• Korean Journal of Occupational Health Nursing
• /
• v.28 no.4
• /
• pp.208-220
• /
• 2019

Purpose: This study aimed to determine health problems experienced by workers in special employment relationships (WSER) and identify coping strategies used when such problems occur. Methods: This qualitative study used the focus group interview method. Thirteen study participants included five home-visit tutors, five insurance salespersons, and three credit card recruiters. The interviews were conducted from November 2018 through January 2019, with each occupational group interview lasting about 2 hours. Analysis based on phenomenological research was independently performed by two researchers. Results: Most participants had common health problems involving vocal cord symptoms, and stress related to emotional labor and traffic accidents. The unique health problems included cystitis, musculoskeletal, and digestive symptoms in home-visit tutors; reduced vision and hearing in insurance salespersons; and mental distress in credit card recruiters. There was no protection system for their health coverage, and the company emphasized their self-employed status to avoid taking responsibility for them. Twelve participants did not purchase occupational accident insurance owing to both not having adequate information and economic burden concerning premium status. Conclusion: WSER experienced both physical and mental health problems. These problems were caused by their unstable employment status, and the social security system for their coverage being non-functioning.

• Journal of Internet Computing and Services
• /
• v.20 no.5
• /
• pp.19-26
• /
• 2019

Recently, the importance of the internet of things (IoT) education has been emphasized due to the progress of research on IoT technology. Therefore, universities require an efficient IoT course. In this paper, we propose an undergraduate IoT course using the Bluetooth function of smartphone and Arduino kit. The proposed embedded IoT class uses the Bluetooth capabilities of the smartphone to connect Arduino and activate various sensors to encourage students to become interested in the class. According to students' midterm and final exam scores, students programming skills have been improved since students' projects were in progress during the course. Further, according to students' survey, the proposed IoT class is very effective in understanding the embedded IoT and 75% of the students satisfied with the proposed course.

• Journal of the Korean Chemical Society
• /
• v.65 no.1
• /
• pp.25-36
• /
• 2021

This study aimed to analyze achievement characteristics of Korean students in the results of PISA 2018 science domain. To this end, the characteristics of PISA 2009 to PISA 2018 science were analyzed in terms of the percentage of each performance level and the ratio of male and female by achievement level; in addition, the percentage of correct answers by framework subscale was compared with PISA 2015. The results showed that Korea has a higher percentage of students at the lower level of achievement as compared to the high-ranking countries of PISA, and the ratio of students at the higher level of achievement was lower. On average, the difference in achievement between boys and girls was negligible; however, but at the higher achievement level, the ratio of boys continued to be higher than that of girls. In addition, in the PISA science framework, the percentage of correct answers of the questions corresponding to 'personal' of 'contexts', 'evaluate and design scientific enquiry' of 'competencies', 'epistemic' of 'knowledge', and 'high' of 'cognitive demand' increased; similarly, and achievement improved as compared to PISA 2015. Based on these results of the study, we propose a method for improving teaching and evaluation to foster Korean students' scientific competence.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.1
• /
• pp.77-82
• /
• 2020

As Korea enters an aging age, social efforts to improve the IADL of the elderly are increasing. In this study, to improve the performance of financial management activities that the elderly is particularly burdened, we aim to learn the elderly through ATM simulation education contents so that they can use ATM smoothly. To this end, interviews were conducted with seniors to derive four major financial activities (deposits, withdrawals, deposit inquiries and bank account arrangements), and developed tablet PC-based ATM education contents identical to the existing bank ATM interfaces. The experiment was conducted on 20 elderly people in the Elderly Day Care Center, and their satisfaction, fatigue and performance were measured before and after education. The results of this study can provide ATM design guidelines for the elderly who have difficulty using ATM.

• Journal of Digital Convergence
• /
• v.19 no.2
• /
• pp.215-220
• /
• 2021

In order to reduce the harmful effects on the human body caused by the recent increase in the generation of fine dust in Korea, there is a need for technology to help predict the level of fine dust and take precautions. In this paper, we propose a 1D Convolutional-Recurrent Neural Network (1-D CRNN) model to predict the level of fine dust in Korea. The proposed model is a structure that combines the CNN and the RNN, and uses domestic and foreign fine dust, wind direction, and wind speed data for data prediction. The proposed model achieved an accuracy of about 76%(Partial up to 84%). The proposed model aims to data prediction model for time series data sets that need to consider various data in the future.

• Journal of Convergence for Information Technology
• /
• v.11 no.7
• /
• pp.14-20
• /
• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.1-7
• /
• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of Platform Technology
• /
• v.8 no.4
• /
• pp.20-28
• /
• 2020

In this paper, a voice, object, and optical character recognition platform including voice recognition-based smart wearable devices, smart devices, and web AI servers was proposed as an appropriate technology to help the visually impaired to live independently by learning the life cycle of the visually impaired in advance. The wearable device for the visually impaired was designed and manufactured with a reverse neckband structure to increase the convenience of wearing and the efficiency of object recognition. And the high-sensitivity small microphone and speaker attached to the wearable device was configured to support the voice recognition interface function consisting of the app of the smart device linked to the wearable device. From experimental results, the voice, object, and optical character recognition service used open source and Google APIs in the web AI server, and it was confirmed that the accuracy of voice, object and optical character recognition of the service platform achieved an average of 90% or more.

• Journal of Convergence for Information Technology
• /
• v.10 no.12
• /
• pp.9-14
• /
• 2020

Currently, NTIS (National Technology Information Service) is building an interactive search service based on artificial intelligence technology. In order to understand users' search intentions and provide R&D information, an interactive search service is built based on deep learning models and morpheme analyzers. The deep learning model learns based on the log data loaded when using NTIS and interactive search services and understands the user's search intention. And it provides task information through step-by-step search. Understanding the search intent makes exception handling easier, and step-by-step search makes it easier and faster to obtain the desired information than integrated search. For future research, it is necessary to expand the range of information provided to users.

• The Mathematical Education
• /
• v.60 no.1
• /
• pp.41-59
• /
• 2021

The purpose of this study is to synthesize a comprehensive and general conclusion about the effects of mathematics classes using peer tutoring on the cognitive (mathematics learning achievement) and affective domains. For this purpose, a total of 61 individual studies were meta-analyzed in this study to calculate the effect size, measuring the strength of the relationship between mathematics classes using peer tutoring and either the cognitive or affective domain. As a result of this study, it was confirmed that mathematics classes using peer tutoring generally have a medium effect size in both cognitive and affective domains. Also, it was found that level of school, type of student, learning location, class time, tutor education or prior training are significant variables that affect the impact of mathematics classes using peer tutoring on the cognitive and affective domains. These results suggest specific ideas on how to design and operate peer tutoring in school mathematics classes on the basis of different variables.