• Trans-
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• v.14
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• pp.35-63
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• 2023

Parkinson's disease is a degenerative neurological disease that affects even basic daily life movements due to impairment of body function caused by a lack of dopamine, which is charge of the body movement. Presently, it is hard to cure Parkinson's disease entirely with medical technology, so movement therapy as a solution to delay and prevent disease is getting more attention. Therefore, this study aims at desiging and disseminating a body movement program that concentrates on individual self-care and balacing the state of body and mind by applying the Feldenkrais Method® to patients with Parkinson's disease. The Feldenkrais Method® is a mind-body perceptual learning method using body movements. It is a methodology that re-educates the nervous system by connecting the brain and behavior as a function of neuroplasticity. In this study, the body movement program developed and verified by the researcher was modified and supplemented with a focus on the self-awareness of the Feldenkrais Method®. A 24-session physical exercise program was composed of 5 stages to improve the self-management ability of patients with Parkinson's disease. The stages include self-awareness, self-observation, self-organization, self-control, and self-care. The overall changes recognize one's condition and improve one's ability to detect modifications in the internal sense and external environment. In conclusion, the body movement program improves the body movement program improves mental and physical functions and self-care for Parkinson's disease patients through the Feldenkrais method. The availability of the program's on-site applicability remains a follow-up task. Furthermore, it is necessary to establish a systematic structure to spread it more widely through convergent cooperation with the scientific field applied with metaverse as a reference for the wellness of the elderly.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.605-614
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• 2023

In In the era of digital transition, AI-based personalized services are emerging in the field of education. This research aims to examine the development strategies for implementing AI-based learning services in school. Focusing on AI-based math learning service "Math Cell" developed by i-Scream Edu, this study surveyed the functional requirements from the perspective of an educator. The results were analyzed for importance and suitability using IPA, and expert opinions were surveyed to explore specific development directions for the service. Consequently, importance in all areas such as diagnosis, learning, evaluation, and management averaged 4.82 and performance averaged 4.56, showing excellent results in most questions, and in particular, importance was higher than performance. Among certain detailed functions, concept learning, customized task presentation, evaluation result analysis function, dashboard-related functions, and learning materials in the dashboard were not intuitive for students to understand and had to be supplemented. This study provides meaningful insights by summarizing expert opinions on AI-based personalized mathematics learning services, thereby contributing to the exploration of the development strategies for "Math Cell".

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.93-98
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• 2023

In paddy rice farming, water management is a critical task. To suppress weed emergence during the early stages of growth, fields are deeply flooded, and after transplantation, the water level is reduced to promote rooting and stimulate stem generation. Later, water is drained to prevent the production of sterile tillers. The adequacy of water supply is influenced by various factors such as field location, irrigation channels, soil conditions, and weather, requiring farmers to frequently check water levels and control the ingress and egress of water. This effort increases if the fields are scattered in remote locations. Automated irrigation systems have been considered to reduce labor and improve productivity. However, the net income from rice production in 2022 was about KRW 320,000/10a on average, making it financially unfeasible to implement high-cost devices or construct new infrastructure. This study focused on developing an IoT-Based irrigation valve that can be easily integrated into existing agricultural infrastructure without additional construction. The research was carried out in three main areas: Firstly, an irrigation valve was designed for quick and easy installation on existing agricultural pipes. Secondly, a power circuit was developed to connect a low-power Cat M1 communication modem with an Arduino Nano board for remote operation. Thirdly, a cloud-based platform was used to set up a server and database environment and create a web interface that users can easily access.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.431-436
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• 2023

