• Proceedings of the KSMRM Conference
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• 2006.11a
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• pp.11-16
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• 2006

• Journal of Preventive Medicine and Public Health
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• v.54 no.3
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• pp.166-172
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• 2021

Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.

• School Mathematics
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• v.2 no.2
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• pp.389-401
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• 2000

The purpose of the study is to introduce how elementary mathematics pre-sevice teachers in pre-service teacher program could use and integrate poster, a kind of instructional media, to connect mathematics knowledge to real worlds. Poster examples include such as connection to mathematicians and mathematical connections to real world as well as nature. Further, future study will continue to foster students and teachers to try to construct their alive mathematics knowledge.

• Education of Primary School Mathematics
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• v.5 no.1
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• pp.1-11
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• 2001

The purpose of the study is to introduce how tessellation can be used and integrated to connect geometry to pattern in elementary mathematics educations. Tessellation examples include transformations such as translational symmetry, rotational symmetry, reflection symmetry, and glide reflection symmetry. In addition, many examples of tessellation using softwares such as Escher, TesselMania!, and LOGO programs. Further, future study will continue to foster students and teachers to try to construct their alive mathematics knowledge. The study of geometry and patterns require a rich teaching and learning environment provided by in-depth understanding of thinking connections to objects in real world.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.557-574
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• 2024

Purpose: This study investigates whether adoption of AI-based systems and technologies improve operational efficiency in healthcare organizations through a systematic review of the literature and real-world examples. Methods: In this study, we divided the AI application cases into care services and administrative functions, then we explored opportunities and challenges in each area. Results: The analysis results indicate that the care service field primarily uses AI-based systems and technologies for quick disease diagnosis and treatment, surgery and disease prediction, and the provision of personalized healthcare services. In the administrative field, AI-based systems and technologies are used to streamline processes and automate tasks for the following functions: patient monitoring through virtual care support systems; automating patient management systems for appointment times, reservations, changes, and no-shows; facilitating patient-medical staff interaction and feedback through interaction support systems; and managing admission and discharge procedures. Conclusion: The results of this study provide valuable insights and significant implications about the application of AI-based systems or technologies for various innovation opportunities in healthcare organizations. As digital transformation accelerates across all industries, these findings provide valuable information to managers of hospitals that are interested in AI adoption, as well as for policymakers involved in the formulation of medical regulations and laws.

• The Mathematical Education
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• v.57 no.3
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• pp.289-309
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• 2018

There has been a gradually increasing focus on adopting mathematical modeling techniques into school curricula and classrooms as a method to promote students' mathematical problem solving abilities. However, this approach is not commonly realized in today's classrooms due to the difficulty in developing appropriate mathematical modeling problems. This research focuses on developing reformulation strategies for those problems with regard to mathematical modeling. As the result of analyzing existing textbooks across three grade levels, the majority of problems related to the real-world focused on the Operating and Interpreting stage of the mathematical modeling process, while no real-world problem dealt with the Identifying variables stage. These results imply that the textbook problems cannot provide students with any chance to decide which variables are relevant and most important to know in the problem situation. Following from these results, reformulation strategies and reformulated problem examples were developed that would include the Identifying variables stage. These reformulated problem examples were then applied to a 7th grade classroom as a case study. From this case study, it is shown that: (1) the reformulated problems that included authentic events and questions would encourage students to better engage in understanding the situation and solving the problem, (2) the reformulated problems that included the Identifying variables stage would better foster the students' understanding of the situation and their ability to solve the problem, and (3) the reformulated problems that included the mathematical modeling process could be applied to lessons where new mathematical concepts are introduced, and the cooperative learning environment is required. This research can contribute to school classroom's incorporation of the mathematical modeling process with specific reformulating strategies and examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• Journal of Korean Academy of Nursing
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• v.34 no.1
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• pp.15-24
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• 2004

Purpose: The purpose of this research was to explore the cyber world immersion experience of adolescents. Method: Multiple strategies for data collecting were used: an in depth face-to-face interview; analysis of adolescent' writings; and analysis of examples of phenomenon in the realistic world. The sample group consisted of 10 adolescents. Results: Although the experience was different for all adolescent interviewed, the essential themes of experience emerged: "fill up", "homoeologous feeling", "the older generation has a conflicting negative opinion", "change in social character", "become habitually skeptic", "have bad health", "mean of superiority and getting everything solved", "ease of access", "monetary benefit", "addiction to the computer", "forget real life solved stress", "do harm to society", "take comfort", or "new job". Conclusion: Accordingly this paper suggests that contact with various software is necessary in adolescents, and good quality contents function to prepare and activate adolescents to apply the internet for good use.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.