91-100
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• 2023

Background: This study evaluated the learning outcomes of dental hygiene students' ethical consciousness and ethical decision-making competence through dental ethics courses conducted in some universities. Methods: The subjects were 35 and 29 fourth-year dental hygiene students at G University in the first semester of 2021 and 2022, respectively, and 53 and 43 third-year dental hygiene students at D University, respectively, for a total of 160 students. After implementing the dental hygiene ethics course, classroom performance was evaluated in terms of moral sensitivity, confidence in making ethical decisions, classroom practicality, learning outcomes, and class satisfaction. Statistical analysis was conducted using independent t-test and paired t-test, and the statistical significance level was 0.05. Results: Both universities reported an increase in moral sensitivity and confidence in ethical decision-making after the course (p<0.001). Classroom practicality and class satisfaction for the dental hygiene ethics course did not differ between disciplines and were rated positively with a score of 4 or higher (p>0.05). Learning outcomes were higher among 4-year students than 3-year students (p<0.001). Conclusions: It was evaluated that the ethics in dental hygiene curriculum can strengthen students' competence in ethical decision-making, including moral sensitivity and confidence in solving ethical problems in dental hygiene.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.24.1-24.15
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• 2023

Background: The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. Methods: We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Results: Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. Conclusion: The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.20-31
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• 2024

Purpose: Research and development of high-strength concrete enables high-rise buildings and reduces the self-weight of the structure by reducing the cross-section, thereby reducing the thickness of beams and slabs to build more floors. A large effective space can be secured and the amount of reinforcement and concrete used to designate the base surface can be reduced. Method: In terms of field construction and quality, the effect of reducing the occurrence of drying shrinkage can be confirmed by studying the combination of low water bonding ratio and minimizing bleeding on the concrete surface. Result: The ease of site construction was confirmed due to the high self-charging property due to the increased fluidity by using high-performance water reducing agents, and the advantage of shortening the time to remove the formwork by expressing the early strength of concrete was confirmed. These experimental results show that the field application of ultra-high-strength concrete with a design standard strength of 100 MPa or higher can be expanded in high-rise buildings. Through this study, we experimented and evaluated whether ultra-high-strength concrete with a strength of 130 MPa or higher, considering the applicability of high-rise buildings with more than 120 floors in Korea, could be applied in the field. Conclusion: This study found the optimal mixing ratio studied by various methods of indoor basic experiments to confirm the applicability of ultra-high strength, produced 130MPa ultra-high strength concrete at a ready-mixed concrete factory similar to the real size, and tested the applicability of concrete to the fluidity and strength expression and hydration heat.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.