• 전자공학회논문지 IE
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• v.46 no.3
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• pp.62-67
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• 2009

In this paper, ROC curves were designed by using Fuzzy Logic Systems. ROC curve is used for diagnostic evaluation and the person evaluating ROC curve is chosen as a first-level diagnostician. For rating diagnostic capability on ROC curve through learning, the chest X-ray image is used. The images used for making a diagnosis are X-ray film being both noise and signal. The result over diagnostic capability difference between the male and the female represented a man had better than a woman but that difference can be ignored.

• Research in Mathematical Education
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• v.17 no.1
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• pp.1-14
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• 2013

Stochastic phenomena induce us to construct a probability model and structure our thinking; corresponding models help us to understand and interpret the reality. They in turn equip us with tools to recognize, reconstruct and solve problems. Therefore, various implications in terms of methodology as well as epistemology naturally flow from different adoptions of models for probability. Right from the basic scenarios of different perspectives to explore reality, students are occasionally exposed to misunderstanding and misinterpretations. With realistic examples a multi-faceted image of probability and different interpretation will be considered in mathematical modelling activities. As an exploratory investigation, mathematical modelling activity for probability learning was elaborated through semiotic analysis. Especially, the coherence structure in mathematical modelling discourse was reviewed form a semiotic perspective. The discourses sampled from group activities were analyzed on the basis of semiotic perspectives taxonomical coherence relations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• Journal of the Korean Institute of Educational Facilities
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• v.22 no.1
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• pp.13-23
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• 2015

Currently, the group study becomes the most popular method and common custom for learning in public that is distinctly different from the preferred way of self-study in the last decades. As a result, the college and public facilities have been significantly changed to provide more and more new spaces for discussion, learning and sharing information with others as one of the remarkable improvements and advantages. On the other hands, library is one of the all-important public sites to obtain information and knowledge for students. Moreover, we can split out an individual space from the reading rooms to take part in the group activities such as group meetings and academic exchanges. Recently, several universities begin to recognize the value of group study and try to meet the research needs. Also, needs for students, and the group study rooms are applied into the new buildings and rebuilt ones under this background. In this study, It focuses on analyzing of the 134 group study rooms to investigate the variations in universities and classify the types of buildings with site researches through 14 indicators of indoor environments. To investigate different types of group study rooms, we use the SD method to analyze the findings. So far there is no research focusing on the study rooms, especially for the analysis of their types and indoor environments features. Therefore, this article can provide a theoretical basis and evidence to related researches; also can help us to improve indoor environments to offer a better learning environments for the students in the future.

• Research in Community and Public Health Nursing
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• v.6 no.2
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• pp.161-172
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• 1995

This study was conducted to have better understanding of the students' experience in field practice by school health practice experience. The study subjects were 40 nursing students working in department of nursing K Univ. in C city. This study was approached by phenomenological method. Collected data were analyzed by Colaizzi's method. The results were from the protocol 980 significant statements and organized into 240 formulated meanings. From formulated meaning 89 themes were identified, organized into 18 them clusters, and then into 16 categories. The nursing students took part in the practice with (expectation and readiness) different from those of the clinical practice, expressed wonder at the school which had progressed much more than their primary schools used to be. They said that they began to feel (Fatigue and stress), and that experienced tension for the lack of nursing knowledge and skill during the health education and clinical treatment activity. In addition, they experienced 'ambivalence of satisfaction and something wanted', that is to say, they could have done better by means of video education and health education. The 10-day-school health practice brought about the change in( the image of teacher) and (cognition about the nursing teacher's role), made the students have(love to the client) and (desire to be nursing teacher), and then turned out experience benefical enough to be expressed 'satisfaction' However, they pointed out many problems in (School Eniviroment), (Clinic), (Physical assessment), (Recording and reporting), so they had a chance to apply the school nursing process to the school. The professor should play the role of promoting the learning through the field practice and providing the stimulant of learning to help the learner get as much from the field situation as they could. Therefore, I suggest that the students always have a chance to exchange actual affairs and educational study, and that the concrete discussion and continuous cooperation be done. The professors should keep doing their best to find the way to professors should keep doing their best to find the way to promote the ability of thinking through the process the learners experienced themselves.

