• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3696-3705
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• 2014

This study examined the nurse images perceived by nursing students. The study was specifically meant to determine what images grade 2 and 3 nursing students had about nurses before and after their clinical practice to define the nurse's image. The selected nursing students were interviewed to obtain their opinions on the definition and necessity of a nurse and what a great nurse should be like as well as their prejudice about nurses, and content analysis was carried out to categorize their statements. As a result, 48 significant statements and 14 categories were selected. The findings of the study might not be generalizable because the subjects in this study were selected by convenience sampling from two different nursing departments located in Gangwon Province and Gyeonggi Province. More concrete and reliable results are expected if more students from more geographic regions are investigated.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.85-93
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• 2017

The Purpose of this study was to exam the relationships among nurse image, nursing professionalism and major satisfaction and mediating effect. A convenience sample of 200 nursing students were recruited from a college in G city, from May 12 to 16. 2014. Data were collected using structured questionnaires and analyzed with SPSS/PC ver 21.0 programs. There were significantly positive correlations between nurse image, nursing professionalism and major satisfaction. Nursing professionalism fully mediated the relationship between nurse image and major satisfaction. These findings indicate that increasing nursing professionalism can be an effective way to change nurse image more positive among nursing students, especially before clinical practice. A collaborative effort among nursing education and nursing leaders in practice is needed to change nurse image more positive.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.166-172
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• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• Land and Housing Review
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• v.3 no.4
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• pp.323-331
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• 2012

The importance of corporate social responsibility has been more highlighted recently for the sustainable management of the company. The purpose of this study is to empirically find out if the effects of CSR activities on public corporation are shown as same as they are on the private companies which have been verified in the previous studies. We set 'CSR activities' and 'Brand Image' to the independent variables and 'Corporate favorableness' and 'Purchase intention' to the dependent variables. The empirical analysis results are as follows. We have found that high positive causal relations are shown in 'CSR activities' ${\rightarrow}$ 'Corporate favorableness', 'CSR activities' ${\rightarrow}$ 'Purchase intention' and 'Brand image' ${\rightarrow}$ 'Corporate favorableness', 'Brand image' ${\rightarrow}$ 'Purchase intention'. Therefore just as product related 'Brand image' influences positively on purchase intention, the CSR activities have positive influence on product purchase intention and on the whole corporate image as well.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.51-58
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• 2012

Scirtothrips Dorsalis (Thysanoptera: Thripidae) recently has been recognized as a major source of the pest damage in the citrus fruit orchards. So its arrival has been predicted periodically but it is difficult to identify adults of the pest with the naked eyes because of their size smaller than the 0.8mm. In this paper, we propose a method to detect candidate areas for automatic identification of Scirtothrips Dorsalis on forecasting traps. The proposed method uses a histogram-based template matching where the composite image synthesized with the gray-scale image and the gradient image is used. In our experiments, images are acquired by the optical microscopy with 50 magnifications. To show the usefulness of the proposed method, it is compared with the method we previously suggested. Also, the performances when the proposed method is applied to noise-reduced images and gradient images are examined. The experimental results show that the proposed method is approximately 14.42% better than our previous method, 41.63% higher than the case that the noise-reduced image is used, and 21.17% higher than the case that the gradient image is used.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.463-474
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• 2008

The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.351-362
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• 2021

The purpose of this study is to examine the structural relationships among physical environment of coffee shops, brand image and revisit intention and to analyze the moderating effects of emotional responses. The results of this study are as follows: First, among physical environment, spatiality, cleanliness, comfortability and attractiveness had significant influences on brand image. Second, physical environment had significant influences on revisit intention. Third, brand image had significant influences on revisit intention. Fourth, emotional responses had moderating effects in the relationship between attractive factor and brand image. Finally, the conclusion section suggested strategic implications to induce physical environment, brand image, revisit intention and emotional responses based on the research findings.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• Journal of the Korean Applied Science and Technology
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• v.38 no.5
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• pp.1314-1324
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• 2021

This study attempted to understand the nurse image and major commitment of nursing students, investigate their relationship, and prepare necessary measures to improve the major commitment of nursing students. The data were collected from 185 nursing students enrolled in seven nursing colleges and were analyzed statistically with the SPSS/WIN 24.0 program. The research results are as follows. It was confirmed that the more positive the nurse image perceived by the subject, the higher the major commitment. In addition, influencing factors the major commitment of nursing students identified in the order of major satisfaction, perception of nursing, nurse image, and aptitude and interest among nursing selection motives, and total explanatory power was 42%. Based on the above results, it is necessary to develop and apply a program including a positive nurse image for nursing students.