• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.123-131
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• 2019

In this study, we modified CT images of femoral head in consideration of anatomically meaningful structure, proposing the method to augment the training data of convolution Neural network for segmentation of femur mesh model. First, the femur mesh model is obtained from the CT image. Then divide the mesh model into meaningful parts by using cluster analysis on geometric characteristic of mesh surface. Finally, transform the segments by using an appropriate mesh deformation algorithm, then create new CT images by warping CT images accordingly. Deep learning models using the data enhancement methods of this study show better image division performance compared to data augmentation methods which have been commonly used, such as geometric conversion or color conversion.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.369-376
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• 2019

This study aimed to identify the relationship between perfectionism and motivational climate in competitive athletes and to provide information for improvement of their performance. One hundred ninety-six athletes who trained in Korea National Training Center participated in this study and they were divided into record and man-to-man events. Also they filled in the questionnaire about demographic factors, perfectionism, and motivational climate. Collected data were analyzed by using cross validation and independent t-test for identifying the difference of two events and structural equation model for testing hypotheses and model fit. Perfectionism and motivational climate in man-to-man events were stronger compared to record event. In record event, perfectionism was more influenced by ego involving motivational climate compared to task involving, while in man-to-man event, perfectionism was affected by only ego involving motivational climate. However, these both study models had a bad fit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1631-1638
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• 2018

Korea has experienced an increase in the number of immigrants in accordance with accelerated globalization. In particular, the rapid surge in international marriages and multicultural families has created social adaptation or inclusion problems for these families in Korean society. This paper examines how information services or technological education programs affect the social integration of marriage migrant women in Korea. Using survey data on international marriage migrant women, we conducted multiple and logistic regression models to examine the relationship between information and technology services and life satisfaction and employment status. The results show that the experience of using social services tends to have positive effects on life satisfaction. Having experiences of information services or technological education improves marriage migrant women's chances of employment and increases their willingness to work or start a business. This indicates the importance of information services or technological educational training and social services in improving social adaptation and integration of marriage migrant women in Korea.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.37-47
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• 2021

In this paper, we propose a new deep learning model for tongue segmentation with improved accuracy compared to the existing model by diversifying the receptive field in the U-net. Methods such as parallel convolution, dilated convolution, and constant channel increase were used to diversify the receptive field. For the proposed deep learning model, a tongue region segmentation experiment was performed on two test datasets. The training image and the test image are similar in TestSet1 and they are not in TestSet2. Experimental results show that segmentation performance improved as the receptive field was diversified. The mIoU value of the proposed method was 98.14% for TestSet1 and 91.90% for TestSet2 which was higher than the result of existing models such as U-net, DeepTongue, and TongueNet.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.79-85
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• 2021

A standard engineering model that reflects the current organization system and engineering operation process of domestic nuclear power plants was developed based on the Standard Nuclear Performance Model developed by the American Nuclear Energy Association. The level 0 screen, which is the main screen of the engineering model computer system, consisted of an object tree structure, which provided information that is phased down from a higher structure level to a lower structure level (i.e., level 3). The level 1 screen provided information related to the sub-process of the engineering operation, whereas the Level 2 screen provided information related to each engineering operation activity. In addition, the Level 2 screen provided additional functions, such as linking electronic procedures/guidelines, providing electronic performance forms, and connecting legacy computer systems (such as total equipment reliability monitoring system, configuration management systems, technical information systems, risk monitoring systems, regulatory information, and electronic drawing system). This screen level increased the convenience of user's engineering tasks by implementing them. The computerization of an engineering model that connects the entire engineering tasks of an establishment enables the easy understanding of information related to the engineering process before and after the operation, and builds a foundation for the enhancement of the work efficiency and employee capacity. In addition, KHNP developed an online training module, which operates as an e-learning process, on the overview and utilization of a standard engineering model to expand the understanding of standard engineering models by plant employees and to secure competitiveness.

• Journal of Korean Academy of Nursing
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• v.50 no.6
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• pp.740-756
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• 2020

Purpose: The purpose of this study was to evaluate the effect on blood pressure (BP) and heart rate (HR) according to aerobic exercise characteristics in adults with hypertension using a systematic review and meta-analysis. Methods: The related researches were selected from PubMed, EMBASE, Cochrane library, CINAHL, PsycINFO, SPORTDiscus and 5 domestic databases up to September 4, 2019. To estimate the effect size, random effect models were used to derive weighted mean differences (WMD) and their 95% confidence intervals (CI) of aerobic exercise on BP and HR. Results: A total of 37 RCTs with 1,813 samples were included. Aerobic exercise was found to significantly reduce systolic BP (WMD, - 8.29 mmHg; 95% CI, - 10.12 to - 6.46), diastolic BP (WMD, - 5.19 mmHg; 95% CI, - 6.24 to - 4.14) and HR (WMD, - 4.22 beats/min; 95% CI, - 5.36 to -3.09). In detail, systolic BP and diastolic BP were significantly decreased in all groups of exercise types, frequency and duration. Systolic BP and diastolic BP were significantly decreased in the moderate and vigorous-intensity group. Exercise characteristics with the most dramatical change in systolic BP were water-based training, moderate-intensity, 3 times a week and 8 to 11 weeks of duration. In diastolic BP, the greatest effect size was over 24 weeks of exercise. Conclusion: Moderate aerobic exercise, especially water-based exercise can be an important part of lifestyle modification for hypertensive patients. Also, it can be recommended in a variety of clinical settings for lowering BP and HR. However, there is insufficient evidence that low-intensity exercise is effective in lowering BP.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.69-76
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• 2021

Recentlly neural network approach for solving a singular perturbed integro-differential boundary value problem have been researched. Especially the model of the feed-forward neural network to be trained by the back propagation algorithm with various learning algorithms were theoretically substantiated, and neural network models such as deep learning, transfer learning, federated learning are very rapidly evolving. The purpose of this paper is to study the approaching method for developing a neural network model with high accuracy and speed for solving singular perturbed problem along with asymptotic methods. In this paper, we propose a method that the simulation for the difference between result value of singular perturbed problem and unperturbed problem by using neural network approach equation. Also, we showed the efficiency of the neural network approach. As a result, the contribution of this paper is to show the possibility of simple neural network approach for singular perturbed problem solution efficiently.

• Journal of IKEEE
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• v.24 no.4
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• pp.1122-1128
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• 2020

Bandwidth Extension refers to restoring and expanding a narrow band signal(NB) that is damaged or damaged in the encoding and decoding process due to the lack of channel capacity or the characteristics of the codec installed in the mobile communication device. It means converting to a wideband signal(WB). Bandwidth extension research mainly focuses on voice signals and converts high bands into frequency domains, such as SBR (Spectral Band Replication) and IGF (Intelligent Gap Filling), and restores disappeared or damaged high bands based on complex feature extraction processes. In this paper, we propose a model that outputs an bandwidth extended signal based on an autoencoder among deep learning models, using the residual connection of one-dimensional convolutional neural networks (CNN), the bandwidth is extended by inputting a time domain signal of a certain length without complicated pre-processing. In addition, it was confirmed that the damaged high band can be restored even by training on a dataset containing various types of sound sources including music that is not limited to the speech.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.1-9
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• 2019

With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.