• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6447-6454
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• 2013

Seawater intake systems have significant problems due to seawater pollution, suspended solids, unstable intake and maintenance etc. An underground type seawater intake system was newly developed to overcome the existing weaknesses and was facilitated in Gyukpo port. In this study, to check the performance of the new system, the samples for water quality and the 3-D numerical modeling test were conducted. The five times test included the COD, total nitrogen, total phosphorus, pH, and suspended solid for the intake system. The analyses show that the COD, total nitrogen, total phosphorus, PH showedminor changes before and after. On the other hand, the change in suspended solids was significant and water was purified below 5 mg/l, first level fisheries water, after. The numerical model adopted the RNG $k-{\epsilon}$ algorithm and the CFX model based on the finite volume method. The porosity algorithm was used to reproduce filtered-sand, outer diameter, and thickness. The numerical results showed that the double pipe is advantageous in that it provides a uniform pressure between the inner and outer pipe for the flow to be stable. In addition, the use of multiple intake pipes did not interfere with the discharge reduction of 0.98 at the both intake pipes compared with the central intake pipe.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1167-1176
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• 2004

Along the advent of Internet technology, the computing environment has been considerably changed in many application domains. Especially, a lot of researches for e-Logistics have been done for the last 3 years. The e-Logistics means the virtual business activity and service architecture among the logistics companies based on the Internet technology. To construct effectively the e-Logistics framework, researches on the development of the Moving Object Technology(MOT) including GPS and GIS with spatiotemporal databases technique so far has been done The Moving Object Technology stands for the efficient management for the spatiotemporal objects such as vehicles, airplanes, and vessels which change continuously their spatial location along with time flows. However, most systems manage just only the location information detected lately by many reasons so that the uncertainty processing for the past and future location of the moving objects is still very hard. In this paper, we propose the moving object uncertainty model and system design for e-Logistics applications. The MOMS architecture in e-Logistics is suggested and the detailed explain of sub-systems including the uncertainty processor of moving objects is described. We also explain the comprehensive examples of MOMS and uncertainty processing in Delivery Parcel Application that is one of major application of e-Logistics domain.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.129-136
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• 2014

Electromagnetic (EM) methods are generally divided into frequency-domain EM (FDEM) and time-domain EM (TDEM) methods, depending on the source waveform. The FDEM and TDEM fields are mathematically related by the Fourier transformation, and the TDEM field can thus be obtained as the Fourier transformation of FDEM data. For modeling in time-domain, we can use fast frequency-domain modeling codes and then convert the results to the time domain with a suitable numerical method. Thus, frequency-to-time transformations are of interest to EM methods, which is generally attained through fast Fourier transform. However, faster frequency-to-time transformation is required for the 3D inversion of TDEM data or for the processing of vast air-borne TDEM data. The diffusion expansion method (DEM) is one of smart frequency-to-time transformation methods. In DEM, the EM field is expanded into a sequence of diffusion functions with a known frequency dependence, but with unknown diffusion-times that must be chosen based on the data to be transformed. Especially, accuracy of DEM is sensitive to the diffusion-time. In this study, we developed a method to determine the optimum range of diffusion-time values, minimizing the RMS error of the frequency-domain data approximated by the diffusion expansion. We confirmed that this method produces accurate results over a wider time range for a homogeneous half-space and two-layered model.

• Journal of the Korean earth science society
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• v.36 no.6
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• pp.591-608
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• 2015

The purpose of this study was to analyze system thinking revealed in science teachers' teaching practices of middle school astronomy classes. Astronomy lessons were video-taped from four eighth grade science teachers. The video recordings were all transcribed and analyzed by employing a framework for systems thinking analysis after modifying an existing frame of hierarchial structure used in relevant previous studies. In addition, four participants were interviewed in order to uncover their orientation toward teaching using video stimulated recall method. Findings are as follows: All participating teachers were not able to employ the four levels of system thinking appropriately and only utilized the low level of systems thinking. They also demonstrated teacher-centered practices for employing system thinking despite their student-centered orientation toward teaching. The main reason for these results may be that teachers focused more on spatial thinking, than on system thinking as well as the lack of teacher's knowledge about the content and formative assessment of non-earth science teachers. Implications on how to effectively employ the system thinking in astronomy class are discussed in this paper.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.

