• Journal of Digital Convergence
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• v.17 no.11
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• pp.467-474
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• 2019

Key areas of digital contents, the games and animation industries are increasingly expanding. Therefore, training of a specialized workforce is required in accordance with these enterprises' growing demand. Education in the field of games and animation lies in cultivating talents with creative thinking, collaboration, and problem-solving skills. Thus, this paper aims to propose a PBSL teaching model for creative convergent talent through game and animation projects. The study will focus on the characteristics of creative convergence talents, project teaching, and related job competencies for game and animation education. Based on literature research, a 'Project Based Self Learning' instructional model is presented, in which creative thinking and collaboration competencies are explained in a way they can be performed by the learner. As a case study, D University's class was applied with PBSL. A survey showed that the autonomy aspects were higher than the creativity and convergence attitudes, indicating that the students improved their autonomy and motivation. However, the team composition needs further supplementation.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.263-272
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• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.416-421
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• 2021

In case of the road slips due to heavy snow and the temperature drops below 0 degrees, black ice which mainly occurs on the road, bridges for vehicles, and tunnel entrances, is not recognized by the driver's view because the image of the asphalt is transmitted through it. So cars' slip situation occurs, which leads to a big traffic accident and a large amount of loss of life and property. This study proposes a method to check the road condition using an infrared camera and to identify black ice through deep learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.703-708
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• 2022

Tables and figures in academic literature contain important and valuable information. Tables and figures represent the essence of the refined study, which is the closest to the raw dataset. If so, can researchers easily access and utilize these image data through the search system? In this study, we try to identify user perceptions and needs for image data through user and case studies. Through this study we also explore expected effects and utilizations of image search systems. It was found that the majority of researchers prefer a system that combines table and figure indexing functions with traditional search functions. They valued the provision of an advanced search function that would allow them to limit their searches to specific object types (pictures and tables). Overall, researchers discovered many potential uses of the system for indexing tables and figures. It has been shown to be helpful in finding special types of information for teaching, presentation, research and learning. It should be also noticed that the usefulness of these systems is highest when features are integrated into existing systems, seamlessly link to fulltexts, and include high-quality images with full captions. Expected effects and utilizations for user-centered image search systems are also discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.234-239
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• 2013

Learning a lesson about poor concentration and problems of the existing content, the system of language which could not be determined, Many teachers' assessment decision was made. As a result, voice recording based on the combination of ubiquitous technology and virtual reality technology, and install the projector in a classroom Through the learning content corresponding grade English student ID card attached RFID reader in each classroom, and students of RFID tags attached. In reality of the virtual three-dimensional image content foreigners and question-and-answer using the voice recording technology at the same time check the pronunciation and intonation level passes or level failure judged. Student education data to a central server system is configured to do so after saving to the DB through a feedback process, which provides information. Analysis of the issues that can have a common language content in the present study and Problem for voice recording technology to solve the problem and did not solve the existing language in the content level based classes.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.97-100
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• 2016

Using the CNN(Convolutinal Neural Network), Deep Learning for variety of fields are being developed and these are showing significantly high level of performance at image recognition field. In this paper, we show the test accuracy which is learned by large-scale training data, over 5,000,000 of Korean characters. The architecture of CNN used in this paper is KCR(Korean Character Recognition)-AlexNet newly created based on AlexNet. KCR-AlexNet finally showed over 98% of test accuracy. The experimental data used in this paper is large-scale Korean character database PHD08 which has 2,187 samples for each Korean character and there are 2,350 Korean characters that makes total 5,139,450 sample data. Through this study, we show the excellence of architecture of KCR-AlexNet for learning PHD08.

• Journal of the Korean Society of Fashion and Beauty
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• v.1 no.1 s.1
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• pp.65-78
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• 2003

This research is to provide practical help in learning hair and makeup illustration skills by presenting techniques for hair and makeup drawing; to serve efficient illustration education; and to enhance the status of beauty and contribute to artistic development. Hair style and makeup techniques include graphical one, pattern-centered one, one using pattern paper, simplifying one and mood one expressing image. Of them, this research made the illustrations to use cosmetics, color pencils and pastel based on the graphical technique. for each design of the illustrations, ethnic, sexy, natural, romantic and gorgeous images, which were considered to be appropriate to the graphical technique, were chosen by the researcher out of hair and makeup styles that appeared in the fashion magazines including Vogue, Gap, Mode et Mode from 2000 through 2001. In particular, they were chosen with focusing on basic styles. The summaries below were found with the experience of making illustrations. Various techniques and skills are required to express the ideas of hair and makeup styles. Of them, the graphical technique is very useful as the primary step to learn various techniques and improve drawing skills. First, the graphical technique may enable not only expressing what is desired to draw as is, but also accurately representing hair and makeup designs so as to convey objective expression. In this regard, it is a proper way to achieve its inherent purpose as conveyance of messages. Second, more accurate styling of hair and makeup is available through graphical expression, which helps understand related practical techniques. In addition, makeup illustration, which is expressed through direct makeup products and instruments, may serve skill improvement since such direct use provides the feeling of real makeup. Third, the graphical technique as a basic drawing skill may unrestrictedly show the artist's expression ability. Fourth, although artistic merits implying individuality and creativity should be shared through illustrations that express the artist's ideas or emotions, the graphical technique is the easiest method to beginners who just started learning of illustration, in that it enables expression without highly advanced skills.