• Journal of dental hygiene science
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• v.22 no.1
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• pp.1-8
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• 2022

Background: The elderly have, a higher disease morbidity than other age groups due to a decrease in resistance to the disease and have complex diseases, so care should be taken. Accordingly, it is considered important to provide information for improving the health of the elderly. Health information plays an important role in individual health promotion and education, so the degree of exposure to information about oral health of the elderly is expected to have a significant impact on understanding and acquiring information on oral content videos on the importance, prevention, and management of oral health of the elderly in the future. Methods: This study analyzed video content related to oral diseases of the elderly in a total of 150 videos uploaded on YouTube from January 1, 2012 to May 13, 2021, using a total of three books of dental hygiene for the elderly. Results: Forty-nine broadcasters accounted for the most of this information. Among the information providers, there were two dental hygienists. They accounted for 1.3% of all the information providers. The highest number of dental hygienists who broadcasted information was 42 in 2019. The average number of views was 37,303 periodontal diseases, the highest. Among the videos, dry mouth was the most common with 34 oral diseases. Conclusion: The number of images for each disease varies, so it seems that information should be provided in various ways. Dental hygienists should widely improve oral health knowledge by providing various dental hygiene management images for each oral disease to improve the oral health of the general public. In addition, based on the information of the Health Insurance Review and Assessment Service, the development and provision of content should be actively carried out so that people can obtain the information they desire.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.40.3-41
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• 2021

Minor mergers leave behind long lived, but extremely faint and extended tidal features including tails, streams, loops and plumes. These act as a fossil record for the host galaxy's past interactions, allowing us to infer recent accretion histories and place constraints on the properties and nature of a galaxy's dark matter halo. However, shallow imaging or small homogeneous samples of past surveys have resulted in weak observational constraints on the role of galaxy mergers and interactions in galaxy assembly. The Rubin Observatory, which is optimised to deliver fast, wide field-of-view imaging, will enable deep and unbiased observations over the 18,000 square degrees of the Legacy Survey of Space and Time (LSST), resulting in samples of potentially of millions of objects undergoing tidal interactions. Using realistic mock images produced with state-of-the-art cosmological simulations we perform a comprehensive theoretical investigation of the extended diffuse light around galaxies and galaxy groups down to low stellar mass densities. We consider the nature, frequency and visibility of tidal features and debris across a range of environments and stellar masses as well as their reliability as an indicator of galaxy accretion histories. We consider how observational biases such as projection effects, the point-spread-function and survey depth may effect the proper characterisation and measurement of tidal features, finding that LSST will be capable of recovering much of the flux found in the outskirts of L* galaxies at redshifts beyond local volume. In our simulated sample, tidal features are ubiquitous In L* galaxies and remain common even at significantly lower masses (M*>10^10 Msun). The fraction of stellar mass found in tidal features increases towards higher masses, rising to 5-10% for the most massive objects in our sample (M*~10^11.5 Msun). Such objects frequently exhibit many distinct tidal features often with complex morphologies, becoming increasingly numerous with increased depth. The interpretation and characterisation of such features can vary significantly with orientation and imaging depth. Our findings demonstrate the importance of accounting for the biases that arise from projection effects and surface-brightness limits and suggest that, even after the LSST is complete, much of the discovery space in low surface-brightness Universe will remain to be explored.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• Journal of English Language & Literature
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• v.57 no.6
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• pp.939-958
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• 2011

