• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.287-300
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• 2021

Collaborative Cyber-Physical Systems (CCPS) are those systems that contain tightly coupled physical and cyber components, massively interconnected subsystems, and collaborate to achieve a common goal. The safety of a single Cyber-Physical System (CPS) can be achieved by following the safety standards such as ISO 26262 and IEC 61508 or by applying hazard analysis techniques. However, due to the complex, highly interconnected, heterogeneous, and collaborative nature of CCPS, a fault in one CPS's components can trigger many other faults in other collaborating CPSs. Therefore, a safety assurance technique based on fault criticality analysis would require to ensure safety in CCPS. This paper presents a Fault Criticality Matrix (FCM) implemented in our tool called CPSTracer, which contains several data such as identified fault, fault criticality, safety guard, etc. The proposed FCM is based on composite hazard analysis and content-based relationships among the hazard analysis artifacts, and ensures that the safety guard controls the identified faults at design time; thus, we can effectively manage and control the fault at the design phase to ensure the safe development of CPSs. To justify our approach, we introduce a case study on the Platooning system (a collaborative CPS). We perform the criticality analysis of the Platooning system using FCM in our developed tool. After the detailed fault criticality analysis, we investigate the results to check the appropriateness and effectiveness with two research questions. Also, by performing simulation for the Platooning, we showed that the rate of collision of the Platooning system without using FCM was quite high as compared to the rate of collisions of the system after analyzing the fault criticality using FCM.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Journal of Christian Education in Korea
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• v.64
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• pp.445-477
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• 2020

The topic of school community is considered very important in Christian education because the Christian religion is based on relationships. However, the studies on school community in Christian education are insufficient. Therefore, this study intends to explore the characteristics of school communities in Christian elementary school through descriptive approach. For this, data was collected through a ten-week participant observations and ten-time interview with the teachers, students, and parents, and artifacts collection. The result of the analysis, students were growing up with experiencing inner conflicts when they have a good relationship with their friends as a member of one community. This study gives suggestions to Christian school and public school communities as follows. First, studies on Christian schools' community have to be based on the Christian worldview. Second, The community of justice needs to be included in Christian school communities. Third, Christian school students experience continuous inner conflicts when practicing Christian values, thus the classroom community needs to be a place where students can talk about it openly. Fourth, public schools only consider the abilities to cultivate community competence, but it needs to apply the Christian school community where it is comfortable and acceptable to reveal weakness. In this way, the community will become a more humanistic environment.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.3
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• pp.29-39
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• 2021

This study interpreted a traditional garden of Korea through reception aesthetics. The 'gap' of acceptable aesthetics is found in the 'distance that occurs between writers and readers'. This study aims to interpret this gap between what the writer intends and what the reader expects. Boundaries do not limit traditional spaces in Korea and, therefore, are not limited by characteristics. These characteristics were studied from the perspectives of the author, the work, and the reader. The aspect first looked at the life and ideas of the gardener, the second analyzed the form and structure of the garden, and the last examined writings, from the past and present, of those who appreciated the garden. From the author's perspective, Sosoewon was understood as a garden that embodied the philosophy of Yang Sanbo. Second, the duality and indeterminateness were recognized as two characteristics of this work; artifacts in the garden were arranged in a natural form, and the gardens have indefinite boundaries, so they can be freely expanded and reduced. Finally, from the reader's perspective, it was noted that the beauty of this garden is enhanced through poetry, painting, and writing. Thus, historic gardens of Korea can be open spaces where the meaning of the garden is enriched through the free participation of viewers based on their own ideas.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.4-17
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• 2021

This study analyzed 19 samples of harness fittings and pendants, which were excavated in Tomb No. 15 in Songhyeon-dong, Changnyeong. Harness fittings and pendants are used for ostentation, rather than practicality, and were excavated from ancient tombs in Gaya culture. So, they are considered artifacts that compare the production techniques and raw materials. This study aimed to examine the production techniques and provenience studies of Bihwa Gaya, which is estimated to be from the 5th to 6th centuries. According to the research, harness fittings were made of pure copper and were gilded with Au·Ag alloys on their surfaces. Hg was detected together and plated with a mercury amalgam method. As a result of the pendant (fish scales-pattern, oval and fish-tail shape), analysis showed that Fe in the background metal, Cu in the middle layer, and Au and Ag on the surface were the main components. The method of adhesion between Cu and Au·Ag gilded layers are plated by a mercury amalgamation method. So, it was identified by the gilt-iron·gold·bronze technique. Since the pendant (heart shaped) is found to be the main component of Fe in the background metal and Ag in the surface layer, the metal was made gilt-iron·silver technique. The background metal and gilding were additionally fixed using a rivet. The raw materials of 3 harnesses excavated from Changnyeong are plotted in zone 2 in the southern Korean Peninsula. And 16 harnesses were plotted in Chinese copper ore by Mabuchi Hishao in the Chinese Peninsula.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.1
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• pp.60-67
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• 2021

