• Journal of Creative Information Culture
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• v.7 no.2
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• pp.99-109
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• 2021

As the era of the 4th industrial revolution began and the importance of software emerged, education also reflected this. Software education has already been provided in several countries, and Korea also started software education in the regular curriculum in 2019, when the 2015 revised curriculum was applied. This study attempted to present an improvement plan for revitalizing software education based on the feelings and difficulties of teachers who conducted software education for the first time in the practical education curriculum in elementary school. For the study, a survey was conducted on 96 teachers in charge of software education in elementary schools in 2019 with 36 questions related to personal competency, class operation method, textbooks and educational materials, class operation content, and educational environment. And three of them were interviewed. As a result improvements are needed, such as improving educational facilities and environment, revitalizing the development and dissemination of high-quality instructional materials, and expanding support for participatory training for teachers and teacher clubs.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.23-28
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• 2021

Through the 4^th industrial revolution, it is rapidly developing in various fields such as artificial intelligence, AR/VR, and big data, and software is at the center. In the field of education as well, the importance of integrated education to support the development of technology is being emphasized, and in order to compete in software technology, securing human resources for software development should be prioritize in domestic. However, unlike the hardware-centric society of the past, the role of software technology human resources is very important, and the reality is that they are discharging human resources that are far from the human resources image that companies need. In this paper, present an immersed education model for training AR software professionals, and based on this, propose an evaluation index that can grasp the quality of the program of the immersed AR education model. Through the AR education model, it is expected that the weaknesses and strengths of the model can be identified, and it can contribute to setting the direction for improvement of the education program.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.279-285
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• 2022

In this paper, as a liberal arts course for non-majors, we developed a basic practice case for convolutional neural networks, which is essential for designing a basic convolutional neural network course curriculum. The developed practice case focuses on understanding the working principle of the convolutional neural network and uses a spreadsheet to check the entire visualized process. The developed practice case consisted of generating supervised learning method image training data, implementing the input layer, convolution layer (convolutional layer), pooling layer, and output layer sequentially, and testing the performance of the convolutional neural network on new data. By extending the practice cases developed in this paper, the number of images to be recognized can be expanded, or basic practice cases can be made to create a convolutional neural network that increases the compression rate for high-quality images. Therefore, it can be said that the utility of this convolutional neural network basic practice case is high.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.287-299
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• 2022

The article deals with the formation of managerial competence of the future head of preschool education institution by means of information and communication technology as a prerequisite for his ability to act competently and objectively evaluate actions and understand the interaction of forms and content of preschool education. The article aimed to study the effectiveness of information and communication technologies in the formation of managerial competence of the future head of preschool education institution. To achieve the objectives, the methods of comparative and systematic analysis were used to compare different views on the problem under study, namely, the formation of managerial competence of the future head of preschool education institution by means of information and communication technologies. The authors of the article determined that the use of information and communication technologies in the preparation of future heads of preschool educational institutions is of great importance and is an indicator in the structure of managerial competence. The priority directions of the use of various software products for the study of the modern Ukrainian language, methods of teaching the Ukrainian language contribute to the intensification of learning material. It is noted that the current state of development of information technologies and their widespread use in education satisfies the requirements of the objectivity of the assessment obtained the quality of the control process of forming the managerial competence of the future leader in the context of the general problems of pre-school education. It is noted that the means of information and communication technologies play a leading role in creating new educational policies and projects, as they motivate the way of access to knowledge.