Ezra Pound has an idea of poetry as a field of energy in which words interact with each other with kinetic energy. The energy field which Pound creates in his poem is analogous to the theory of electromagnetism developed by Michael Faraday and James Maxwell, who look upon the space around magnets, electric charges and currents not as empty but as filled with energy and activity. Pound argues that "words are charged with force like electricity," demonstrating that words charged with their own images or energies of positive or negative valence interact one another. This idea is similar to Faraday's concept of "line of force" which he used to represent the disposition of electric and magnetic forces in space. Pound's concept of "image" as an "intellectual and emotional complex in an instant" is remarkably consonant with the confluence of electric and magnetic fields that are coupled to each other as they travel through space in the form of electromagnetic waves. The instant profusion of conception and perception, much like that of electric and magnetic fields, enables Pound to move beyond the sequential and linear hierarchy in time and space. Particularly, Maxwell's stunning discovery that the electromagnetic waves propagate in space at 'the speed of light' has allowed Pound a relativistic sense of escape from the limitations of Newtonian absolute time and space. Pound's poetry transcends any geographical space and sequential time by rendering and juxtaposing images simultaneously. Pound was fully aware of light and electricity fundamental to what he called his world "the electric world." Pound's experiments in Imagism and Vorticism can be considered an attempt to rediscover a place for poetry in the modern world of science and technology. Almost all the appliances that we think of today as modern were laid down in the closing decades of the 19th century and the first decades of the 20th century, in response to the availability of electromagnetic energy. This paper explores how Pound responded to the age of modern technology and science, examining his conception of "image" through his many analogies and similes drawn from electromagnetism. Pound's imagist poetics and poetry come to embody, not only the characteristics of the electric age in the early twentieth century, but the principles of electromagnetism the electric age is based upon.

• Journal of IKEEE
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• v.28 no.1
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.324-333
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• 2024

This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A^* and Rapidly-exploring Random Tree star (RRT^*) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.

• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.1
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• pp.99-112
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• 2011

The purpose of this research is to study India's traditional embroidery method, Mirror Work, and to evaluate the examples of contemporary costumes as well as the applications of art to clothing that have been influenced by this technique, in order to expand its usage for creation of a new fashion image. Research in the literature and application of works related to Mirror Work have demonstrated: First of all, as a traditional embroidery method that represents the folk art of India, Mirror Work displays unique methods used in different regions and the way that various methods and materials were combined by the use of mirrors, beads, and $appliqu{\acute{e}}$. Secondly, it was found that the presentation of Mirror Work in the $pr{\hat{e}}t$-a-porte collection is based on a traditional embroidery method using both developed materials and adapted methods to express traditional reproducibility, geometric simplicity, and aesthetic characteristics of complex decorations. Thirdly, new plasticity for art to wear clothing can be created through various methods aside from embroidery, for example by a technique of wrapping crochet laces and tapes around the mirror for decorative purposes. Based on these results it can be inferred that, from the perspective of multiple forms for decorations, Mirror Work shares multiple forms of personal aesthetic goals through the mirror's unique quality for expression and enhanced images of artistic decorative art. Also, the introduction of traditional materials and methods for today's folk art and traditional costumes can be the subject of unique aesthetic characteristics based on different perspectives of the recreation of tradition. Finally, it can further create a new plasticity within the globalization phenomena.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.1
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• pp.56-63
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• 2014

An offshore plant needs costly maintenance and has difficulty coping with various accidents coming from the exposure to the environmental threats such as typhoons, tidal waves and etc., in addition to the artificial ones such as fire, collision of ships and etc. In this paper, we develop the video-complex remote monitoring system for an offshore plant, using AtoN AIS and multi-stage database to monitor an offshore plant and solve those problems. The system handles real time video cameras to collect and monitor images on an offshore plant. So, users can be exactly and quickly aware of the information on various situations with the monitoring application based on ENC.

• Journal of The Korean Association of Information Education
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• v.20 no.5
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• pp.527-534
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• 2016

This study is an analysis of Software image using the semantic differential scale with elementary students. We have selected the items in a total of 35 pairs of test about software-related image adjectives and are categorized into 7 main factors, and then analyzed the entire image with the students. The analysis of the differences between the software images of sex, the female students than male students were recognized the software is complex, slow and difficult and do not want to have. The analysis of the self-awareness on the software, the students who know that well recognized for the software select the positive term for the software. The inter-grade analysis are the older grade students were the answer to the objective features of the software like more difficult and complex.