Electroencephalography (EEG) is used for the diagnosis of epilepsy and testing the brain function. Clinical technologists are responsible for recording EEG without artifacts in accordance with the international 10~20 electrode system. Training on these techniques requires practical education. In the case of EEG, however, it is difficult for trainees to find the correct location of the electrode. Therefore, this study compared the time spent to locate the electrode attachment between traditional tape and the newly developed band. The time spent for sitting position patients using the band (196.7±61.8s) was 1084.3 s faster than the tape (1,281.0±457.4s) (P<0.001). Furthermore, the spend time spent for lying position patients using the band (200.2±49.3s) was 1217.7s faster than the tape (1417.9±482.3s) (P<0.001). Measurements using the band showed fewer differences due to various factors, such as position, practical experience, and gender. The newly developed band can locate the correct electrode attachment position quickly and efficiently, which has been a difficult problem in EEG practical education. In addition, this band is expected to be applied widely by new clinical technologists in the clinical field. Nevertheless, more study will be required to verify the accuracy of the location of the attaching electrode.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.91-102
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• 2022

As virtual space activities become more common, human-virtual agents such as avatars are more frequently used instead of people, but the uncanny valley effect, in which people feel uncomfortable when they see artifacts that look similar to humans, is an obstacle. In this study, we explored the uncanny valley effect for celebrity avatars. We manipulated the degree of atypicality by adjusting the eye size in photos of celebrities, ordinary people, and their avatars and measured the intensity of the uncanny valley effect. As a result, the uncanny valley effect for celebrities and celebrity avatars appeared to be stronger than the effect for ordinary people. This result is consistent with previous findings that more robust facial representations are formed for familiar faces, making it easier to detect facial changes. However, with real faces of celebrities and ordinary people, as in previous studies, the higher the degree of atypicality, the greater the uncanny valley effect, but this result was not found for the avatar stimulus. This high degree of tolerance for atypicality in avatars seems to be caused by cartoon characters' tendency to have exaggerated facial features such as eyes, nose, and mouth. These results suggest that efforts to reduce the uncanny valley in the virtual space service using celebrity avatars are necessary.

• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.131-139
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• 2022

The purpose of this study was to insert a pedicle screw into a pig thoracic vertebrae, a general CT scan(Non MAR), and a thoracic axial image obtained with the Metallic Artifact Reduction for Orthopedic Implants (O-MAR) to reduce artifacts. The image obtained by reconstructing the algorithm (Standard, Soft, Bone, Detail) was used using the image J program. Signal to noise ratio(SNR) and contrast to noise ratio(CNR) were compared and analyzed by obtaining measured values based on the given equation. And this study was to investigate tube voltage and algorithm suitable for CT scan for thoracic pedicle screw insertion. As a result, when non-MAR was used, the soft algorithm showed the highest SNR and CNR at 80, 100, 120, and 140 kVp, On the other hand, when MAR was used, the standard algorithm showed the highest at 80 kVp, and the standard and soft algorithms showed similar values at 100 kVp. At 120 kVp, the Soft and Standard algorithms showed similar values, and at 140 kVp, the Soft algorithm showed the highest SNR and CNR. Therefore, when comparing Non-MAR and MAR, even if MAR was used, SNR and CNR did not increase in all algorithms according to the change in tube voltage. In conclusion, it is judged that it is advantageous to use the Soft algorithm at 80, 100, 120, and 140 kVp in Non MAR, the Standard algorithm at 80 and 100 kVp in MAR, and the Soft algorithm at 120 and 140 kVp. This study is expected to serve as an opportunity to further improve the quality of images by using selective tube voltage and algorithms as basic data to help evaluate images of pedicle screw CT scans in the future.