• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• International Journal of Oral Biology
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• v.46 no.4
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• pp.168-175
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• 2021

To investigate the perceptions and attitudes of dental hygienists toward radiation safety management in Korea. A total of 800 dental hygienists were randomly selected for an anonymous survey, and 203 of them participated. The questionnaire items included the following: sex, career period, type of installed radiographic equipment, recognition of the diagnostic reference level (DRL), participation in radiation safety education, and attitudes toward radiation protection for both patients and dental hygienists. The participants were divided into two groups according to their years of experience (< 10 years versus ≥ 10 years). The difference between the groups was investigated according to frequency distribution. Fisher's exact test or Pearson's chi-square (𝛘²) test was used as appropriate. A regression analysis was performed to investigate the impact of wearing a thyroid collar for personnel protection during patient radiation exposure. The types of installed radiographic equipment included panoramic radiography (96.1%), cephalometric radiography (76.9%), intraoral radiography (72.9%), and cone-beam computed tomography (69.5%). Significant differences were observed in the learning pathway for the DRL (Fisher's exact test, p < 0.05), satisfaction with radiation safety education (Pearson's 𝛘² test = 5.3975, Pr = 0.02), and use of personnel radiation monitoring systems (Pearson's 𝛘² test = 18.1233, Pr = 0.000) between the groups. Significant differences were also observed in personnel protection using a thyroid collar and patient protection during panoramic radiography (odds ratio = 14.2). Dental hygienists with more than 10 years of experience were more satisfied with radiation safety education and more interested in radiation monitoring. Considering career experience, customized, continuous, and effective radiation safety management education should be provided.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.213-221
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• 2021

In this paper, we suggest the Deep Neural Network Model System for predicting results of the match of 'League of Legends (LOL).' The model utilized approximately 26,000 matches of the LOL game and Keras of Tensorflow. It performed an accuracy of 93.75% without overfitting disadvantage in predicting the '2020 League of Legends Worlds Championship' utilizing the real data in the middle of the game. It employed functions of Sigmoid, Relu and Logcosh, for better performance. The experiments found that the four variables largely affected the accuracy of predicting the match --- 'Dragon Gap', 'Level Gap', 'Blue Rift Heralds', and 'Tower Kills Gap,' and ordinary users can also use the model to help develop game strategies by focusing on four elements. Furthermore, the model can be applied to predicting the match of E-sports professional leagues around the world and to the useful training indicators for professional teams, contributing to vitalization of E-sports.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.87-96
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• 2022

This paper is an empirical analysis as a reference model that can predict up to the maximum number of elementary school student care needs in local governments across the country. This study analyzed and predicted the characteristics of the region based on machine learning to predict the demand for elementary care in a new apartment complex. For this purpose, a total of 292 variables were used, including data related to apartment structure, such as number of parking spaces per household, and building-to-land ratio, environmental data around apartments such as distance to elementary schools, and population data of administrative districts. The use of various variables is of great significance, and it is meaningful in complex analysis. It is also an empirical case study that increased the reliability of the model through comparison with the actual value of the basic local government.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.307-315
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• 2022

The purpose of this study was to design a training program for pre-service elementary teachers, incorporating the concepts of extended reality technologies. This program contained the basic skills necessary for them to utilize in their future classrooms. To accomplish this, 12 undergraduate students of various majors enrolled in one of Korea's national universities of education were selected as research subjects. For a total of 6 times over 6 weeks, they participated in a training program learning the basic concepts of virtual, augmented, and mixed reality, as well as creating their own education software to use in simulated classes. To improve the quality of future research efforts, this study found it would be beneficial to: 1) expand the relevant support equipment, 2) provide students with preliminary, background knowledge of text-based programming, 3) introduce short-term, more intensive training, and 4) improve the survey methods for this research.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.1
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• pp.109-114
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• 2010

In this paper, we developed the web content of statistical analysis using statistical package and Active Server Page (ASP). A statistical package is very difficult to learn and use for non-statisticians, however, non-statisticians want to do analyze the data without learning statistical packages such as SAS, S-plus, and R. Therefore, we developed the web based statistical analysis contents using S-plus which is the popular statistical package and ASP. In real application, we developed the web content for various statistical analyses such as exploratory data analysis, analysis of variance, and time series on the web using water quality data. The developed statistical analysis web content is very useful for non-statisticians such as public service person and researcher. Consequently, combining a web based contents with a statistical package, the users can access the site quickly and analyze data easily.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.488-490
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• 2022

Recently, interest in industrial accidents such as the Industrial Safety and Health Act and the Serious Accident Punishment Act is increasing, and the demand for safety managers for safety management of workers in research institutes and industrial fields of various fields is increasing. For worker safety management, CCTVs are being installed in factories and workplaces, and workers are monitored to enhance safety management. In this paper, we intend to design a dangerous situation assessment system by constructing data using CCTV in such a workplace and modeling it in JSON format. The data modeling was produced by referring to the data set construction guide for artificial intelligence learning and the quality management guideline of the Korea National Information Society(NIA). Through this system, we want to check what kind of risk management exists in the workplace by risk situation scenario and use it to build a more systematic system.

• Journal of Christian Education in Korea
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• v.72
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• pp.7-24
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• 2022

Christian education for the world after COVID-19 needs to use rapid changes in the surrounding situation as an opportunity to overcome a new crisis so that the church can achieve its educational mission. If the biggest dilemma in the post-Corona era is that there is no authoritative educational prescription anywhere, the most reasonable option for church education in this situation is to emphasize and cultivate learners' ability to flexibly cope with rules that are completely different than before COVID-19. As a natural result of the crisis, Christian education needs to be more interested in the trend of social change in the pandemic era(glocalization, digital transformation, economic inequality, educational environment change, church crisis) and actively reflect its contents in education. In addition, while operating a mobile(or online) church school that combines offline and online, there is an urgent need for an innovative transition to a core church school where certain church schools and churches cooperate with each other, a church school that guarantees a safe learning space, and an ecological church school that is interested in education dealing with climate change and ecology.