• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.735-738
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• 2005

The phase error in the digital transmission system are generated by random phase noise and tracking phase error due to doppler phenomenon. In the mobile satellite communication system that generates the doppler frequency, which is a system with a movement, the proper system phase noise spectrum should be designed based on analyses for phase noise and static phase error effects. Based on the analyses of the doppler frequency and the phase error for bilateral satellite communication system providing an asynchronous service, the phase noise spectrums for the mobile satellite communication are designed in this paper. Also, the available transmission services under the less doppler effect are proposed and the proper signal source units for a required transmission system can be designed under the proposed system phase noise spectrum.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1247-1255
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• 2016

The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3026-3034
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• 2021

Multi-target X-ray tube is a new type X-ray source, and can be applied in many fields such as sensitive X-ray fluorescence analysis and medical imaging. In this work, we report an electric field modulation multi-target X-ray tube, which contains four targets (Cr, Ni, Au, Mo) coated on a Beryllium (Be) window. A four-valve electric field deflector was developed to deflect the electron beam to bombard the corresponding targets. Particle dynamics analysis software was employed to simulate the particle tracking of electron beam. The results show that the 30 keV electron beam could get a 6.7 mm displacement on the target plane by 10⁵ V/m electric field. The focus areas are about 2 mm × 5 mm and 4 mm × 2.5 mm after deflection in two directions. Thermal behavior calculated by ANSYS shows that the designed target assembly could withstand a 10 W continuous power. The optimum target thicknesses and emission spectra were obtained by Geant4 when the thickness of Be window was 300 mm and the electron beam incident angle was 0.141 rad. The results indicate that this multi-target X-ray tube could provide different X-ray sources effectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• Korean Journal of Optics and Photonics
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• v.30 no.1
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• pp.1-7
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• 2019

MANPADS (man-portable air defense system) is a counterweapon system against enemy aircraft, tracking the MWIR (mid-wavelength of infrared) signature of the plume. Under foggy conditions, however, multiple scattering phenomenon caused by the particles affects the MWIR transmittance, and the MANPADS detection performance. Therefore, in this study we analyzed the lock-on range of MANPADS with varying fog conditions and plume characteristics. To analyze the optical extinction properties and transmittance in fog, Mie scattering theory and analytic solution of the radiative-transfer equation are utilized. In addition, we used flare signature as an alternative MWIR light source. We confirmed that the lock-on range could be noticeably reduced under conditions of mist, and proportional to the flare temperature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.577-580
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• 2014

In this paper an auto-switching dual-input energy harvesting circuit is proposed. Since the maximum power points of a thermoelectric generator(TEG) output and a vibration device(PEG) output is 1/2 of their open-circuit voltage, an identical MPPT controller can be used for both energy sources. The proposed circuit monitors the outputs of the TEG and PEG, and chooses the energy source generating a higher output using an auto-switching controller, and then harvests the maximum power from the selected device using a MPPT controller. The harvested energy is boosted through a charge pump and stored in a storage capacitor. The stored energy is provided to a load through a PMU(Power Management Unit). The proposed circuit is designed in a $0.35{\mu}m$ CMOS process and its functionality has been verified through extensive simulations. The designed chip occupies $1.4mm{\times}1.2mm$ including pads.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.185-196
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• 2019

BACKGROUND: This study investigated the effects of rice genetically modified to be resistant against rice blast and rice bacterial blight on the soil microbial community. A comparative analysis of the effects of rice genetically modified rice choline kinase (OsCK1) gene for disease resistance (GM rice) and the Nakdong parental cultivar (non-GM rice) on the soil microbial community at each stage was conducted using rhizosphere soil of the OsCK1 and Nakdong rice. METHODS AND RESULTS: The soil chemistry at each growth stage and the bacterial and fungal population densities were analyzed. Soil DNA was extracted from the samples, and the microbial community structures of the two soils were analyzed by pyrosequencing. No significant differences were observed in the soil chemistry and microbial population density between the two soils. The taxonomic analysis showed that Chloroflexi, Proteobacteria, Firmicutes, Actinobacteria, and Acidobacteria were present in all soils as the major phyla. Although the source tracking analysis per phylogenetic rank revealed that there were differences in the bacteria between the GM and non-GM soil as well as among the cultivation stages, the GM and non-GM soil were grouped according to the growth stages in the UPGMA dendrogram analysis. CONCLUSION: The difference in bacterial distributions between Nakdong and OsCK1 rice soils at each phylogenetic level detected in microbial community analysis by pyrosequencing may be due to the genetic modification done on GM rice or due to heterogeneity of the soil environment. In order to clarify this, it is necessary to analyze changes in root exudates along with the expression of transgene. A more detailed study involving additional multilateral soil analyses is required.

• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• Journal of Soil and Groundwater Environment
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• v.27 no.4
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• pp.37-48
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• 2022

Accurate analysis of petroleum hydrocarbons in soil is an important prerequisite for proper source tracking of contamination. Identification of petroleum compounds is commonly carried out by peak pattern matching in gas chromatography. However, this method has several technical limitations, especially when the soils underwent biological, physical and chemical transformation. For instance, it is very difficult to distinguish jet fuel (JP-8) from kerosene because JP-8 is derivatized from secondary reaction between chemical agents (e.g. anti-oxidants, antifreezer and so on) and kerosene. In this study, an alternative method to separately analyze JP-8 and kerosene in the petroleum hydrocarbon contaminated soil was proposed. Qualitative analyses were performed for representative phenolic antioxidants [2,6-di-tert-butyl phenol (2,6-DTBP), 2,4-di-tert- butylphenol(2,4-DTBP), 2,6-di-tert-butyl-4-methyl phenol (2,6-DTBMP)] using a two dimensional gas chromatograph mass spectrometer (2D GC×GC-TOF-MS). This qualitative analysis of antioxidants in soil would be a useful complementary tool for the peak pattern matching method to identify JP-8 contamination in soil.