• Korean Journal of Human Ecology
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• v.23 no.4
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• pp.665-680
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• 2014

The purpose of this study was to develop the construction process of orthopedic compression garments (OCG) for balancing of the left and right lumbar muscle power and strength to prevent low back pain. One male subject having low back pain was involved for investigating of the lumbar muscle power. EMG (Telemyo DTS2, Noraxon, U.S.A) was measured with/ without 3 types of waist assistant belt around the waist area of the subject. Based on the electromyogram value of left and right body, OCG were constructed as follows. Firstly, stretchable t-shirts type with supportive waist belt was selected for the convenience of wearing and laundering the OCG. The design lines of the front and back waist parts were created depending on the anatomy of the torso. Secondly, 3D pattern was developed using 3D Clo, RapidForm XOR, 2C-AN, and Yuka CAD program to increase the fit of the OCG. Finally, stretchable power-net was layered as linings in two ways, a single lining and double layered linings, and evaluated measuring lumbar muscle EMG by five subjects with low back pain. As the results, they were effective to balance the left and right lumbar muscle power and strength. Also the OCG with the double layered power-net lining was superior to the one layered lining in terms of fit and comfort.

• Review of Korean Society for Internet Information
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• v.14 no.4
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• pp.63-76
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• 2013

This short report introduces the general background why Brazil conference is being prepared and what topics would be undertaken and what goals are being taken into account. It overviews what differences from traditional telecommunication governance, internet governance has had in its historical development and how such differences had been formed from its technological differences and the regulatory policy shift from common carrier regulation to privatization. Moreover, the fact that open, voluntary, bottom-up, diverse stakeholder's participation had evolved throughout the historical development of the internet, had established the present multistakeholder governance model from technological standardization to addressing scheme policies. ICANN, which has governed internet addressing schemes since the earlier 2000s, had developed address policies including IANA function from Jon Postel and technical community's legacy management system into contract based formation between ICANN and gTLD, ccTLD registries. And it made dispute resolution policies responding to trademark disputes and resolved gTLD monopoly issue by introducing new TLD generation and the separation of registry and registar. However, there had been challenges on the legitimacy of ICANN due to its dependency on the Federal Government of the U.S. particularly in its oversight role over ICANN and IANA contract. WSIS raised up internet governance issues including addressing governance, and set up IGF as a discussion platform for multistakeholders to discuss and share all views on other internet related public policies. IGF's loose and non-binding discussion once frustrated governments and other stakeholders, but more focused discussion and visible outcomes have consolidated its unique role for internet governance discourses. Particularly, IGF addressed many emerging internet related issues like cybersecurity, privacy, net neuratlity, development related issues. WTPF of 2013, after WCIT debate on whether traditional telecommunication regulation could be applied to internet infrastructure, suggested other governance issues such as the transition to ipv6, IXP coordination etc. How to make sure the legitimacy of internet addressing governance and how and where other internet related public policies could be undertaken are fundamental tasks for internet governance. Brazil conference, which has been motivated by the breakdown of trust in internet governance from NSA mass surveillance revealed by Snowden, faces these questions and try to make consensus on principles, institutions and roadmap for internet governance in multistakeholder participation way.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.884-894
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• 2023

Design and safety assessment of fuel pins for application in innovative Generation IV fast reactors calls for a dedicated nuclear fuel modelling and for the extension of the fuel performance code capabilities to the envisaged materials and irradiation conditions. In the INSPYRE Project, comprehensive and physics-based models for the thermal-mechanical properties of U-Pu mixed-oxide (MOX) fuels and for fission gas behaviour were developed and implemented in the European fuel performance codes GERMINAL, MACROS and TRANSURANUS. As a follow-up to the assessment of the reference code versions ("pre-INSPYRE", NET 53 (2021) 3367-3378), this work presents the integral validation and benchmark of the code versions extended in INSPYRE ("post-INSPYRE") against two pins from the SUPERFACT-1 fast reactor irradiation experiment. The post-INSPYRE simulation results are compared to the available integral and local data from post-irradiation examinations, and benchmarked on the evolution during irradiation of quantities of engineering interest (e.g., fuel central temperature, fission gas release). The comparison with the pre-INSPYRE results is reported to evaluate the impact of the novel models on the predicted pin performance. The outcome represents a step forward towards the description of fuel behaviour in fast reactor irradiation conditions, and allows the identification of the main remaining gaps.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.