• Korean Journal of Fisheries and Aquatic Sciences
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• v.33 no.6
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• pp.514-519
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• 2000

In order to develop the method for the calculation of flow resistance acting on cage net, the relation between the velocity reduction factor and $S_n/S$, the ratio of total area of netting projected to the perpendicular to the water flow $S_n$ to wall area of netting S, was derived based on the numerical and experimental analysis of the wake flow through a netting twine simplified by a cylinder and a netting panel. The velocity was reduced in accordance with the velocity reduction factor when the flow passed the netting panel upstream of a cage net. The proposed method for the calculation of fluid force acting on a square cage net was based upon the assumption that it could be divided into four side panels and one bottom panel. It was proved that the force could be calculated by the sum of the drag forces acting on the individual netting panels.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1407-1416
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• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.

• Journal of IKEEE
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• v.25 no.4
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• pp.750-755
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• 2021

This paper proposes techniques for detecting the damage status of each part of a vehicle using YOLOv4. The proposed algorithm learns the parts and their damages of the vehicle through YOLOv4, extracts the coordinate information of the detected bounding boxes, and applies the algorithm to determine the relationship between the damage and the vehicle part to derive the damage status for each part. In addition, the technique using VGGNet, the technique using image segmentation and U-Net model, and Weproove.AI deep learning model, etc. are included for objectivity of performance comparison. Through this, the performance of the proposed algorithm is compared and evaluated, and a method to improve the detection model is proposed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of KIBIM
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• v.2 no.2
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• pp.17-26
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• 2012

This paper presents the results of a theoretical energy analysis of a research test bed called the Zero Net Energy Test House (ZNETH) in Omaha, Nebraska in U.S.A. The ZNETH project is being designed and built with the goal of consuming a negligible amount of energy by offsetting remaining usage after energy conservation. The theoretically consumed and generated energy levels were analyzed using energy modeling software programs. By integrating a highly graphical and intuitive analysis with a Building Information Model(BIM) of the house, this investigation introduces strategies to include sustainable materials and systems to predict energy generation with a case study of ZNETH. In addition, this paper introduces parametric analyses for better envelope design and construction material selection by analyzing simulated energy consumption with various parametric inputs, e.g., material types, location, and size. It was found that the current design of ZNETH does not meet its goal of zero net energy. Sugeestions are presented to assist ZHETH in meeting its net zero energy goal.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Nuclear Engineering and Technology
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• v.41 no.8
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• pp.1073-1078
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• 2009

A kinetic model for the oxidation of a $UO_2$ pellet to $U_3O_8$ powder has been suggested by considering the mass transfer and the diffusion of oxygen molecules. The kinetic parameters were estimated by a fitting of the experimental data. The activation energies for the chemical reaction and the product layer diffusion were calculated from the kinetic model. The oxidation conversion of a $UO_2$ pellet was simulated at various operating conditions. The suggested model explains the oxidation behavior of $UO_2$ well.

• Journal of Information Technology Applications and Management
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• v.13 no.3
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• pp.107-127
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• 2006

Understanding characteristics of Internet businesses from cross-cultural perspective could offer valuable insights on developing business strategy and policy. This work is concerned with revealing divergence and convergence of Internet business models in their financial performance, given organizational conditions and cultural context. For this, we studied the association between organizational attributes (core activity, origination, firm age, and industry) and their effects on a firm's financial performance (gross revenue and net income). Relevant data was gathered from representative Internet firms in Korea and U.S. Data analysis indicated that there exist both similarities and differences between Korea and U.S and year 2003 and 2006. While core activities and industry types of U.S. firms has not been changed much between the periods, Korean firms show much difference. In addition, while core activities and industry type were found to have strong relationship with financial performance, age and origination of a firm weak connections with financial performance. This study is expected to provide a foundation for developing more robust and systematic research model and performing further empirical research in this area.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.534-540
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• 2017

The adsorption of uranium (VI) by calcium alginate beads was examined by batch experiments. The effects of environmental conditions on U (VI) adsorption were studied, including contact time, pH, initial concentration of U (VI), and temperature. The alginate beads were characterized by using scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy. Fourier transform infrared spectra indicated that hydroxyl and alkoxy groups are present at the surface of the beads. The experimental results showed that the adsorption of U (VI) by alginate beads was strongly dependent on pH, the adsorption increased at pH 3~7, then decreased at pH 7~9. The adsorption reached equilibrium within 2 minutes. The adsorption kinetics of U (VI) onto alginate beads can be described by a pseudo first-order kinetic model. The adsorption isotherm can be described by the Redlich-Peterson model, and the maximum adsorption capacity was 237.15 mg/g. The sorption process is spontaneous and has an exothermic reaction.