• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1197-1199
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• 2003

ROCSAT-2 mission is to daily image over Taiwan and the surrounding area for disaster monitoring, land use, and ocean surveillance during the 5-year mission lifetime. The satellite will be launched in December 2003 into its mission orbit, which is selected as a 14 rev/day repetitive Sun-synchronous orbit descending over (120 deg E, 24 deg N) and 9:45 a.m. over the equator with the minimum eccentricity. National Space Program Office (NSPO) is developing a ROCSAT-2 Image Processing System (IPS), which aims to provide real-time high quality image data for ROCSAT-2 mission. A simulated ROCSAT-2 image, based on Level 1B QuickBird Data, is generated for IPS verification. The test image is comprised of one panchromatic data and four multispectral data. The qualification process consists of four procedures: (a) QuickBird image processing, (b) generation of simulated ROCSAT-2 image in Generic Raw Level Data (GERALD) format, (c) ROCSAT-2 image processing, and (d) geometric error analysis. QuickBird standard photogrammetric parameters of a camera that models the imaging and optical system is used to calculate the latitude and longitude of each line and sample. The backward (inverse model) approach is applied to find the relationship between geodetic coordinate system (latitude, longitude) and image coordinate system (line, sample). The bilinear resampling method is used to generate the test image. Ground control points are used to evaluate the error for data processing. The data processing contains various coordinate system transformations using attitude quaternion and orbit elements. Through the qualification test process, it is verified that the IPS is capable of handling high-resolution image data with the accuracy of Level 2 processing within 500 m.

• Journal of Korea Soil Environment Society
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• v.3 no.1
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• pp.21-30
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• 1998

The purpose of this study is to assess contaminant transfer and environmental impacts to the surroundings by inadequate control of USTs. Several methodologies can be used to approach for the site assessment depending on the appearance of contaminants on the site and their types. In this case study, randomized and/or triangle matrix techniques were used. As a result, the composition of materials in tank station were appeared in several state. From 1 to 1.5m depth, the soil was composed of reclaimed soils. And 1.8-3.5m depth, silty sand was appeared and about 4m, weathered soil was appeared. Based on the preliminary and actual site investigation by DPT methodologies on the width and depth of the site with analysis of BTEX and TPH, the contamination was found in this tank station and already distributed near areas. Finally, it was found that the hydroflow differences during the season affects the area and depth of contamination.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.80-80
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• 2010

Graphene has attracted tremendous attention for the last a few years due to it fascinating electrical, mechanical, and chemical properties. Up to now, several methods have been developed exclusively to prepare graphene, which include micromechanical cleavage, polycrystalline Ni employing chemical vapor deposition technique, solvent thermal reaction, thermal desorption of Si from SiC substrates, chemical routes via graphite intercalation compounds or graphite oxide. In particular, polycrystalline Ni foil and conventional chemical vapor deposition system have been widely used for synthesis of large-area graphene. [1-3] In this study, synthesis of mono-layer graphene on a Ni foil, the mixing ratio of hydrocarbon ($CH_4$) gas to hydrogen gas, microwave power, and growth time were systemically optimized. It is possible to synthesize a graphene at relatively lower temperature ($500^{\circ}C$) than those (${\sim}1000^{\circ}C$) of previous results. Also, we could control the number of graphene according to the growth conditions. The structural features such as surface morphology, crystallinity and number of layer were investigated by scanning electron microscopy (SEM) and atomic force microscopy (AFM), transmission electron microscopy (TEM) and resonant Raman spectroscopy with 514 nm excitation wavelength. We believe that our approach for the synthesis of mono-layer graphene may be potentially useful for the development of many electronic devices.

