• The Korean Journal of Mycology
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• v.28 no.1
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• pp.6-10
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• 2000

For artificial cultivation of Phellinus gilvus (Schw.) Pat we have conducted a study on cultural characteristics and condition of fruitbody formation. The optimum temperature was about $30^{\circ}C$ at pH $6.0{\sim}7.0$ for mycelial growth. Optimum sawdust media were oak sawdust+willow sawdust(5:5, V/V), oak sawdust+willow sawdust+rice bran (4.5:4.5:1, V/V) and oak sawdust+pine sawdust+rice bran(4.5:4.5:1, V/V) and, the spawn incubation period was about $25{\sim}26$ days. Mycelial growth in the inner portion of oak log was 200 mm after 60 days and duration for the first fruitbody primordia were formed about 90 days after inoculation.

• The Korean Journal of Mycology
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• v.28 no.1
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• pp.11-15
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• 2000

For artificial cultivation of Phellinus pini (Thore. Fr.) Ames, we conducted some study on mycelium growth and optimum condition for fruitbody formation. The optimum condition for mycelial growth was $25{\sim}30^{\circ}C$ at pH $5.0{\sim}6.0$. Optimum sawdust media were oak sawdust+willow sawdust+rice bran (4.5:4.5:1, V/V) and oak sawdust+pine sawdust+rice bran (4.5:4.5:1, V/V) and the optimum spawn incubation period was about $33{\sim}34$ days. Mycelial growth in the inner portion of oak log was 40 mm after 60 days and duration for first fruitbody primordia formation was about 110 days after inoculation.

• The Research Journal of the Costume Culture
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• v.21 no.5
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• pp.613-628
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• 2013

This study aims to identify characteristics of neo-naturalism coming from periodical changes in the 21th society, culture based on naturalism and analyze the expressional and design characteristics of neo-naturalism on 2000s. For a research method, this study researched development of naturalism in fashion, and searched digital naturalism and ecology which are design paradigm effecting on neo-naturalism through literature research and preliminary study. Analyzing preliminary study on architecture, interior, fashion about digital naturalism and ecology design, concept of neo-naturalism identified and four expressional characteristics of neo-naturalism was classified, actual examples of neo-naturalism in 21th fashion were extracted and drew design characteristics. The results are as followings. Firstly, naturalism described nature as it is and developed according to the values and needs of the times. Naturalism in fashion showed natrual human body's curve, nature pattern and used natural materical focused on ideal beauty of nature. Secondly, neo-naturalism renews with the foundation of digital culture and ecology design paradigm, and focuses on the flexible possibility to express nature with digital, new media and formative art, and made the artificial nature uniting human-nature-environment as organic whole by ecology design paradigm. Thirdly, design of neo-naturalism divided four characteristics, nature's organic form, combination with the technology, ethical harmony with nature, global local design. The first characteristics of the nature's organic form are expressing silhouette of the nature's organic volume abstractly, the second ones of the combination with the technology are reinterpreting primitive nature contemporary with artificial sensibility of high technology, the third ones of the ethical harmony with nature are showing simple design and high-touch, and the forth ones of global local design are expressing cultural hybrid preserving vernacular design.

• Child Health Nursing Research
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• v.2 no.2
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• pp.35-44
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• 1996

The purpose of this study was to examine the knowledge and attitude of breast feeding and to explore the predictive variables for the intention of breast feeding of mothers. One hundred and thirty-five mothers who delivered at the D University hospital during the period of May to June in 1996 comprised the sample. Data were collected by questionnaire methods before discharge at the hospital. Data were analyzed using percent, 1-test, and logistic regression. The results were as follows : 1. During their pregnancy, majority of mothers (74.8%) got the breast feeding information. Information sources were book (34.5%), family and relatives(32.4%), mass media(24.3%), and professionals such as nurses and doctors (8.8% ). The frequently reported sources of most encouragement for breast feeding were mother in law(20.7%) and baby's father (11.1% ). 2. The mean score of the items of Knowledge and Attitude toward Breast Feeding Scale were 42.56 (SD=5.47) and 39.07(SD=5.15) , representing positive attitude toward breast feeding. The correlation between knowledge and attitude score was significant(r 〓.54, p<.001). Knowledge of breast feeding were significantly different between breast feeding intention group (including partial breast feeding) and artificial feeding intention group(t=2.79, p<.01) 3. Logistic regression analysis revealed that feeding method in the hospital, delivery type, knowledge toward breast feeding, disease related to pregnancy, complication related to delivery, and educational level of mother were predictives of the intention of breast feeding. 4. The most frequently rated reasons for the plan for mixed feeding were concern about insufficient milk (37.9%) and work(27.6%), The major reasons for plan for artificial milk feeding were having premature baby(25.9%) and maternal health problems including infection(14.8% ) and drug use due to chronic illness (14.8%). From the result of the study, it is recommended to develop supportive nursing intervention strategy to promote breast-feeding intention and practice. The intervention could be more effective to begin early in pregnancy and include teaching for breast feeding skills as well as information provision for positive attitude formation.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.791-801
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• 2019

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. Therefore, there is a need to develop a method that could increase the feature dimension, thereby improving the detection performance. In this paper, we use variational mode decomposition (VMD) to obtain multiple decomposed signals and then extract statistical features from them. The features from VMD outperform those from no-VMD in terms of detection performance. Further, artificial neural network is employed as an arc classifier. Experiments validated that the use of VMD improves the classification accuracy by up to 4 percent, based on 14,000 training data.

• Korean Medical Education Review
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• v.23 no.3
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• pp.160-167
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• 2021

Rapid developments in technology as part of the Fourth Industrial Revolution have created a demand for educational technology (EdTech) and a gradual transition from traditional teaching and learning to EdTech-assisted learning in medical education. EdTech is a portmanteau (blended word) combining the concepts of education and technology, and it refers to various attempts to solve education-related problems through information and communication technology. The aim of this study was to explore the use of key EdTech applications in medical education programs. A scoping review was conducted by searching three databases (PubMed, CINAHL, and Educational Sources) for articles published from 2000 to June 2021. Twenty-one studies were found that presented relevant descriptions of the effectiveness of EdTech in medical education programs. Studies on the application and effectiveness of EdTech were categorized as follows: (1) artificial intelligence with learner-adaptive evaluation and feedback, (2) augmented/virtual reality for improving learning participation and academic achievement through immersive learning, and (3) social media/social networking services with learner-directed knowledge generation, sharing, and dissemination in medical communities. Although this review reports the effectiveness of EdTech in various medical education programs, the number of studies and the validity of the identified research designs are insufficient to confirm the educational effects of EdTech. Future studies should utilize suitable research designs and examine the instructional objectives achievable by EdTech-based applications to strengthen the evidence base supporting the application of EdTech by medical educators and institutions.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.511-518
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• 2022

Supervised learning based on deep learning has made a leap forward in various application fields. However, many supervised learning methods work under the common assumption that training and test data are extracted from the same distribution. If it deviates from this constraint, the deep learning network trained in the training domain is highly likely to deteriorate rapidly in the test domain due to the distribution difference between domains. Domain adaptation is a methodology of transfer learning that trains a deep learning network to make successful inferences in a label-poor test domain (i.e., target domain) based on learned knowledge of a labeled-rich training domain (i.e., source domain). In particular, the unsupervised domain adaptation technique deals with the domain adaptation problem by assuming that only image data without labels in the target domain can be accessed. In this paper, we explore the unsupervised domain adaptation techniques.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.267-274
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• 2023

There is a problem that the size of the dataset is insufficient due to the limitation of the collection of the medical image public data, so there is a possibility that the existing studies are overfitted to the public dataset. In this paper, we compare the performance of eight (Unet, X-Net, HarDNet, SegNet, PSPNet, SwinUnet, 3D-ResU-Net, UNETR) medical image semantic segmentation models to revalidate the superiority of existing models. Anatomical Tracings of Lesions After Stroke (ATLAS) V1.2, a public dataset for stroke diagnosis, is used to compare the performance of the models and the performance of the models in ATLAS V2.0. Experimental results show that most models have similar performance in V1.2 and V2.0, but X-net and 3D-ResU-Net have higher performance in V1.2 datasets. These results can be interpreted that the models may be overfitted to V1.2.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.