Recently the incorporation of artificial intelligence approaches in the field of software engineering has been one of the big topics. In the world, there are actively studying in two directions: 1) software engineering for artificial intelligence and 2) artificial intelligence for software engineering. We attempt to apply artificial intelligence to software engineering to identify and refactor bad code module areas. To learn the patterns of bad code elements well, we must have many datasets with bad code elements labeled correctly for artificial intelligence in this task. The current problems have insufficient datasets for learning and can not guarantee the accuracy of the datasets that we collected. To solve this problem, when collecting code data, bad code data is collected only for code module areas with high-complexity, not the entire code. We propose a method for exploring common weakness enumeration by learning the collected dataset based on transfer learning of the CodeBERT model. The CodeBERT model learns the corresponding dataset more about common weakness patterns in code. With this approach, we expect to identify common weakness patterns more accurately better than one in traditional software engineering.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.145-156
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• 2024

In an era where construction projects are becoming increasingly large and complex, the Korean construction industry faces the challenge of implementing systematic and cohesive schedule management practices. This study initially delineates the concept of schedule management capabilities, systematically categorizing them into five distinct domains: law and regulations, requirements of project owners, capabilities of professionals and organizations, task capability, and the adoption of smart construction technologies for schedule management. Through a survey targeting industry professionals, this research assesses the perceived importance and the actual proficiency level in schedule management across these categories, employing an Importance Performance Analysis(IPA) to scrutinize these capabilities. The findings underscore the acknowledged significance of diverse aspects of schedule management, yet reveal discrepancies between the current proficiency levels and their perceived importance, pinpointing areas necessitating enhancement. Critical improvement needs identified encompass the planning of budgets for schedule management, development of regulations for assessing construction periods, deployment of specialized on-site staff for schedule management, and investment in advanced schedule management software solutions. Consequently, this study offers a nuanced analysis and strategic insights for enhancing schedule management practices, aiming to facilitate their effective implementation in the field.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.41-55
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• 2023

In order to secure sustainable competitiveness of startups, business model innovation is an important task to achieve competitive advantage by transforming the various elements that make up the business model. This study conducted a multi-case analysis study on leading smart farm companies around the world using an analysis framework based on business model theory. Through this, we sought to identify business model types and their constituent elements. For this, 19 companies were selected from the list of top 10 investment startups of the year for the past three years published by Agfunder, a global investment research company specializing in AgTech. Then data collection and analysis of the company cases were conducted according to the case study protocol. As a result of the study, the business model types were analyzed into four types: large-scale centralized production model, medium-to-large local distributed production model, small-scale hyperlocal modular FaaS model, and small-scale hyperlocal turnkey solution supply model. A comparative analysis was conducted on five business model components for each type, and strategic implications were derived through this. This study is expected to contribute to improving the competitiveness of domestic smart farm startups and diversifying their strategies by identifying the business models of overseas leading companies in the smart farm field using an academic analysis framework.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.79-101
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• 2024

This study aims to investigate the impact of hotel marketing agility on employee job crafting and service innovative behavior in the context of the uncertainties faced by the Korean hotel industry amidst a rapidly changing business environment. In the post-COVID era, hotel companies need to quickly detect market trends and respond flexibly to secure a competitive advantage and promote sustainable growth. Given the high proportion of face-to-face services in the hotel industry, marketing agility is emerging as a core competency to effectively cope with changing customer demands and market dynamics. However, an organization's agile marketing capabilities only take on true meaning when frontline employees voluntarily redesign their jobs and lead service process innovations. In this regard, job crafting, which refers to employees' active redefinition and modification of their job boundaries, can serve as a link between organizational agility and individual innovative behavior. By empirically analyzing the structural relationship between marketing agility, job crafting, and service innovative behavior in the hotel context, this study applies the concept of agility to the field of human resource management and provides new insights into the antecedents and outcomes of job crafting. The results show that hotel responsiveness has a significant positive impact on all three dimensions of job crafting. In the case of speed and flexibility, they had a significant positive effect on task and relational crafting, but the effect of speed on cognitive crafting was not significant. The implications of the study suggest that hotels' rapid decision-making and execution may actually constrain employees' cognitive crafting activities, such as reflecting on and reinterpreting the meaning and identity of their work. Furthermore, it is expected to provide meaningful insights for hotel managers facing environmental upheavals to seek practical measures to enhance agility and innovativeness.