• Nutrition Research and Practice
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• v.15 no.sup1
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• pp.110-121
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• 2021

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.83-89
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• 2022

Internet of Things technology is rapidly developing due to the recent development of information and communication technology. IoT technology utilizes various sensors to generate unique data from each sensor, enabling diagnosis of system status. However, the equipment management system currently in effect is a post-preservation concept in which administrators must deal with the problem after the problem occurs, which could mean system reliability and availability problems due to system errors, and could result in economic losses due to negative productivity disruptions. Therefore, this study confirmed that edge controller control decision algorithms for more efficient operation of rectifiers in the factory by applying intelligent IoT (AIoT) technology and domain knowledge-based modeling for each sensor data collected based on this, outputting appropriate status messages for each scenario.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.899-906
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• 2021

Data science is a discipline comprised of the academic fields of statistics, computer science, information technology, and domain knowledge. It analyzes data and derives meaningful results using complex technologies. Data science, along with artificial intelligence, is a core technology of the 4th industrial revolution; consequently, universities and companies worldwide are actively developing programs to develop data scientists who require high levels of expertise. In line with this undertaking, the field of elementary education has recognized the importance of data science education and so various studies have been conducted to develop curricula designed to help students understand how to use data. This paper proposes a curriculum for the purpose of educating elementary school teachers who are mostly non-majors in the computer field about data science. Satisfaction analysis was conducted based on questionnaires collected from students to analyze the effectiveness of the data science education proposed in this paper.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1142-1160
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• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.259-277
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• 2022

This study aims to understand international students' web search behaviors. During the experiment, fifteen international students were asked to conduct three search tasks which includes six search questions. Depending on the characteristics of search task, there were differences in search performance and search behavior. It was commonly found that participants with higher Korean fluency showed higher search performance; however, prior knowledge about the search topic did not always affect the search performance. In the search tasks that required navigation through menus and links within one web domain, participants often overlooked the correct answers, even if they were at the webpages containing the correct answer. Also, some participants did not realized that they found wrong answers. For enhancing information seeking behaviors among foreigners in Korea, the followings were suggested: 1) to design websites which are easy for non-native speakers to navigate, and 2) to use social media as a means of interactive communication.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Education of Primary School Mathematics
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• v.27 no.2
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• pp.155-171
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• 2024

This study compared and analyzed students' reasoning processes and justification methods when introducing the concept of "the sum of angles in a triangle" in mathematics classes with a focus on both measurement and geometric aspects. To confirm this, the research was conducted in a 4th-grade class at H Elementary School in Suwon, Gyeonggi-do, South Korea. The conclusions drawn from this study are as follows. First, there is a significant difference when introducing "the sum of angles in a triangle" in mathematics classes from a measurement perspective compared to a geometric perspective. Second, justifying the statement "the sum of angles in a triangle is 180°" is more effective when explained through a measurement approach, such as "adding the sizes of the three angles gives 180°," rather than a geometric approach, such as "the sum of the angles forms a straight angle." Since elementary students understand mathematical knowledge through manipulative activities, the level of activity is connected to the quality of mathematics learning. Research on this reasoning process will serve as foundational material for approaching the concept of "the sum of angles in a triangle" within the "Geometry and Measurement" domain of the Revised 2022 curriculum.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.