• Journal of Fashion Business
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• v.17 no.1
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• pp.81-97
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• 2013

The purpose of this study is to summarize systematically and understand the characteristics of the production process of whole garments in order to develop knitwear using a real whole garment machine and propose this as a development case for high value added knitwear design. Concerning research methods, the study looked at existing research into whole garment knitwear and relevant data, data on websites, and the whole garment knitting machine made by Shima Seiki, a Japanese company, which has been the most commonly used machine in Korea. Also the study collected program data concerning a knitting machine and knitting by participating in the production process of whole garment knitwear, and the production line was filmed directly. In addition, the study conducted research into the development of knitwear design using the SWG-X 12 gauge. The conclusions obtained from the production process of whole garments and product development include the following. First, whole garment knitwear is appropriate for expressing a sophisticated look that makes the body appear to be in one form through natural connection without any seam allowance. Second, it is very suitable for response production since it does not go through the pattern, cutting, and processing stages. Furthermore, because of the consistent management of the entire process by computer control, it may be the highest cutting-edge fashion area in which planning and proposal style industry may be realizable. Third, it is easy to approach design through a programming process, and it is possible to develop diverse patterns; thereby, it is appropriate for producing high value added knitwear products.

• Journal of Soil and Groundwater Environment
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• v.19 no.6
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• pp.6-12
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• 2014

We reviewed literature focusing on the amounts of domestic production, distribution, and consumption of strong acids and their spill cases. In particular, we investigated the chemistry and toxicity of four strong acids classified as "accident preparedness substances," including hydrochloric, nitric, sulfuric, and hydrofluoric acid. We recommend sulfuric and hydrofluoric acid as the chemicals of priority control based on the amounts used and toxicity. An advanced prevention/response system needs to be established along with an improved human and social infrastructure to prevent and efficiently respond to chemical accidents. Understanding the behavior and transport of spilled strong acids in the soil and groundwater environments requires a multi-disciplinary approach since they go through a variety of chemical and biogeochemical reactions with complex geomedia. However, no such research has been done in this area in Korea to the best of our knowledge. We expect the results of this study to contribute as basic data to future research.

• Biomedical Science Letters
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• v.25 no.1
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.289-295
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• 2019

Maritime back-up navigation system in port approach requires a horizontal accuracy of 10 meters in IALA (International Association of Lighthouse Authorities) recommendations. eLoran which is a best back-up navigation system that satisfies accuracy requirement has poor navigation performance depending signal environments. Especially, noise caused by multipath and electronic devices around eLoran antenna affects navigation performance. In this paper, Ship based Navigation Back-up system using UAV on Interference is designed to satisfy horizontal accuracy requirement. To improve the eLoran signal environment, UAVs are equipped with camera, IMU sensor and eLoran antenna and receivers. This proposed system is designed to receive eLoran signal through UAV-based receiver and control UAV's position and attitude within Landmark around area. The ship-based positioning using eLoran signal, vision and attitude information received from UAV satisfy resilient and robust navigation requirements.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Korean Journal of Materials Research
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• v.32 no.12
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• pp.522-527
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• 2022

Among efforts to improve techniques for the chemical vapor deposition of large-area and high-quality graphene films on transition metal substrates, being able to reliably transfer these atomistic membranes onto the desired substrate is a critical step for various practical uses, such as graphene-based electronic and photonic devices. However, the most used approach, the wet etching transfer process based on the complete etching of metal substrates, remains a great challenge. This is mainly due to the inevitable damage to the graphene, unintentional contamination of the graphene layer, and increased production cost and time. Here, we report the systematic study of an H₂ bubbling-assisted transfer technique for graphene films grown on Cu foils, which is nondestructive not only to the graphene film but also to the Cu substrate. Also, we demonstrate the origin of the graphene film tearing phenomenon induced by this H₂ bubbling-assisted transfer process. This study reveals that inherent features are produced by rolling Cu foil, which cause a saw-like corrugation in the poly(methyl methacrylate) (PMMA)/graphene stack when it is transferred onto the target substrate after the Cu foil is dissolved. During the PMMA removal stage, the graphene tearing mainly appears at the apexes of the corrugated PMMA/graphene stack, due to weak adhesion to the target substrate. To address this, we have developed a modified heat-press-assisted transfer technique that has much better control of both tearing and the formation of residues in the transferred graphene films.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.219-230
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• 2023

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures; thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising technology since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu, using Mininet, an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks using an OpenFlow switch with sixteen nodes and a controller for each topology. The study finds that the choice of network controller can significantly impact network performance and suggests that monitoring network performance indicators is crucial for optimizing network performance. The project provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture.