• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Geomechanics and Engineering
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• 제13권1호
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• pp.1-23
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• 2017

A coupled liquid-gas-solid three-phase model, linking two numerical codes (TOUGH2/EOS3 and $FLAC^{3D}$), was firstly established and validated by simulating an in-situ air flow test in Essen. Then the coupled model was employed to investigate responses of multiphase flow and soil skeleton deformation to compressed air or freshwater injection using the same simulation conditions in an aquifer of Tianjin, China. The simulation results show that with injecting pressurized fluids, the vertical effective stress in some area decreases owing to the pore pressure increasing, an expansion of soil skeleton appears, and land uplift occurs due to support actions from lower deformed soils. After fluids injection stops, soil deformation decreases overall due to injecting fluids dissipating. With the same applied pressure, changes in multiphase flow and geo-mechanical deformation caused by compressed air injection are relatively greater than those by freshwater injection. Furthermore, the expansion of soil skeleton induced by compressed air injection transfers upward and laterally continuously with time, while during and after freshwater injection, this expansion reaches rapidly a quasi-steady state. These differences induced by two fluids injection are mainly because air could spread upward and laterally easily for its lower density and phase state transition appears for compressed air injection.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.182-183
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• 2004

Atmospheric carbon monoxide (CO) and ozone $(O_3)$ play the important trace gases in tropospheric chemistry, through its concentration in the troposphere directly influences the concentrations of tropospheric hydroxyl (OH). Understanding the impact of CO and $O_3$ on the global tropospheric chemistry requires measurements of the global atmospheric CO and $O_3$ distributions. This study focuses on the identification of CO and O3 released in the East Asia between March 2000 and February 2004. During the period, the MOPITT instrument onboard the Earth Observing System (EOS)-Terra platform collected extensive measurement of CO. So we have used MOPITT data at 700hPa to analyze seasonal distribution of CO concentration. And the O3 measurements for this study were Total Ozone Mapping Spectrometer (TOMS) and Dobson spectrometer provided NASA/GSFC and Yonsei University, Korea. During springtime, the CO and O3 concentrations were increased over East Asia for April, May, and June. CO and O3 transport and chemistry in the springtime in East Asia are studied by use of the HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model.

• 융합신호처리학회논문지
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• 제21권4호
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• pp.183-194
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• 2020

최근 영유아 성장기에 발생하는 고관절 이형성증(Developmental Dysplasia of Hip, DDH)의 숫자가 늘어나고 있다. DDH는 영유아 성장을 방해하고 다른 부작용도 많이 발생시키기 때문에 최대한 조기에 발견하여 치료해야 한다. 최근 들어 Convolutional Neural Networks (CNN) 및 개선된 Resnet50을 활용한 머신러닝 기법이 초음파 영상 분석에 많이 활용되고 있다. 연구 결과를 보면 컴퓨터 보조 이미지 분석이 의료현장에서 객관성과 생산성을 크게 향상시키고 있다. 본 연구의 결과는 정형외과에서의 난제인 초음파 영상을 통한 DDH 컴퓨터 보조 진단 알고리즘에도 충분히 활용될 수 있다는 것을 보여주고 있다. 본 논문에서는 CNN을 활용하여 DDH를 자동으로 측정하고 진단할 수 있는 컴퓨터 보조 진단 알고리즘을 제안하였다. DDH 측정을 위해 유아 고관절의 정상/비정상 판독을 위해 Acetabulum-Femoral head의 angle을 자동으로 계산하였으며 기존 영상을 딥 러닝하여 진단을 자동으로 하는 알고리즘을 설계하였다. 실험 결과 의사와 비교하여 진단의 속도와 정확도가 향상된다는 것을 확인하였다.

• 한국군사과학기술학회지
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• 제24권2호
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• pp.211-218
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• 2021

This paper deals with the computational work to characterize the formation and pinching of a plasma in an X-pinch configuration. A resistive magnetohydrodynamic model of a single fluid and two temperature is adopted assuming a hollow conical structure in the (r,z) domain. The model includes the thermodynamic parameter of tungsten from the corrected Thomas-Fermi EOS(equation of state), determining the average ionization charge, pressure, and internal energy. The transport coefficients, resistivity and thermal conductivity, are obtained by the corrected Lee & More model and a simple radiation loss rate by recombination process is considered in the simulation. The simulation demonstrated the formation of a core-corona plasma and intense compression process near the central region which agrees with the experimental observation in the X-pinch device at Seoul National University. In addition, it confirmed the increase in radiation loss rate with the density and temperature of the core plasma.