• Journal of Elementary Mathematics Education in Korea
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• v.22 no.2
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• pp.161-181
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• 2018

This study involved an investigation of prospective elementary teachers' preconceptions and experiences of diagrams and their ability to draw diagrams in solving math word problems. A questionnaire and two math word problems were administered to prospective elementary teachers who began to taking an introductory mathematics education course. The results from the analysis of their responses to the questionnaire items indicate that prospective elementary teachers appreciate the value of diagrams as tools for problem solving and communication. In addition, prospective elementary teachers have the will not only to teach their future students how to use diagrams but also to encourage them to draw diagrams in solving math word problems. However, the results also indicates that prospective elementary teachers neither use diagrams spontaneously in their math problem solving activities nor have confidence in drawing useful diagrams. Prospective elementary teachers also manifested low scores on the questionnaire items asking whether they were taught how to draw useful diagrams or encouraged by their teachers to use diagrams in their previous learning experiences. The results from the analysis of the diagrams that prospective elementary teachers drew in solving math word problems showed that most of them had difficulty drawing diagrams that represent their reasoning and solving process.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.59-60
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• 2013

Infobutton is an important concept from long time in use and much has been done with respect to its standardization and context supplementation. The concept is to create contextual links to information resources from within the information systems usually health information systems. The need which has been realized by the authors of this paper is the augmentation of Infobuttons from the level of only information links to the level of knowledge links. The authors proposed the concept of knowledge links named as "Knowledgebuttons" which complements the concept Infobuttons. It adds further capabilities of getting knowledge to the users instead of just connectivity to information resources. The better representation of the information retrieved with Infobuttons is the first and foundation step to achieve the goal of getting knowledge. This paper discusses about the concept and applicability of Knowledgebuttons in health information systems. It is envisioned that this concept will add to the overall quality of patient care. Both physicians and patients can benefit from this technique as per their needs. Physicians can help in patient diagnosis and treatment critical decisions while patients can educate them to know more about their health conditions by studying the right knowledge at right time. Knowledgebuttons are able to create a true learning environment for the users while using health information systems.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

• The Mathematical Education
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• v.60 no.4
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• pp.543-554
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• 2021

The dynamic geometric environment plays a positive role in solving students' geometric problems. Students can infer invariance in change through dragging, and help solve geometric problems through the analysis method. In this study, the continuous spectrum of the dynamic geometric environment can be used to solve problems of students. The continuous spectrum can be used in the 'Understand the problem' of Polya(1957)'s problem solving stage. Visually representation using continuous spectrum allows students to immediately understand the problem. The continuous spectrum can be used in the 'Devise a plan' stage. Students can define a function and explore changes visually in function values in a continuous range through continuous spectrum. Students can guess the solution of the optimization problem based on the results of their visual exploration, guess common properties through exploration activities on solutions optimized in dynamic geometries, and establish problem solving strategies based on this hypothesis. The continuous spectrum can be used in the 'Review/Extend' stage. Students can check whether their solution is equal to the solution in question through a continuous spectrum. Through this, students can look back on their thinking process. In addition, the continuous spectrum can help students guess and justify the generalized nature of a given problem. Continuous spectrum are likely to help students problem solving, so it is necessary to apply and analysis of educational effects using continuous spectrum in students' geometric learning.

• Smart Media Journal
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• v.12 no.7
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• pp.76-83
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• 2023

This study proposes a method to assist webtoon artists in the process of webtoon creation by utilizing a pretrained Text-to-Image model to generate webtoon images from text. The proposed approach involves fine-tuning a pretrained Stable Diffusion model using a webtoon dataset transformed into the desired webtoon style. The fine-tuning process, using LoRA technique, completes in a quick training time of approximately 4.5 hours with 30,000 steps. The generated images exhibit the representation of shapes and backgrounds based on the input text, resulting in the creation of webtoon-like images. Furthermore, the quantitative evaluation using the Inception score shows that the proposed method outperforms DCGAN-based Text-to-Image models. If webtoon artists adopt the proposed Text-to-Image model for webtoon creation, it is expected to significantly reduce the time required for the creative process.

• Smart Media Journal
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• v.12 no.4
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• pp.27-40
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• 2023

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.177-188
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• 2024

As technology evolves, Internet usage continues to grow, resulting in a geometric increase in network traffic and communication volumes. The network path selection process, which is one of the core elements of the Internet, is becoming more complex and advanced as a result, and it is important to effectively manage and analyze it, and there is a need for a representation and visualization method that can be intuitively understood. To this end, this study designs a framework that analyzes network data using BGP, a network path selection method, and applies it to the cyber common operating picture for situational awareness. After that, we analyze the visualization elements required to visualize the information and conduct an experiment to implement a simple visualization. Based on the data collected and preprocessed in the experiment, the visualization screens implemented help commanders or security personnel to effectively understand the network situation and take command and control.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Korean Journal of Clinical Pharmacy
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• v.25 no.4
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• pp.286-295
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• 2015

Objective: This experiment is to study how elderly group and their various lifestyles interact with health functional foods, according to their selection behavior. Different lifestyles will be observed closely, as well as how different health conditions and consumer involvements will affect critical decision making in selecting health functional foods. Method: Theories and discoveries from original advanced research were compared parallel to the new study. Results: First, cluster analysis and exploratory analysis were performed amongst different elder lifestyles. Lifestyle exploratory analysis was used for healthy, unique, leisure, and economical-style elders. Cluster analysis was used for material trend oriented, health oriented, complacent oriented-style elders. Health Functional Foods' selection trait Exploratory Factor Analysis showed that product's originality (function, uniqueness, specialty, compatibility, distributor, expiration date), quality (amount, daily dose, visual representation, accessibility, portability, natural ingredients), and popularity (product container, brand image, taste and smell, advertised product, domestic or import, well-known function) were the three main causes. Secondly, the amount of benefits for the elderly group health lifestyle were affected by 'Interest in health', 'Notability of the health functional food', and 'Functionality approved mark'. Specifically, the importance of, 'Interest in health', 'Notability of health functional food', and 'Functionality approved mark' were noticeably high within health oriented elders. Lastly, after examining the data from elder lifestyle's relationship with health functional food selection trait, all three different results showed equal importance. If you closely examine material trend oriented elderly group, selection trait showed distinctively high regards in 'Fundamental Attribute', 'Typical Attribute', and 'Cognitive Attribute'. Health oriented elders showed their distinctively high regards in 'Natural Attribute', and less consideration in 'Typical Attribute' and 'Cognitive Attribute'. Complacent oriented-style elderly group showed less focus on 'Fundamental Attribute', and even less in 'Typical Attribute', and 'Cognitive Attribute'. Health oriented elderly group concluded with above data from the fact that they showed most importance and involvement in health beneficial products that are scientifically proven. Material trend oriented elderly group showed balanced traits in their concluded data, showing that they prefer function, safety, as well as the brand image and their reputation. Also, they consider the products' outer elements, such as design and product name, in order to sense inner functions. Conclusion: So, Silver Business corporations must develop products to fulfill the market demands, and strategize marketing plans to better target the correct audience.