• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.371-376
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• 2022

The purpose of this study is to develop a walking assistance system with mobility support and life support functions so that the elderly with reduced physical ability and patients who are uncomfortable when moving can move comfortably indoors and outdoors, and help social life. An obstacle recognition sensor module that can be applied indoors and outdoors is installed on a lightweight walking aid. The purpose of this study is to develop a walking assistance system with mobility support and life support functions so that the elderly with reduced physical ability and patients who are uncomfortable when moving can move comfortably indoors and outdoors, and help social life. An obstacle recognition sensor module that can be applied indoors and outdoors is installed on a lightweight walking aid. It is a system structure of an integrated actuator and brake system that can avoid obstacles in consideration of the safety of the elderly and is easy to install on the device. In this paper, the design of a lightweight walking aid was designed to increase the convenience of the socially disadvantaged and the elderly with reduced exercise ability. In addition, in order to overcome the disadvantage of being inconvenient to use indoors due to the noise and vibration of the motor during operation, an In-Wheel type motor is applied to develop and apply a low noise, low vibration and high efficiency drive system.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.227-236
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• 2021

The purpose of this study is to promote the efficient operation of the Home visit nursing care services by identifying the perception and experience of visiting nurses at the community center. To this end, a methodological triangle study method was applied to visiting nurses. As a result of analysis the delivery system of the home visit nursing care services, the perception and participation experience of nurses, it was found that it is necessary to supplement the program standards such as the number of visiting target, health assesment, and information system for the efficiency of the visit. It is suggested that policy alternatives should be developed by sharing the major issues raised in this study.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.182-187
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• 2022

This study aims to study non-rechargeable wrist-type smart bands for those vulnerable to the digital environment. The transition to the digital age means improving the efficiency of human life and the convenience of management. In the digital age, it can be a very convenient infrastructure for the digital generation, but otherwise, it can cause inconvenience. COVID-19 is spreading non-face-to-face culture. The reality is that the vulnerable are complaining of discomfort in non-face-to-face culture. The core of the digital environment is smartphones. Digital life is spreading around smartphones. Technology that drives the digital environment is the core technology of the Fourth Industrial Revolution. The technologies are lot, big data, Blockchain, Smart Mobility, and AI. Related technologies based on these technologies include digital ID cards, digital keys, and nfc technologies. Non-rechargeable wrist-type smart bands based on related technologies can be conceptualized. Through these technologies, blind people can easily access books and manage their ID cards conveniently and efficiently. In particular, access authentication is required wherever you go due to COVID-19, which can be used as a useful tool for the elderly who feel uncomfortable using smartphones. It can also eliminate the inconvenience of the elderly finding or losing their keys.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.373-380
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• 2022

This study explored the effectiveness of virtual reality education interventions for nursing students in adult nursing practicum. This systematic review extracted intervention methods, research topics, outcome variables, and evidence synthesis of effectiveness. Seven studies were extracted from the databases of PubMed, Cochrane Library, EMBASE, and RISS. This study showed effects on knowledge, performance, attitude, critical thinking, self-efficacy, information assessment ability, problem-solving ability, self-confidence, and efficiency of nursing students. Therefore, virtual reality educational intervention contribute to enhance the competencies in adult nursing practicum.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.51-60
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• 2022

Cruciferous vegetables are rich in biologically active compounds such as glucosinolates and have various health benefits. Among these vegetables, Kimchi cabbage (Brassica rapa L. ssp. Pekinensis) is one of the most popular leafy vegetables due to the presence of the highest amounts of numerous vital phytonutrients, minerals, vitamins, and antioxidants. This study aims to investigate the effects of six cultivars (Chundong 102, Asia No Rang Mini, Hwimori Gold, Asia Seoul, Wol Dong Chun Chae, and Asia Bbu Ri) on hairy root induction and glucosinolate biosynthesis in the hairy root cultures of Kimchi cabbage. Seven different glucosinolates, in this case sinigrin, gluconapin, glucoerucin, glucobrassicin, 4-methoxyglucobrassicin, gluconasturtiin, and neoglucobrassicin, were detected in the hairy root cultures of Kimchi cabbage. Among the different cultivars, Asia No Rang Mini was the most promising candidate for hairy root stimulation, as it achieved the highest values for the growth rate, root number, root length, transformation efficiency, and total glucosinolate content. Overall, the Asia No Rang Mini cultivar of Kimchi cabbage performed best as a promising cultivar hairy root culture for glucosinolate production.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• The Research Journal of the Costume Culture
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• v.31 no.4
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• pp.452-466
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• 2023

This study focused on how retail tech promotes differentiated customer experiences in offline fashion stores. The purpose of this study is to determine the effects of the characteristics of fashion retail tech stores on consumers' flow and satisfaction. We surveyed Koreans aged 10 to 50 who had experienced offline fashion retail tech stores. The survey was conducted from April 28, 2023, to May 21, 2023. The total number of survey respondents was 200. The quantitative data collected through questionnaires was analyzed using SPSS 25.0. To reveal the effects of fashion retail tech store characteristics on consumer's flow and satisfaction, frequency analysis, we conducted frequency analysis, factor analysis, reliability analysis, correlation analysis, and regression analysis. The results of this study, figured out that fashion retail tech store's characteristics, including playfulness, efficiency, interaction, and information provision, have a significant impact on behavior flow, emotional flow, and satisfaction. As a result of analyzing the influence of consumers' flow led to satisfaction, it was confirmed that emotional flow positively influenced satisfaction, but behavioral flow had no meaningful effect on satisfaction. The results of our study can be used to make a successful marketing strategy and can serve as foundational data for consumer research on retail-tech-applied offline fashion stores.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.569-574
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• 2023

For maintenance of concrete structures, it is necessary to identify and maintain various defects. With the current method, there are problems with efficiency, safety, and reliability when inspecting large-scale social infrastructure, so it is necessary to introduce a new inspection method. Recently, with the development of deep learning technology for images, concrete defect classification research is being actively conducted. However, studies on contamination and spalling other than cracks are limited. In this study, a variety of concrete defect type classification models were developed through transfer learning on a pre-learned deep learning model, factors that reduce accuracy were derived, and future development directions were presented. This is expected to be highly utilized in the field of concrete maintenance in the future.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.309-316
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• 2024

Due to rapid socio-economic development and environmental changes, particularly autism spectrum disorder (ASD) in the context of Attention Deficit Hyperactivity Disorder (ADHD) and high-functioning autism, has become a significant social issue. This issue is increasingly recognized from a societal perspective rather than just an individual or family problem. But there remains a lack of information in frontline education. Traditionally, treatment for ASD has been conducted in specialized institutions, or by professional doctors, therapists, and counselors. There are still several challenges such as 1) accessibility to hospitals and transportation for children with ASD, 2) the maturity and competence of therapists, and 3) the lack of appropriate educational content. To solve these problems, we propose a supplementary 3D educational game process for children with high-functioning autism that utilize speech recognition technology and games designed for continuous and repetitive learning. Our proposed game content can be used at home, which incorporates Speech-To-Text (STT) technology and mini-games to help children indirectly experience and learn to handle unexpected real-life situations. With this approach, we will expect that the children can develop social skills and enhance the efficiency of their treatment.