• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.1
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• pp.11-26
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• 1984

The aim of this study is to establish a synthetical optimal method that simultaneously analyze and design reinforced concrete flat slab-column structures involving multi-constraints and multi-design variables. The variables adopted in this mathematical models consist of design variables including sectional sizes and steel areas of frames, and analysis variable of the ratio of bending moment redistribution. The cost function is taken as the objective function in the formulation of optimal problems. A number of constraint equations, involving the ultimate limit state and the serviceability limit state, is derived in accordance with BSI CP110 requirements on the basis of limit state design theory. Both objective function and constraint equations derived from design variables and an analysis variable generally become high degree nonlinear problems. Using SLP as an analytical method of nonlinear optimal problems, an optimal algorithm is developed so as to analyze and design the structures considered in this study. The developed algorithm is directly applied to a few reinforced concrete flat slab-column structures to assure the validity of it and the possibility of optimization From the research it is found that the algorithm developed in this study is applicable to the optimization of reinforced concrete flat slab column structures and it converges to a optimal solution with 4 to 6 iterations regardless of initial variables. The result shows that an economical design can be possible when compared with conventional designs. It is also found that considering the ratio of bending moment redistribution as a variable is reasonable. It has a great effect on the composition of optimal sections and the economy of structures.

• Progress in Medical Physics
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• v.16 no.1
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• pp.24-31
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• 2005

The stereotactic radiosurgery (SRS) describes a method of delivering a high dose of radiation to a small tar-get volume in the brain, generally in a single fraction, while the dose delivered to the surrounding normal tissue should be minimized. To perform automatic plan of the SRS, a new method of multi-isocenter/shot linear accelerator (linac) and gamma knife (GK) radiosurgery treatment plan was developed, based on a physical lattice structure in target. The optimal radiosurgical plan had been constructed by many beam parameters in a linear accelerator or gamma knife-based radiation therapy. In this work, an isocenter/shot was modeled as a sphere, which is equal to the circular collimator/helmet hole size because the dimension of the 50% isodose level in the dose profile is similar to its size. In a computer-aided system, it accomplished first an automatic arrangement of multi-isocenter/shot considering two parameters such as positions and collimator/helmet sizes for each isocenter/shot. Simultaneously, an irregularly shaped target was approximated by cubic structures through computation of voxel units. The treatment planning method by the technique was evaluated as a dose distribution by dose volume histograms, dose conformity, and dose homogeneity to targets. For irregularly shaped targets, the new method performed optimal multi-isocenter packing, and it only took a few seconds in a computer-aided system. The targets were included in a more than 50% isodose curve. The dose conformity was ordinarily acceptable levels and the dose homogeneity was always less than 2.0, satisfying for various targets referred to Radiation Therapy Oncology Group (RTOG) SRS criteria. In conclusion, this approach by physical lattice structure could be a useful radiosurgical plan without restrictions in the various tumor shapes and the different modality techniques such as linac and GK for SRS.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Economic and Environmental Geology
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• v.48 no.5
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• pp.371-377
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• 2015

Isotopic compositions of precipitation have been used to understand moisture transport in the atmosphere and interactions between precipitation and groundwater. Isotopic compositions of speleothems and ice cores, so called, ''paleoarchives'', can be utilized to interpret climate of the past and global circulation models (GCMs). The GCMs are able to explain the paleoarchives, can be validated by the precipitation isotopes. The developments of stable isotope analyzers make high-resolution isotopic studies feasible. Therefore, a high-resolution study of precipitation isotopes is needed. For this study, precipitation samples were collected for every 5 to 15 minutes, depending on precipitation rates, using an auto-sampler for precipitation isotopes near coastal area. The isotopic compositions of precipitation range from -5.7‰ (-40.1‰) to -10.8‰ (-74.3‰) for oxygen (hydrogen). The slope of ${\delta}^{18}O-{\delta}D$ diagram for the whole period is 6.8, but that of each storm is 5.1, 4.2, 7.9 and 7.7, respectively. It indicates that evaporation occurred during the first two storms, while the latter two storm did not experience any evaporation. The isotopic fractionations of precipitation has significant implications for the water cycle and high-resolution data of precipitation isotopes will be needed for the future studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.139-151
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• 2017

