• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• The Mathematical Education
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• v.61 no.2
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• pp.257-271
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• 2022

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.407-408
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• 2023

In this paper, we intend to develop a multimodal algorithm that secures recognition performance of over 95% in daytime illumination environments and secures recognition performance of over 90% in bad weather (rainfall and snow) and night illumination environments.

• Journal of the Chungcheong Mathematical Society
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• v.11 no.1
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• pp.27-34
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• 1998

We introduce ${\delta}$-frames, strong ${\delta}$-frames and completely distributive lattices, and investigate some relationships among those frames.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• The Mathematical Education
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• v.60 no.3
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• pp.321-340
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• 2021

As many changes in the future society represented by the age of artificial intelligence(AI) are expected to come, efforts are being made to draw the shape of the future education and various research methods are being employed to support the attempts. While many research studies use methods for deriving generalized results such as expert survey and trend analysis in along with a review of literature, there are attempts to apply the scenario methodology to explore ideas and information needed within a changing context. A scenario method, one of the experiential learning strategies, aims to seek various and alternative approaches by establishing a plan from the present conditions considering future changes. In this study, in-service teachers' perceptions and expectations of the interactivity between human and AI teachers were visualized by applying the role-play presentation technique that grafted the concept of role-play game to the scenario method. In addition, the mandal-art method was introduced to support in conducting productive discussion during the teachers' collaboration. This method appeared to help to depict teachers' perceptions of AI teachers in the detailed and concrete form, which may flow in the abstract otherwise. Through analyses of the teachers' role-play presentations with the implementation of the madal-art method it was suggested that most teachers would want to collaborate with an AI teacher for improved instruction and individualized student learning while they would take the instructional authority over the AI teacher in the classroom.

• School Mathematics
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• v.18 no.1
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• pp.159-173
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• 2016

In real educational field, there are cases that concrete problematic situations are introduced after abstract concepts are taught on the contrary to process that abstract from concrete contexts. In other words, there are cases that abstract knowledge has to be concreted. Freudenthal expresses this situation to antidogmatical inversion and indicates negative opinion. However, it is open to doubt that every class situation can proceed to abstract that begins from concrete situations or concrete materials. This study has done a comparative analysis in difference of mathematical thinking between a process that builds abstract context after being abstracted from concrete materials and that concretes abstract concepts to concrete situations and attempts to examine educational implication. For this, this study analyzed the mathematical thinking in the abstract process of concrete materials by manipulating AiC analysis tools. Based on the AiC analysis tools, this study analyzed mathematical thinking in the concrete process of abstract concept by using the way this researcher came up with. This study results that these two processes have opposite learning flow each other and significant mathematical thinking can be induced from concrete process of abstract knowledge as well as abstraction of concrete materials.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.

• Journal of the Chungcheong Mathematical Society
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• v.11 no.1
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• pp.13-26
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• 1998

The purpose of this note is to study the relation between Heyting algebra and t-algebra which is the dual concept of BCK-algebra. We define t-algebra with binary operation ${\rhd}$ which is a generalization of the implication in the Heyting algebra, and define a bounded ness and commutativity of it, and then characterize a Heyting algebra and a Boolean algebra as a bounded commutative t-algebra X satisfying $x=(x{\rhd}y){\rhd}x$ for all $x,y{\in}X$.

• Journal of applied mathematics & informatics
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• v.41 no.4
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• pp.821-829
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• 2023

In this paper, we obtain characterizations of continuous distributions based on the independent property of generalized record values extending the characterization results reported by Jin and Lee [4], Skřivánková and Juhás [8]. Also, example of special cases of general classes as Bur types, Pareto, power and Weibull distribution are discussed.