• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.165-172
• /
• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• The Korean Journal of Food And Nutrition
• /
• v.34 no.5
• /
• pp.477-486
• /
• 2021

To utilize Malus pruniforia Borkh. as a functional material, cold-water (CW), hot-water (HW), and 70% ethanol (EtOH) extracts were prepared, and their antioxidant and anti-inflammatory activities were compared. The antioxidant activity of the HW extract evaluated by ABTS and DPPH radical scavenging and FRAP activity was significantly effective. The total polyphenol content of the HW extract was also higher by 15.5±0.7 mg GAE/g extract compared to other extracts. The EtOH extract showed significantly decreased TNF-α (39.8%), IL-6 (65.5%), and NO (34.9%) levels in RAW 264.7 cells compared to the LPS-induced control group. The levels of IL-6 (21.1%) and IL-8 (19.3%) were significantly decreased by treatment of EtOH extract in HaCaT keratinocytes induced with TNF-α and IFN-γ. The UHPLC-MS results indicated that the EtOH extract might have chlorogenic acid and phlorizin as the major compounds. This was validated using HPLC-DAD, which showed that the EtOH extract had higher levels of chlorogenic acid and phlorizin (1,185±58 and 470±10 ㎍/g extract, respectively). In conclusion, the present study suggested that the anti-inflammatory activity of the EtOH extract was more effective than the CW and HW extracts, and chlorogenic acid and phlorizin could be used as indicator compounds and functional substances.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.4
• /
• pp.101-111
• /
• 2019

In distance learning, quality of service is an important part of improving customer satisfaction and customer loyalty. However, in verifying the effectiveness of remote education service quality, it has been researched based on fragmentary effects on remote education service quality, and the effect study on the specific target is insufficient. In this study, the effects of remote education service factors on customer satisfaction and customer loyalty were analyzed in the previous study and among job seekers. The survey was conducted from March 2019 and 258 samples of job seekers who experienced remote education were used for empirical analysis. As a result of the analysis, typology, problem solving, interaction, information serviceability, and convenience had a positive effect on customer satisfaction, and satisfaction had a significant influence on customer loyalty. In addition, it was analyzed that characterization, problem-solving, interaction, information serviceability, convenience and customer loyalty were affected in the verification of the mediated effects of satisfaction. In response, the implications of this study were derived from practical research on customer satisfaction and loyalty of educational companies related to eduTech, where education and ICT (Information Communication Technology) were integrated during the 4th Industrial Revolution, which suggested that the quality of a company's remote education service affected customer satisfaction and customer loyalty to entrepreneurs and marketers in the education company's start-up and marketing process. Further, further research will be needed in other areas as well as in the areas of employment education to verify the importance of service quality and assess the various effects.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.5
• /
• pp.467-474
• /
• 2019

In this paper, we propose a novel method to improve FPS while maintaining the accuracy of YOLO v2 model in NVIDIA Jetson TX1. In general, in order to reduce the amount of computation, a conversion to an integer operation or reducing the depth of a network have been used. However, the accuracy of recognition can be deteriorated. So, we use methods to reduce computation and memory consumption through adjustment of the filter size and integrated computation of the network The first method is to replace the $3{\times}3$ filter with a $1{\times}1$ filter, which reduces the number of parameters to one-ninth. The second method is to reduce the amount of computation through CBR (Convolution-Add Bias-Relu) among the inference acceleration functions of TensorRT, and the last method is to reduce memory consumption by integrating repeated layers using TensorRT. For the simulation results, although the accuracy is decreased by 1% compared to the existing YOLO v2 model, the FPS has been improved from the existing 3.9 FPS to 11 FPS.

• Journal of Digital Convergence
• /
• v.17 no.9
• /
• pp.135-145
• /
• 2019

Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

• Journal of Science Education
• /
• v.43 no.1
• /
• pp.136-156
• /
• 2019

This study examined the effects of STEAM program on preservice science teachers' communication competency and further explored their experiences of and reflection on STEAM program. The study design is one group pretest-posttest with mixed methodology using both quantitative and qualitative data. The STEAM program consists of three stages: introduction of STEAM, participation in STEAM activities, and reflection on the STEAM program. The preservice science teachers improved their communication competency after the STEAM program (p < .01). The preservice science teachers represented statistically higher scores on the three subscales of communication competency: Interpretation ability, self-presenting ability, and understanding others' viewpoints. In addition, the preservice science teachers reflected on their STEAM experiences. During the first stage of 'Presentation of the Problem Situation,' the preservice science teachers mentioned that they roused their curiosity due to everyday experience-related, social issues or present issues. In the stage of 'Creative Design,' the preservice science teachers mentioned that they selected the final idea through mutual consent of the members, the practical possibility of everyday life, the previous experience-based decisions, or persuasive power. Further, about 87.5% of preservice science teachers mentioned that they were fully engaged in the 'Emotional Learning' stages due to the application of integrated thinking, everyday related issues, and communication among group members. About 85% of the preservice science teachers mentioned that they could challenge new problems in future situations.