• Physical Therapy Rehabilitation Science
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• v.9 no.4
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• pp.318-323
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• 2020

Objective: Reacquisition of motor functions following stroke depends on interhemispheric neural connections. The intervention highlighted in the present case is an insight for augmenting motor recovery by stimulating the lesioned area and adjacent areas governing the motor behaviour of an individual. The purpose of this study was to determine the changes in the motor and cognitive outcomes through multi target stimulation of cortical areas by application of multichannel transcranial direct current stimulation (M-tDCS) in a stroke survivor. Design: A case report. Methods: The patient was a participant of a trial registered with the clinical trial registry of India (CTRI/2020/01/022998). The patient was intervened with M-tDCS over the left primary motor cortex i.e. C3 point and left dorsolateral prefrontal cortex i.e. F3 point with 0.5-2 mA intensity for the period of 20 minutes. SaeboFlex-assisted task-oriented training, functional electrical stimulation over the lower extremity (LE) to elicit dorsiflexion at the ankle and eversion of the foot, and conventional physiotherapy rehabilitation including a tailored exercise program were performed. Outcome assessment was done using the Fugl-Meyer assessment scale (FMA) for the upper and lower extremity (UE and LE), Montreal Cognitive Assessment (MOCA), Wisconsin Gait Scale (WGS) and the Stroke Specific Quality of Life (SSQOL) measures. Assessment was taken at Day 0, 15 and 30 post intervention. Results: Improvement was observed in all the outcome measures i.e FMA (UE and LE), MOCA, SSQOL and WGS across the span of 4 weeks. Conclusions: M-tDCS induced improvement in motor functions of the UE and LE, gait parameters and cognitive functions of the patient.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.801-812
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• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• Advances in Computational Design
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• v.7 no.2
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• pp.139-151
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• 2022

The article is devoted to the actual problem of studying the possibilities of implementing personalization of the user interface in accordance with the personality psychotypes. The psychological aspect of user interface design tools is studied and the correspondence of their application to the manifestations of personality psychotypes is established. The results of the distribu-tion of attention of users of these categories on the course page of the educational platform are presented and the distribution of attention in accordance with the focus on educational material is analyzed. Individual features and personal preferences regarding the used design tools are described, namely the use of accent colors in interface design, the application of the prin-ciples of typographic hierarchy, and so on. In accordance with this, the prospects for implementing personalization of the user interface of the educational platform are described. The results of the study allow us to state the relevance of developing and applying personalization of the user interface of an educational platform to improve learning outcomes in accordance with the psychological impact of individual design tools, and taking into account certain features of user categories. The research is devoted to the study of user attention concentration using heatmaps, in particular based on eyetreking technology, we will investigate the distribution of user attention on the course page of an educational platform Ta redistribution of atten-tion in accordance with certain categories of personality psychotypes. The results of the study can be used to rearrange the LMS Moodle interface according to the user's psychotype to achieve the best concentration on the training material. The obtained data are the basis for developing effective user interfaces for personalizing educational platforms to improve the quality of the education.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.683-694
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• 2020

Background and objective: Cancer is the number one cause of death in Korea, and it affects any part of the body regardless of gender and age. Forest healing is a treatment that maximizes the effect of treatment and improves the quality of life. This study aims to provide basic data for the development and implementation of differentiated forest healing programs for cancer patients based on the survey on their interest and needs for the programs. Methods: The subjects were those diagnosed with cancer from October 2018 to April 2019, and this study used 144 copies of the questionnaire retrieved. The sample size of this study (n = 144) was the appropriate size required by G-Power, and the collected responses were analyzed using SPSS 25.0. Results: In the frequency analysis on the interest in forest healing, 79.2% of the subjects had no experience participating in forest healing, but 87% were aware of it, and 82.6% showed the intention to participate in forest healing programs. This indicates that even though not many of them have experience participating in forest healing, they showed high interest and needs for participation. They preferred to participate in spring (29.9%) and fall (27.8%), in programs carried out for 1.5-2 hours in the morning on weekdays. Conclusion: This study has implications for the analysis on forest healing needs of cancer patients, and it is necessary to plan, develop, and implement differentiated forest healing programs that meet the needs of the cancer patients depending on their characteristics. There is also a need to plan forest healing program that can promote both psychological stability and physical health of cancer patients and verify and evaluate their effects based on specialized training of forest healing instructors.