• Journal of Acupuncture Research
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• v.38 no.3
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• pp.219-226
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• 2021

Background: The objective structured clinical examination (OSCE) is a widely used method to assess the clinical performance of students in clinical practice. Although OSCE has been used for undergraduate students of Korean medicine, this has not been widely reported. Methods: In 2020, the practical course for acupuncture and moxibustion medicine (acupuncture, electroacupuncture, pharmacopuncture, auricular acupuncture, and burning acupuncture) was taught using flipped learning, according to clinical practice guidelines, and assessed by the OSCE. The appropriateness of this model of education and its evaluation using OSCE were evaluated using a 5-point Likert scale, and the results were analyzed. Results: Of the respondents, 67% reported that the OSCE accurately reflected their competency, and 82% reported that online video lectures helped them to improve their clinical skills. The average adequacy score of the model was > 3.7/5, and the average adequacy score of the checklist used in the OSCE was > 4.1/5 for all 5 clinical application skills. The difference in the mean self-efficacy score between students who had taken the OSCE and those students who had not taken the OSCE, was highest in the burning acupuncture group (0.923). Conclusion: This study showed that students' satisfaction with the OSCE was high and flipped learning was an effective education model. In the future, models representing the human body or simulated patients should be used to evaluate students' skills and attitude.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.33-39
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• 2019

As cyber attack methods are becoming more intelligent, incidents such as security breaches and international crimes are increasing. In order to predict and respond to these cyber attacks, the characteristics, methods, and types of attack techniques should be identified. To this end, many security companies are publishing security intelligence reports to quickly identify various attack patterns and prevent further damage. However, the reports that each company distributes are not structured, yet, the number of published intelligence reports are ever-increasing. In this paper, we propose a method to extract structured data from unstructured security intelligence reports. We also propose an automatic intelligence report analysis system that divides a large volume of reports into sub-groups based on their topics, making the report analysis process more effective and efficient.

• 한국노년학
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• v.32 no.4
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• pp.1041-1061
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• 2012

The study looked into the effect of housing poverty on the depression level for the elderly in depth. In this study, we defined housing poverty as sub-minimum standard housing conditions, excess housing expenditure and housing instability. In order to verify the correlation of two variables, a mediating model structured by social welfare service was used which gave out the 4th Korea welfare panel data. When it came to our research methods, structured equation analysis was applied to verify the mediating effect and theoretical background. The results revealed that housing poverty of the elderly directly affected their depression level. Also the satisfaction of social service showed a partial mediating effect between housing poverty and depression level. But the mediating effect of social service experience between housing poverty and depression level was not statistically significant. The outcome of this study indicated the practical and social intervention to promote a mental health of the elderly by improving residential environment.

• Journal of the HCI Society of Korea
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• v.14 no.1
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• pp.15-22
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• 2019

The purpose of this study is to investigate the users' expectation and expectation gap about the attributes of smart speaker as an intelligent agent, ie autonomy, sociality, responsiveness, activeness, time continuity, goal orientation. To this end, semi-structured interviews were conducted for smart speaker users and analyzed based on ground theory. Result has shown that people have huge expectation gap about the sociality and human-likeness of smart speakers, due to limitations in technology. The responsiveness of smart speakers was found to have positive expectation gap. For the memory of time-sequential information, there was an ambivalent expectation gap depending on the degree of information sensitivity and presentation method. We also found that there was a low expectation level for autonomous aspects of smart speakers. In addition, proactive aspects were preferred only when appropriate for the context. This study presents implications for designing a way to interact with smart speakers and managing expectations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.244-251
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• 2020

This paper presents a design of the Meta-Class Repository (MCR) which maintain syntactically analyzed and structured meta-class information from UML diagrams, and then proposes 'meta-class,' also known as super-class, to construct structured information analyzed syntactically. The MCR is a collection of these meta-classes which contains the information extracted from diagrams. This paper also presents a design of the Code Generation Engine (CGE) which roles generating codes corresponding classes from UML diagrams based on the MCR maintaining a collection of meta-classes which is syntactically-analyzed and constructed in previous process. The logics of CGE are designed to generate codes collaborated with MCR and CGE with integration. The logics of CGE mechanism is presented with the form of finite state machine to present the algorithms of code generation formally and have the advantages of simplicity and easiness in development.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.159-169
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• 2020

Shanghanlun (Treatise on Cold Damage Diseases) is the oldest document in the literature on clinical records of Traditional Asian medicine (TAM), on which TAM theories about symptoms-herbs relationships are based. In this study, we aim to quantitatively explore the relationships between symptoms and herbs in Shanghanlun. The text in Shanghanlun was converted into structured data. Using the structured data, Term Frequency - Inverse Document Frequency (TF-IDF) scores of symptoms and herbs were calculated from each chapter to derive the major symptoms and herbs in each chapter. To understand the structure of the entire document, principal component analysis (PCA) was performed for the 6-dimensional chapter space. Bipartite network analysis was conducted focusing on Jaccard scores between symptoms and herbs and eigenvector centralities of nodes. TF-IDF scores showed the characteristics of each chapter through major symptoms and herbs. Principal components drawn by PCA suggested the entire structure of Shanghanlun. The network analysis revealed a 'multi herbs - multi symptoms' relationship. Common symptoms and herbs were drawn from high eigenvector centralities of their nodes, while specific symptoms and herbs were drawn from low centralities. Symptoms expected to be treated by herbs were derived, respectively. Using measurable metrics, we conducted a computational study on patterns of Shanghanlun. Quantitative researches on TAM theories will contribute to improving the clarity of TAM theories.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.15-21
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• 2020

In order to utilize big data in general industrial sites, the structured big data collected from facilities, processes, and environments of industrial sites must first be processed and stored, and in the case of unstructured data, it must be stored as unstructured data or converted into structured data and stored in a database. In this paper, we study a method of collecting big data based on open IoT standards that can converge and utilize measurement information, environmental information of industrial sites to collect big data. The platform for collecting big data proposed in this paper is capable of collecting, processing, and storing big data at industrial sites to process real-time sensing information. For processing and analyzing data according to the purpose of the stored industrial, various big data technologies also can be applied.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.107-114
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• 2023

X-ray fluorescence analysis has been widely used in the field of science and industry because it gives information about elements and their concentrations without destruction of samples. To increase analysis accuracy of fluorescence generated by photons of the transmission-type X-ray tube for mixture and compound samples would be recommend to have strong energy near 10 keV and 20 keV simultaneously. Tungsten of 9.65 keV and molybdenum of 17.48 keV were considered as targets with dual deposition structure for obtaining two strong characteristic X-rays, and the transmission-type X-ray tube was analyzed using Geant4 Monte Carlo simulation. The W-Mo structure resulted in strong characteristic X-ray near 10 keV and 20 keV simultaneously. A structure with Mo-W multilayers of 5 ㎛ thick also gave optimal spectrum. Various material combination and thickness optimization for the dual-structured target can give X-ray spectrum with strong characteristic X-ray of specific energies.