• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Journal of The Korean Association For Science Education
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• v.26 no.6
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• pp.743-752
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• 2006

The purpose of this study is to gain an understanding of scientifically gifted students' views on the nature of science. A multiple-choice format questionnaire was administered to 237 Korean 10th, 11th and 12th graders at the Korea Science Academy. The differences and similarities by gender and experience of R&E program on the students' views of the nature of science were investigated. The questionnaire developed by Lim(2004) was implemented for this investigation. We found that the majority of scientifically gifted students had highly possessed the tentativeness of scientific knowledge. The students who experienced R&E program have relatively high apprehension of scientists' motivation for researches and activities in social context compared to the students who did not experience an R&E program. Scientifically gifted students had relatively high apprehension that government should not control researches of scientists and relatively low apprehension of social responsibilities of scientists comparing to general high school students. The experience on R&E program was identified as a factor to effect changes in the students' views on the nature of science. The study has implications for the development of gifted program and curriculum such as running and assessing R&E program, and also the pre-service preparation of science teacher, teacher education reformat in both the practical and the policy levels.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.189-196
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• 2024

Currently, it is almost impossible to move the focus of a previously taken photo to a different location. This paper challenges a technology that can move the focus of a captured photo to another location after shooting. To achieve this goal, this paper proposed and implemented a method for taking photos with various focuses at the moment the camera took pictures and storing them in a single JPEG file to extract photos focused on the user's preferred location. In this paper, two methods are implemented: taking various photos by quickly moving the focal length of the lens from close to far away, and taking various photos focused on each object by recognizing objects in the camera viewfinder. Various photos taken are stored in a single JPEG to maintain compatibility with traditional photo viewers. At this time, this JPEG file used the All-in-JPEG format proposed in previous research to store a variety of images. This paper verified its practicality by implementing these technologies in an Android app named OnePIC.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1348-1363
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• 2020

The newly revised 2018 Organ Injury Scale (OIS) has a similar format to the previous American Association for Surgery and Trauma (AAST) Emergency General Surgery Grading System, dividing the criteria for grading solid organ damage into three groups; imaging, operation, and pathology. The most significant alteration in the OIS system 2018 revision is the incorporation of multidetector CT (MDCT) findings of vascular injury including pseudoaneurysm and arteriovenous fistula. Similar to the previous OIS, the highest of the three criteria is assigned the final grade. In addition, if multiple grade I or II injuries are present, one grade is advanced for multiple injuries up to grade III. This pictorial essay demonstrates the MDCT findings of solid organ injury grades based on the 2018 OIS system.

• Journal of Science Education
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• v.48 no.1
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• pp.31-46
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• 2024

This study analyzes the academic achievement characteristics of Korean fourth graders on the problem solving and inquiry tasks (PSIs) introduced in TIMSS 2019. TIMSS 2019 conducted a computer-based assessment in addition to the traditional paper-based assessment. The PSIs were included only in the computer-based assessment, so 30 countries participated in the PSIs of the computer-based assessment. PSIs consist of integrating multiple content and cognitive domains, including 10 or fewer items. Most of the items are constructed in an open-ended format rather than multiple-choice. The analysis results showed that there were differences in student achievement across countries depending on the inclusion of PSIs. Korea's average achievement score decreased by 1 point. The analysis of individual items showed that students' achievement was somewhat low, and the correct answer rate for male students was generally higher than that for female students in many items. Furthermore, item-by-item analysis revealed that there were items where countries such as England and Finland had higher correct answer rates than traditional high-achieving countries, i.e. Singapore, Taiwan, and Korea. Considering the recent emphasis on integrated education, it seems necessary to review the use of PSIs in assessments in Korea as well.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.205-212
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• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.577-586
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• 2024

As new technologies are developed, new security threats such as the emergence of AI technologies that create ransomware are also increasing. New security equipment such as XDR has been developed to cope with these security threats, but when using various security equipment together rather than a single security equipment environment, there is a difficulty in creating numerous regular expressions for identifying and classifying essential data. To solve this problem, this paper proposes a method of identifying essential information for identifying threat information by introducing artificial intelligence-based entity name recognition technology in various security equipment usage environments. After analyzing the security equipment log data to select essential information, the storage format of information and the tag list for utilizing artificial intelligence were defined, and the method of identifying and extracting essential data is proposed through entity name recognition technology using artificial intelligence. As a result of various security equipment log data and 23 tag-based entity name recognition tests, the weight average of f1-score for each tag is 0.44 for Bi-LSTM-CRF and 0.99 for BERT-CRF. In the future, we plan to study the process of integrating the regular expression-based threat information identification and extraction method and artificial intelligence-based threat information and apply the process based on new data.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2024

This study conducted voluntary focus group user observations to empirically validate the efficacy of the self-developed psychological support mobile application, "Mindful Now". The app is structured as an interactive game format, enabling individuals to activate self-awareness of mindfulness states anytime, anywhere. It consists of a 3-step process of sensory/emotional/consciousness awareness, facilitating the expression of non-judgmental awareness. To demonstrate the effectiveness of this mindful activation in enhancing psychological well-being such as happiness and stress reduction, voluntary mindfulness mobile app usage was tracked among 49 university students. The results revealed significant improvements, with a 14.4% increase in SWLS happiness index and a 17.1% decrease in PSS-10 stress levels among 12 users who used the app continuously for over 60 days to practice mindfulness awareness. Particularly, higher app engagement was observed among students who initially reported relatively lower indices before using the app. The utilization of mobile apps that promote mindful activation aligns with various therapeutic paradigms based on mindfulness and meditation, contributing to advancements in digital therapeutic interventions for psychological support.