The habitat preference of roe deers(Capreolus pygargus) in Jeju island, South Korea was analyzed by using their occurrence probability in MaxEnt model in this study. Totally 490 surveying data were gathered and 15 environmental variables were chosen for the model in which 6 variables out of 15 ones were filtered and finally removed because of there being higher correlation(over 0.7 in correlation coefficient). According to the modeling, roe deers were known to prefer the area ranging from 200 to 700 meter and over 1,500 meter in sea level, where there were not many dominant tree and/or dominant vegetation with low density so that understory vegetation can grow well with plentiful sunlight and can be used as a food of herbivore like roe deers. Otherwise, the region ranging from 700 to 1,500 meter was mostly covered with high density vegetation which cut off sunlight trying to penetrate through the dominant vegetation. It can cause a lower density of vegetation on surface, which can not attract to roe deers.

• 전자공학회논문지 IE
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• v.44 no.1
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• pp.27-32
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• 2007

This study was to suggest the predictive model of propagation, considering the effect by the multipath waves produced by the sending and receiving vehicles' left/right reflectors and the adjacent vehicles when the communication between the vehicles on the one-way two-lanes road in the urban city with a lot of traffic jams. Then, the radius of curved road was 600[m], the length of curved roads $52.4\sim471.2[m]$, and the bridge's pier of road was $5o\sim45o$. Also, it was simulated by changing the receiving vehicle located on the curved road's gap from minimum 3.3[m] to maximum 29.5[m], corresponding to the change of distance of the bridge's pier of road and curved road. As a result of this research above, in case of $5o\sim15o$ bridge's pier of road, it was within l[dB] regardless of the receiving vehicle's position on the curved road in case of propagation path loss. In case of $15o\sim45o$, it was approximately $1\sim8[dB]$ as the bridge's pier of road is changed. And, in case of propagation path, it found out that it was changed to $0.4\sim120[m]$ according to the change of bridge's pier of road. Then, the delay time of propagation was 400[nsec] as it produced 120[m] in the difference of propagation path.

• Journal of the Korean Chemical Society
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• v.54 no.5
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• pp.573-578
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• 2010

In this study, iron(III)-2,4-dihydroxysalophen chloride (Fe(2,4-DHSalophen)Cl), has been synthesized by combination of 2,4-dihydroxysalophen (2,4-DHSalophen) with $FeCl_2$ in a solvent system. This complex combination was characterized using UV-vis and IR spectroscopies. Subsequently, the interaction between native calf thymus deoxyribonucleic acid (ct-DNA) and Fe(2,4-DHSalophen)Cl, was investigated in 10 mM Tris/HCl buffer solution, pH 7.2, using UV-visible absorption and fluorescence spectroscopies, thermal denaturation technique and viscosity measurements. From spectrophotometric titration experiments, the binding constant of Fe(2,4-DHSalophen)Cl with ct-DNA was found to be $(1.6{\pm}0.2){\times}10^3\;M^{-1}$. The fluorescence study represents the quenching effect of Fe(2,4-DHSalophen)Cl on bound ethidium bromide to DNA. The quenching process obeys linear Stern-Volmer equation in extended range of Fe(2,4-DHSalophen)Cl concentration. Thermal denaturation experiments represent the increasing melting temperature of DNA (about $4.3^{\circ}C$) due to binding of Fe(2,4-DHSalophen)Cl. These results are consistent with a binding mode dominated by interactions with the groove of ct-DNA.

• Current Research on Agriculture and Life Sciences
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• v.33 no.2
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• pp.41-47
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• 2015

This study calculated the overall heat transfer coefficient (U-value) of greenhouse covering materials with thermal screens using a simulation model and then estimated the validity of the calculated results by comparison with measured values. The U-value decreased gradually as the thickness of the air space between the double glazing increased, and then remained essentially constant at thicknesses exceeding 25 mm. The U-value also increased with the difference in temperature between the inside and outside of the hot box. The vigorous convective heat transfer between two plastic films caused unsteady heat flow and then created a nonlinear temperature distribution in the air space. The distance did not affect the U-value at distances of 50~200 mm between the plastic covering and thermal curtain. The numerical calculation results, with and without sky radiation, were in accord with the experimental results for a $30^{\circ}C$ temperature difference between the inside and outside of the hot box. In conclusion, a reliable U-value can be calculated for a temperature difference of $30^{\circ}C$ or more between the inside and outside of the hot box.