• Aerospace Engineering and Technology
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• v.13 no.2
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• pp.80-86
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• 2014

In this study, we the estimated surface normalized difference vegetation index by using the KOrea Multi-Purpose SATellite-3 (KOMPSAT-3) multi-spectral images for comparative analysis. The estimated NDVI from KOMPSAT-3 is used as for comparison with the high resolution GeoEye products. The geometry conditions for atmospheric effects are selected from meta files of KOMPSAT-3 bundle data. The used geometry conditions are consist of solar zenith angle, solar azimuth angle, viewing zenith angle, viewing azimuth angle, and date. And, Atmospheric effects such as attenuation, scattering and absorption were physically simulated from water vapor, ozone and aerosol information. Generally, although ground measurements are important for accurate information, in this study, MODIS atmospheric products are used as atmospheric constituents. The surface reflectance from radiative transfer model is utilized for estimating vegetation index. The present study, to reduce atmospheric and geometry conditions between KOMPSAT-3 and GeoEye having difference observation characteristics, data acquisition time is carefully determined for reliable vegetation spectral characteristics.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.739-747
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• 2018

Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.109-117
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• 2012

Recently, large-scale multi-hazards have been occurred in the various areas of the world. A variety of Earth observation sensors such as satellite EO, aerial and terrestrial LiDAR have been utilized for global natural disaster monitoring. Especially, commercial satellites which observe the Earth regularly and repeatedly, and acquire images with cm-level high spatial resolution enable its applications to extend in the fields of disaster management from advanced disaster monitoring to timely recovery. However, due to existing satellite operation systems with some limitations in almost real-time and wide regional disaster response, close international collaborations between satellite operating organizations like NASA, JAXA, KARI etc. have been required for collecting satellite images in time through a satellite platform with multi-sensors or satellite constellation. For responding domestic natural disaster such as heavy snowfall and extreme rainfall in 2011, this paper proposes a disaster management system for timely decision-making; rapid acquisition of satellite imagery, data processing, GIS analysis, and digital mapping through cooperation with NDMI in Korea and International Charter-Space and Major disasters.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.305-311
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• 2016

If the automated image calibration system is performed in the position of non-experts, an expert will be required in every case inefficiently. But this requires an expert only when absolutely necessary. As well as the rapid system operation and efficient workforce can be managed. Image correction to perform projector inspection and management skills and to filter SW plug-in correction is that special theater system maintenance is not only managed efficiently, but also combined image analysis techniques can improve the technical perfection. This paper is to minimize the economic loss by developing a 10-bit High-depth and high-resolution $360^{\circ}$ projection image analysis technique and is to development of the special theater calibration system to effectively support quality.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.26-37
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• 2012

Multi-dimensional hydrodynamic and water quality models are widely used to simulate the physical and biogeochemical processes in the surface water systems such as reservoirs and estuaries. Most of the models have adopted the Eulerian grid modeling framework, mainly because it can reasonably simulate physical dynamics and chemical species concentrations throughout the entire model domain. Determining the optimum grid cell size is important when using the Eulerian grid-based three-dimensional water quality models because the characteristics of species are assumed uniform in each of the grid cells and chemical species are represented by concentration (mass per volume). The objective of this study was to examine the effect of grid-size of a three dimensional hydrodynamic and water quality model (EFDC) on hydrodynamics and mass transport in the Saemangeum Reservoir. Three grid resolutions, respectively representing coarse (CG), medium (MG), and fine (FG) grid cell sizes, were used for a sensitivity analysis. The simulation results of numerical tracer showed that the grid resolution affects on the flow path, mass transport, and mixing zone of upstream inflow, and results in a bias of temporal and spatial distribution of the tracer. With the CG, in particular, the model overestimates diffusion in the mixing zone, and fails to identify the gradient of concentrations between the inflow and the ambient water.

• Proceedings of the KSRS Conference
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• v.1
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.33-40
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• 2005

The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.871-888
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• 2012

This paper aims at presenting the state of the art, the recent progress, and the perspective for the future, in the modelling of two-phase flow in the horizontal legs of a PWR. All phenomena relevant for safety analysis are listed first. The selection of the modelling approach for system codes is then discussed, including the number of fluids or fields, the space and time resolution, and the use of flow regime maps. The classical two-fluid six-equation one-pressure model as it is implemented in the CATHARE code is then presented and its properties are described. It is shown that the axial effects of gravity forces may be correctly taken into account even in the case of change of the cross section area or of the pipe orientation. It is also shown that it can predict both fluvial and torrential flow with a possible hydraulic jump. Since phase stratification plays a dominant role, the Kelvin-Helmholtz instability and the stability of bubbly flow regime are discussed. A transition criterion based on a stability analysis of shallow water waves may be used to predict the Kelvin-Helmholtz instability. Recent experimental data obtained in the METERO test facility are analysed to model the transition from a bubbly to stratified flow regime. Finally, perspectives for further improvement of the modelling are drawn including dynamic modelling of turbulence and interfacial area and multi-field models.

• Horticultural Science & Technology
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• v.35 no.2
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• pp.149-164
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• 2017

Next generation sequencing (NGS) technologies have led to the rapid accumulation of genome sequences through whole-genome sequencing and re-sequencing of crop species. Genomic resources provide the opportunity for a new revolution in plant breeding by facilitating the dissection of complex traits. Among vegetable crops, reference genomes have been sequenced and assembled for several species in the Solanaceae and Cucurbitaceae families, including tomato, pepper, cucumber, watermelon, and melon. These reference genomes have been leveraged for re-sequencing of diverse germplasm collections to explore genome-wide sequence variations, especially single nucleotide polymorphisms (SNPs). The use of genome-wide SNPs and high-throughput genotyping methods has led to the development of new strategies for dissecting complex quantitative traits, such as genome-wide association study (GWAS). In addition, the use of multi-parent populations, including nested association mapping (NAM) and multiparent advanced generation intercross (MAGIC) populations, has helped increase the accuracy of quantitative trait loci (QTL) detection. Consequently, a number of QTL have been discovered for agronomically important traits, such as disease resistance and fruit traits, with high mapping resolution. The molecular markers for these QTL represent a useful resource for enhancing selection efficiency via marker-assisted selection (MAS) in vegetable breeding programs. In this review, we discuss current genomic resources and marker-trait association analysis to facilitate genome-assisted breeding in vegetable species in the Solanaceae and Cucurbitaceae families.