• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• The Research Journal of the Costume Culture
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• v.30 no.3
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• pp.414-428
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• 2022

The goal of this study was to develop a curriculum based on practice and job competency, reflecting opinions on the required job competence of nail practitioners and professionals related to nail beauty. Through in-depth interviews with nail experts, the research focuses on developing nail beauty competency-based curriculum and curriculum profiles that reflect practitioners' needs of job competence in the field. In-depth interviews with 11 field experts and surveys of 154 people were conducted to develop a competency-based curriculum for beginner nail hairdressers. The results of this study show that the existing 38 National Competency Standards (NCS) job competencies were reduced to 21 job competencies. In addition, based on the common opinions of experts who reflect the current trend, two tasks on "eyelashes" and "waxing" were added, and they were modified and supplemented with 23 core competencies. The development of a competency-based curriculum and educational programs for nail beauty was performed based on the requirements of the core competencies investigated and the development of a systematic map for the core competencies of beginner nail technicians and hairdressers. In conclusion, the need for professional education and training for nail hairdressers is growing, and it can be seen that a curriculum building multi-faceted abilities is needed for their qualifications as experts. This study found that it is necessary to develop interpersonal communication skills that include marketing elements other than practical skills such as personality and customer response methods in the nail beauty curriculum.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1826-1831
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• 2022

Rapid Aging Society demands the transformation of medical paradigm of diagnosis and treatment towards prevention and management. This paper explores the norm and development of digital health care, focusing on Busan Metropolitan City. Digital health care which combines new ICT technology and medical technology is predictive, preventive, personalized and participatory; and suggests alternative to solve the problem of demographic changes and increasing social cost of medical welfare. Community Health Center in Busan is unique one based in the minimum community of collecting data from self-leading health management. Digital transformation using basic health data and social information can build preventive care system in the community. Easy access leads community center to test bed of developing new technology, as a living lab. In order to use the newly developed goods and service effectively, user-participatory test is nicessary. Finally community nurse and activists can specify health-welfare converged service through digital transformation empowerment training.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.890-895
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• 2021

Recently, the "Act of Serious Disasters, etc" was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it.

• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.341-353
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• 2021

Background and objective: As the agricultural industry becomes a more convergent industry, it is believed that the demand for human resources by companies will change. Therefore, a survey was conducted to investigate the human resources required by agriculture companies. Methods: In the survey on 77 agriculture companies, 98.7% of respondents answered that new employees with a college degree needed additional training to adapt to practical affairs. Results: The first priority of education was "community spirit" (22.1%) and the second priority was "convergence capability" (15.6%). The most important educational goal desired by agricultural companies was "cultivating human resources with community spirit and ethical judgment", followed by "cultivating human resources with serious communication and problem-solving skills", and "cultivating human resources with scientific thinking and unique creative imagination." Sub-competencies that companies want agricultural colleges to strengthen were "community spirit" 4.32(SD=0.96), "desirable values" 4.30 (SD = 1.05), "sympathy" 4.28 (SD = 0.95), "convergence capability" 4.16 (SD = 0.88), "creativity" 4.11 (SD = 0.83), "civic spirit" 4.10 (SD = 0.91), and "rational/critical thinking" 3.94 (SD = 1.04). There was a significant difference in sub-competencies that require reinforcement depending on the number of full-time employees. "Creativity" was most necessary in companies with less than 3 employees (4.39), and 4~7 employees (4.33), and "aesthetics"" in companies with less than 3 employees (3.94), and 4-7 employees (3.61) "Civic spirit" was most necessary in companies with 31 employees or more (4.33). Conclusion: The most important educational goal desired by companies was "cultivating human resources with community spirit and ethical judgment".

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Korean Journal of Acupuncture
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• v.39 no.3
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• pp.71-83
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• 2022

Objectives : Instructional design is the systematic approach to the Analysis, Design, Development, Implementation, and Evaluation of learning materials and activities. We aimed to apply the rapid prototyping to instructional systems design (RPISD) in meridianology laboratory, a subject in which students train acupuncture to develop lesson plan. Methods : The needs of the stakeholders including client, subject matter expert and students were analyzed using the performance needs analysis model. Task analysis was implemented by observation and interview. First prototype was drafted and implemented in meridianology laboratory class once. The second prototype was modified from the first, by usability evaluation of the stakeholders. Results : The client requested an electronically documented manual to improve the quality of acupuncture training. The learner requested an extension of practice time and detailed practice guidelines. The main problems of students' performance were some cases of violation of clean needle technique, the lack of communication between the operator and recipient in direct, and lack of confidence in their own performance. Stakeholders were generally satisfied with the proposed first prototype. Second prototype of lesson plan was produced by modifying some contents. Conclusions : A lesson plan was developed by applying the systematic RPISD model. It is expected that the developed instructional design may contribute to the quality improvement of meridianology laboratory education.