• Fire Science and Engineering
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• v.29 no.2
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• pp.44-53
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• 2015

This study is a survey research to improve the fire-fighting ambulance interior design safer and more efficient to identify the type of structure and functional problems 119 ambulance. When the paramedics and four degrees to over 755 people modify the target report and related literature on the future development of an ambulance for patient safety at the 2007 British National Patients Safety Agency (NPSA) and was used as a complementary tool. General characteristics questionnaire was composed of items for your design improvements for ambulance promote safety and efficiency. The data were collected by distributing a questionnaire e-mail or in person. The collected data were processed using the SPSS 20.0 statistical program, the general characteristics as frequency analysis, percentage, ambulance interior design improvement-related items were analyzed using the chi-square verified. As a result, this research elicited that vans converted fire ambulance cars have a problem with the narrow interior space and truck converted fire ambulance cars should be comfortable to drive in ride quality. In addition, we also found that the improvement of paramedics treatment position and the paramedic's personnel safety belt are required. Based on these results, we propose that a number of improvements are needed in the fire-fighting ambulance car.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.4
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• pp.221-232
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• 2023

Carbon neutrality requires carbon dioxide reduction technology and alternative green energy sources. Palygorskite is a clay mineral with a ribbon structure and possess a large surface area due to the nanoscale pore size. The clay mineral has been proposed as a potential material to capture carbon dioxide (CO₂) and possibly to store eco-friendly hydrogen gas (H2). We report our preliminary results of grand canonical Monte Carlo (GCMC) simulations that investigated the adsorption isotherms and mechanisms of CO₂ and H₂ into palygorskite nanopores at room temperature. As the chemical potential of gas increased, the adsorbed amount of CO₂ or H₂ within the palygorskite nanopores increased. Compared to CO₂, injection of H₂ into palygorskite required higher energy. The mean squared displacement within palygorskite nanopores was much higher for H₂ than for CO₂, which is consistent with experiments. Our simulations found that CO₂ molecules were arranged in a row in the nanopores, while H₂ molecules showed highly disordered arrangement. This simulation method is promising for finding Earth materials suitable for CO₂ capture and H₂ storage and also expected to contribute to fundamental understanding of fluid-mineral interactions in the geological underground.

• Radiation Oncology Journal
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• v.17 no.1
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• pp.84-88
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• 1999

Purpose :To develop a method for verifying a treatment setup in stereotactic radiotherapy by ma- tching portal images to DRRs. Materials and Methods : Four pairs of orthogonal portal images of one patient immobilized by a thermoplastic mask frame for fractionated stereotactic radiotherapy were compared with DRRs. Portal images are obtained in AP (anteriorfposterior) and lateral directions with a target localizer box containing fiducial markers attached to a stereotactic frame. DRRs superimposed over a planned iso-center and fiducial markers are printed out on transparent films. And then, they were overlaid over onhogonal penal images by matching anatomical structures. From three different kind of objects (isgcenter, fiducial markers, anatomical structure) on DRRs and portal images, the displacement error between anatomical structure and isocenters (overall setup error), the displacement error between anatomical structure and fiducial markers (irnrnobiliBation error), and the displacement error between fiducial markers and isocenters (localization error) were measured. Results : Localization error were 1.5$\pm$0.3 mm (AP), 0.9$\pm$0.3 mm (lateral), and immobilization errors were 1.9$\pm$0.5 mm (AP), 1.9$\pm$0.4 mm (lateral). In addition, overall setup errors were 1.0$\pm$0.9 mm (AP), 1.3$\pm$0.4 mm (lateral). From these orthogonal displacement errors, maximum 3D displacement errors($\sqrt{(\DeltaAP)^{2}+(\DeltaLat)^{2}$)) were found to be 1.7$\pm$0.4 mm for localization, 2.0$\pm$0.6 mm for immobilization, and 2.3$\pm$0.7 mm for overall treatment setup. Conclusion : By comparing orthogonal portal images with DRRs, we find out that it is possible to verify treatment setup directly in stereotactic radiotherapy.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.