• Journal of the Korea Convergence Society
• /
• v.10 no.4
• /
• pp.165-173
• /
• 2019

Recently, a variety of programs for elementary school students that utilize play in their curricula are supported. In this study, we are trying to draw up ways to activate play education based on the elements necessary for the play education to be effectively provided on the field and the current operational status. In order to achieve the research goal, nine participants of play experts and parents were selected for the focus group interview (FGI). The FGI consist of five questions: (1) opinions on the establishment and joint operation of the organization to support play and parents' education; (2) opinions based on experience in participating in existing training programs; (3) activation plan of play & education program; (4) competencies required by members of the organization; (5) evaluation of program for quality improvement. Through the FGI survey, we drew ideas for the operation of play & education programs to promote positive growth and support systemic programs of both preschoolers and elementary students. In order for play & education to be active in the field of education, a center where play & education and parents' education can be conducted at the same time should be established and operated so that the education can be integrated with play. Based on these findings, we proposed follow-up research in the direction of achieving specific goals and enhancing the quality of play education.

• Journal of Korean Elementary Science Education
• /
• v.37 no.4
• /
• pp.402-415
• /
• 2018

This study aims to inquire into the U. S. elementary school teachers' storylines for science teaching methods, scientific knowledge and interactions with students. As research subjects, two American elementary school teachers, named Christina and Melissa, were selected. As test tools, this study adopted Storyline Test and semi-structured interviews. Firstly, in regard to the science teaching method, Christina evaluated that she gradually improved her science teaching skills up to positive 6 points, starting from 2 points in the first year of teaching career, while Melissa sustained a stable state with 5 points in the 9th year of teaching career, starting from 1 point in the first year of teaching career. It was found that both the teachers had more confidence in their science teaching methods by participating in various training programs. Secondly, Christina evaluated her scientific knowledge in the first year of teaching career as 4 points, but evaluated her present scientific knowledge as stable as 6 points since she started studying teaching materials actively, discussing with other teachers and having more ability of application through science class integrated with other school subjects, in the 7th year of her teaching career. On the other hand, Melissa evaluated her scientific knowledge in the first year of teaching career as 1 point since she did not exactly know what to teach elementary school students, but in the 6th year of teaching career, she sustained a stable state with points through joint-activities with other teachers. It was found that chances to research with other teachers had important effect on both the teachers' confidence in scientific knowledge. Thirdly, in regard to interactions with students in science class, Christina said that she did not have any interaction with students when instructing inquiry activities in the first year of teaching career, but since the 10th year of her teaching career, she had sustained a stable state with 6 points through active interaction with students, by leading learning projects and science competitive exhibitions, etc. On the other hand, Melissa evaluated her interaction with students in the first year of teaching career as 1 point because her class was reading-oriented, but since the 9th year of teaching career, she had sustained a stable state with 6 points so far, by developing inquiry activity strategies to improve interaction with students. Overall, it was found that inquiry activities played a central role in improving both the teachers' interaction with students.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.297-304
• /
• 2021

This study aims not only to access the visual thought-oriented approach that has been implemented in established art therapy and education but also to integrate language education and therapeutic approach to support the development of school-age children. Thus, text mining technique was applied to search for areas where different areas of language and art can be integrated. This research was conducted in accordance with the procedure of basic research, preliminary DB construction, text screening, DB pre-processing and confirmation, stop-words removing, text mining analysis and the deduction about the convergent areas. These results demonstrated that this study draws convergence areas related to regional, communication, and learning functions, areas related to problem solving and sensory organs, areas related to art and intelligence, areas related to information and communication, areas related to home and disability, topics, conceptualization, peer-related areas, integration, reorganization, attitudes. In conclusion, this study is meaningful in that it established a framework for designing an activity-centered convergence program of art and language in the future and attempted a holistic approach to support child development.