• Korea Journal of Hospital Management
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• v.24 no.2
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• pp.84-101
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• 2019

Purposes: Organizational socialization introductory education for new members is a representative organizational socialization program that enables new members to become true members of an organization and grow into the people they expect from the organization. I analyzed the case of Pop Art Cooperative Learning introductory education for the socialization of the entire organization, which was conducted by Eunpyeong St. Mary's Hospital of Catholic University, in 13 steps, one month before its opening on April 1, 2019. Methodology: In order to analyze the case, the case study was analyzed in order from education planning to preparation, implementation and evaluation. The important point in this process is that all the members to be input at the start of the treatment are not included in the position and occupation. In the composition of education contents, the core keywords for achieving the vision are derived through each group activity and strategies for achieving spirituality, vision, and vision of Hospital are made and the ideal image of Eunpyeong St. Mary's Hospital is completed using the pop art technique. All the works produced by each group will be shared by all attendees of the relevant education level and the whole picture of each education order will be completed again with the big picture called - The heart of Jesus Christ the healer♥ - Respectively. Findings: Education Results All the participants showed high satisfaction and they shared the vision and mission of Eunpyeong St. Mary 's Hospital as well as the formation of intimacy and belonging to each other. They recognized the future direction and goal. And contribute to tissue stabilization. Practical Implications: Through this case study, it will be possible to contribute to establishment and implementation of introductory education plan for new members' socialization to new hospitals.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.197-202
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• 2021

Biometric technology using finger veins is receiving a lot of attention due to its high security, convenience and accuracy. And the recent development of deep learning technology has improved the processing speed and accuracy for authentication. However, the training data is a subset of real data not in a certain order or method and the results are not constant. so the amount of data and the complexity of the artificial neural network must be considered. In this paper, the deep learning model of Inception-Resnet-v2 was used to improve the high accuracy of the finger vein recognizer and the performance of the authentication system, We compared and analyzed the performance of the deep learning model of DenseNet-201. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly. There is no preprocessing for the image in the finger vein authentication system, and the results are checked through EER.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.39-60
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• 2014

In domestic university libraries, the difference between the knowledge resource collection activities on campus is depending on the size of the university, and their collection is concentrated on some types of digital resources. In recent years, the main universities in developed countries has developed actively in social openness and share activities of their knowledge resources, through the OA-based institutional repository, for the purpose of image improvement and competitiveness as a knowledge production base. This study examined ways to improve the relevant regulations in order to effectively collect and systematically manage the knowledge resources from graduate school, research institutes, center for teaching and learning, e-learning center, museum, press, a variety of campus organizations, so as to enhance the role of the library as the right manager of knowledge resources on campus. To this end, this study, considering the improvement of relevant regulations, investigates the operating situation of the library regulations of 176 universities and suggests necessary improvement methods in order to facilitate the digital legal deposit and expand its scope.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The Journal of Bigdata
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• v.5 no.1
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• pp.209-225
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• 2020

This study tried to establish a base level for the form of ginseng in order to prevent fraud in which novice consumers, who have no information on ginseng and mountain ginseng, regard ginseng as mountain ginseng. To that end, researchers designed a service design in which when a consumer takes a picture of ginseng with an APP dedicated to a smartphone, the photo is sent remotely and the determined results are sent to the consumer based on machine learning data. In order to minimize the difference between the data set in the research process and the background color, location, size, illumination, and color temperature of the mountain ginseng when consumers took pictures through their smartphones, the filming box exclusively for consumers was designed. Accordingly, the collection of mountain ginseng samples was made under the same controlled environment and setting as the designed box. This resulted in a 100% predicted probability from the CNN(VGG16) model using a sample that was about one-tenth less than widley required in machine learning.