• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• Composites Research
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• v.34 no.6
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• pp.394-399
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• 2021

Piecewise Integrated Composite (PIC) is a new concept to design composite structures of multiple stacking angles both for in-plane direction and through the thickness direction in order to improve stiffness and strength. In the present study, PIC beam was suggested based on 3D training data instead of 2D data, which did offer a limited behavior of beam characteristics, with enhancing the stiffness accompanied by reduced tip deformation. Generally training data were observed from the designated reference finite elements, and preliminary FE analysis was conducted with respect to regularly distributed reference elements. Also triaxiality values for each element were obtained in order to categorize the loading state, i.e. tensile, compressive or shear. The main FE analysis was conducted to predict the mechanical characteristics of the PIC beam.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.19-36
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• 2022

Purpose: The purpose of this study is to analyze the effects of COVID 19. The effects of COVID 19 are grouped into 5; economic stress, mental stress, health stress, task concern, self-confidence. We introduce the concept of personal ambidexterity that is necessary power for cabin crews to provide appropriate and efficient service to passengers. Ambidexterity consists of exploiting existing resources to sustain and exploring the new including method of performing task, customer, market etc. The former is necessary to maintain present condition while the latter is necessary to prepare for the future. Also motive is considered as a stimulating factor for task. Previous studies show that motive affects ambidexterity and we try to analyze whether COVID 19 effects influence this relationship. Research design, data, and methodology: Considering the relationship between the variables, we designed to measure the influence of the effects of COVID 19 by analyzing the moderating effects of them. For empirical analysis we distributed survey questionnaire and collected. Total of 361 samples are used fo the analysis. For analysis program, SPSS version 23 was used. Regression analysis and moderating effect analysis were conducted. Results: Study results show that first, the variables of economic stress, mental stress, health stress, task concern, self confidence affects personal ambidexterity and service provision. Also ambidexterity affects service provision significantly. Among COVID 19 effects, economic stress, task concern, and self confidence has moderating effects. On the other hand, new work environment does not have moderating effect. Conclusions: In conclusion, the effects of COVID 19 are wide and various. Among them the most serious effect is that COVID 19 is depriving workers of self confidence and passion toward the work. To remedy stresses and restore self confidence and passion, each worker should make his/her own efforts, such as, learning more to become more competitive, also firms should do make efforts to protect employees and to rebuild trust between firm and employees in every respect. Especially firms should realize that economic stress can be treated by economic compensation as the situation turns to normal but trust as well as self confidence and passion is not easy to restore.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.19-27
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• 2022

Graduate well-being is foundational to academic success, and they are becoming more and more vulnerable. This is as they suffer from mental health challenges like anxiety and depression at rates six times higher than the general population. When the nature of their educational experience changes, such as when they had to stay in their homes during the COVID-19 pandemic, the stress on their mental health increases. The number of cases of emotional wellness among university students is considered a public health problem, but these young people often do not seek appropriate treatment. This study, therefore, aims to identify the influence of health behavior factors on graduate emotional wellness. This study used a questionnaire with a cross-sectional survey design. Questionnaires were distributed online to graduates from selected Private and Public Higher Education Institutions in Malaysia. The Partial Least Square Equation Model (PLS-SEM) was used to analyze the results of the study. Overall findings indicate that the health behavior factors have a significant influence on graduate emotional wellness. The findings from this study will benefit the management, academics, counselors, and other entities, including the Students' Representative Council, in identifying ways to improve services and upgrade the necessary facilities to enhance the graduate's emotional wellness.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.225-232
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• 2022

Recent development in information and communication technology has been beneficial to many, but at the same time, malicious attack attempts are also increasing through vulnerabilities in new programs. Among malicious attacks, malware operate in various ways and is distributed to people in new ways every time, and to solve this malware, it is necessary to quickly analyze and provide defense techniques. If new malware can be classified into the same type of malware, malware has similar behavioral characteristics, so they can provide defense techniques for new malware using analyzed malware. Therefore, there is a need for a solution to this because the method of accurately and quickly classifying malware and the number of data may not be uniform for each family of analyzed malware. This paper proposes a system that combines image preprocessing and ensemble techniques to increase accuracy in imbalanced data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.21-29
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• 2022

Based on the importance of dialect research, preservation, and communication, this paper conducted a study on machine translation of Korean dialects for dialect users who may be marginalized. For the dialect data used, AIHUB dialect data distributed based on the highest administrative district was used. We propose a many-to-one dialect machine translation that promotes the efficiency of model distribution and modeling research to improve the performance of the dialect machine translation by applying Copy mechanism. This paper evaluates the performance of the one-to-one model and the many-to-one model as a BLEU score, and analyzes the performance of the many-to-one model in the Korean dialect from a linguistic perspective. The performance improvement of the one-to-one machine translation by applying the methodology proposed in this paper and the significant high performance of the many-to-one machine translation were derived.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.927-934
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• 2023

With the recent growth of the companion animal market, various social issues related to companion animals have also come to the forefront. Notable problems include incidents of dog bites, the challenge of managing abandoned companion animals, euthanasia, animal abuse, and more. As potential solutions, a variety of training programs such as companion animal-focused broadcasts and educational apps are being offered. However, these options might not be very effective for novice caretakers who are uncertain about what to prioritize in training. While training apps that are relatively easy to access have been widely distributed, apps that allow users to directly engage in training and learn through hands-on experience are still insufficient. In this paper, we propose a more efficient AR-based mobile app for companion animal training, utilizing the Unity engine. The results of usability evaluations indicated increased user engagement due to the inclusion of elements that were previously absent. Moreover, training immersion was enhanced, leading to improved learning outcomes. With further development and subsequent verification and production, we anticipate that this app could become an effective training tool for novice caretakers planning to adopt companion animals, as well as for experienced caretakers.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.59-76
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• 2024

The prominent role accorded to social media in the academic community for research, teaching and learning revolves around its significance among users. Social media offers a platform for individuals to engage with and share perceptions relating to different disciplines. This current research was conducted to investigate the level of awareness and frequency of social media technology use among postgraduate students of Library and Information Science in Nigerian universities. The descriptive survey design was used for the study. Structured questionnaires were used to collect data from 919 library and information science (LIS) postgraduate students in the universities. In all, 742 copies out of the 919 distributed were returned and found usable, thereby making the return rate to be 81%. Data collected were analysed using mean and standard deviation. The study revealed that the LIS postgraduate students frequently use social media such as Wikipedia (x=3.94>3.50), Instagram (x=3.86>3.50), Facebook (x=3.85>3.50), Zoom ($\overline{x}$=3.78>3.50), LinkedIn (x=3.69>3.50), YouTube ($\overline{x}$=3.54>3.50), Twitter (x=3.52>3.50). The study established that students use social media tools for their personal, professional and research activities. The study also found that the level of awareness and use of social media by the students was high. The study recommended that the use of social media should be incorporated into the LIS curriculum including training sessions for the students on how to use the media effectively.