• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.125-137
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• 2020

The purpose of this study is to analyze new words in Korean and Chinese based on changes in popular culture. As China and Korea embrace increasingly close communication in recent years, their languages have influenced each other. A lot of new Korean and Chinese words have been discovered to have the same linguistic characteristics. New words are considered as new developments of a language. They are welcomed and widely used by young people in Korea and China. Therefore, in terms of the communicative function of languages, it is worthwhile to understand new words in Korean and Chinese from the perspective of academic research. This study takes Chinese words created in 2018 as the research object. Firstly, a morphological and semantic comparison of Chinese words created in 2018 and those created in 2017 is carried out to extract the characteristic indicators of Chinese words created in 2018, with emphasis on compound words, abbreviations, substitutions, patters and rhetorical expressions. Secondly, the similarities and differences of these Chinese words with Korean words created in 2018 in terms of morphology are analyzed. Finally, after conducting sample classification and comparison, the characteristics of new Chinese and Korean words and the interaction mechanism under mutual influence are concluded. According to the study, the majority of the new words are created on the basis of existing words. Thus, it is important to explore the morphology of new words as a standard language.

• Therapeutic Science for Rehabilitation
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• v.11 no.3
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• pp.51-64
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• 2022

Objective : The purpose of this study is to establish the domain for assessment of feeding disorder with cerebral palsy. In addition, it is to develop item on feeding assessment for feeding disorder on children with cerebral palsy Methods : Factors to be included in the assessment of feeding disorders in children with cerebral palsy based on the ICF classification criteria constituted the assessment domain through literature review. Results : Assessment items of feeding disorders in children with cerebral palsy were categorized into 6 domains. These were systematized through expert advice, and through the first and second Delphi surveys, nine middle categories, 32 sub-categories, and 76 items were confirmed. Conclusions : The results of this study are useful for occupational therapists who want to check the current feeding function and activity level during therapeutic interventions for children with cerebral palsy who have feeding disorders.

• Korean small business review
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• v.41 no.1
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• pp.97-129
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• 2019

The concepts of entrepreneurship are very various. So many researchers are confused or not sure of the concepts of entrepreneurship. Some entrepreneurship researches has defined the entrepreneurship as 'self-employment. This study try to set a comprehensive conceptual model of concepts of entrepreneurship through classification of word and phrase with job analysis and competence model. Several concepts of entrepreneurship which important prior researchers, had defined are analysed into 'subject', 'object', 'verb', 'goal and behavioral results' with content analysis. Also, Several concepts of entrepreneur which important prior researchers had defined, are analysed into 'individual psychological traits', 'competence and ability', 'motive', and 'function or job (business).' This study suggests a integrated conceptual model of entrepreneurship based on analysed results.

• 한국노년학
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• v.29 no.4
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• pp.1327-1340
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• 2009

The purposes of the study were (1) to identify the level of successful aging of older people living in a city, (2) to identify associated factors with successful aging, and (3) to identify a risk group for successful aging using classification and regression trees (CART) analysis. One hundred eighty seven older people (>65years) participated in the cross-sectional survey. Trained interviewers collected data with a structured questionnaire on demographic information, Korean geriatric depression score, activity of daily living(ADL), instrumental activity of daily living(IADL), and Young's successful aging instrument in subject's home. A CART analysis split subjects into ten homogeneous small groups based on five determinant factors. Older people who are male, with higher education, living with family, and not receiving Medicaid showed better scores in successful aging than their counter parts. Depression was a strong primary determinant for successful aging. A risk group for successful aging of older people was identified by depression and IADL. An intervention to prevent and manage depression and to improve physical function of older people can be developed to promote successful aging of older people. It is suggested to consider an assessment of depression to develop the policies for older people welfare.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.394-403
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• 2023

In the pursuit of sustainable and environmentally-friendly agricultural practices, we conducted an extensive study on the rhizosphere bacteria inhabiting soils that have been devoid of chemical fertilizers for an extended period exceeding 40 years. Through this investigation, we isolated a total of 80 species of plant growth-promoting rhizosphere bacteria and assessed their potential to enhance plant growth. Among these isolates, Burkholderia cepacia CD2 displayed remarkable plant growth-promoting activity, making it an optimal candidate for further analysis. Burkholderia cepacia CD2 exhibited a range of beneficial characteristics conducive to plant growth, including phosphate solubilization, siderophore production, denitrification, nitrate utilization, and urease activity. These attributes are well-known to positively influence the growth and development of plants. To validate the taxonomic classification of the strain, 16S rRNA gene sequencing confirmed its placement within the Burkholderia genus, providing further insights into its phylogenetic relationship. To delve deeper into the potential mechanisms underlying its plant growth-promoting properties, we sought to confirm the presence of specific genes associated with plant growth promotion in CD2. To achieve this, whole genome sequencing (WGS) was performed by Plasmidsaurus Inc. (USA) utilizing Oxford Nanopore technology (Abingdon, UK). The WGS analysis of the genome of CD2 revealed the existence of a subsystem function, which is thought to be a pivotal factor contributing to improved plant growth. Based on these findings, it can be concluded that Burkholderia cepacia CD2 has the potential to serve as a microbial fertilizer, offering a sustainable alternative to chemical fertilizers.

• Journal of Korean Library and Information Science Society
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• v.55 no.2
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• pp.29-53
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• 2024

Public library programs serve a cultural function and are a tool for community interaction. Recently, there has been an increase in the number of websites that integrate the programs of various institutions, but there is no set framework to describe the programs. Therefore, in order to prepare a framework for program information, we conducted a content analysis of Jeongdok Library programs as an example. Using MAXQDA, a content analysis tool, category codes for type, topic, special classification, and target audience were derived. Based on this, we analyzed the characteristics of the Jeongdok library programs as follows. In terms of type, there are many programs such as classes and lectures, but fewer programs related to tours, performances and screenings, and operational experiences. In terms of topic matter, programs related to reading and the arts were dominant, while programs related to book curation, awards, and the environment were less common. In terms of target audience, the most common programs were for adults, with fewer programs for high school students, middle school students, and library staff. The framework of Jeongdok library program can be used not only to classify the programs currently operated by other public libraries, but also to develop a service platform for public library programs in Korea.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.714-726
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• 2024

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.