• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.111-115
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• 2009

In Korea, the probabilistically developed seismic hazard maps are used with deterministically derived seismic site coefficients in developing the design response spectrum of a specific site. Even though the seismic hazard maps and seismic site coefficients are incompatible, the current design code ignores such incompatibility. If the seismic hazard map and seismic coefficients are both developed in identical probabilistic framework, such problems can be solved. Unfortunately, the available method cannot be use to derive "true" probabilistic site coefficients. This study uses the ground motion time histories, which were developed as the result of a new probabilistic seismic hazard analysis in the companion paper, as input motions in performing one-dimensional equivalent linear site response analyses, from which the uniform hazard response spectra are generated. Another important characteristic of the hazard response spectra are that the uncertainties and randomness of the ground properties are accounted for. The uniform hazard spectra are then used to derive probabilistic site coefficients. Comparison of probabilistic and deterministically site coefficients demonstrate that there is a distinct discrepancy between two coefficients.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.148-148
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• 2018

Land subsidence caused by excessive groundwater pumping is a serious hydro-geological hazard. The spatial variability in land use, unbalanced groundwater extraction and aquifer characteristics are the key factors which make the problem more difficult to monitor using conventional methods. This study uses the European Space Agency (ESA) Sentinel satellites to investigate and monitor land subsidence varying with different land covers and groundwater use in the arid Quetta valley, Pakistan. The Persistent Scattering Differential Interferometry of Synthetic Aperture Radar (PS-DInSAR) method was used to develop 28 subsidence interferograms of the study area for the period between 16 Oct 2014 and 06 Oct 2016 using ESA's Sentinel-1 SAR data. The uncertainty of DInSAR result is first minimized by removing the dynamic effect caused by atmospheric factors and then filtered using the radar Amplitude Dispersion Index (ADI) to select only the stable pixels. Finally the subsidence maps were generated by spatially interpolating the land subsidence at the stable pixels, the comparison of DInSAR subsidence with GPS readings showed an R 2 of 0.94 and mean absolute error of $5.7{\pm}4.1mm$. The subsidence maps were also analysed for the effect of aquifer type and 4 land covers which were derived from Sentienl-2 multispectral images. The analysis show that during the two year period, the study area experienced highly non-linear land subsidence ranging from 10 to 280 mm. The subsidence at different land covers was significantly different from each other except between the urban and barren land. The barren land and seasonally cultivated area show minor to moderate subsidence while the orchard and urban area with high groundwater extraction rate showed excessive amount of land subsidence. Moreover, the land subsidence and groundwater drawdown was found to be linearly proportional to each other.

• Investigative Magnetic Resonance Imaging
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• v.23 no.1
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• pp.38-45
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• 2019

Purpose: To demonstrate the high-resolution numerical simulation of the respiration-induced dynamic $B_0$ shift in the head using generalized susceptibility voxel convolution (gSVC). Materials and Methods: Previous dynamic $B_0$ simulation research has been limited to low-resolution numerical models due to the large computational demands of conventional Fourier-based $B_0$ calculation methods. Here, we show that a recently-proposed gSVC method can simulate dynamic $B_0$ maps from a realistic breathing human body model with high spatiotemporal resolution in a time-efficient manner. For a human body model, we used the Extended Cardiac And Torso (XCAT) phantom originally developed for computed tomography. The spatial resolution (voxel size) was kept isotropic and varied from 1 to 10 mm. We calculated $B_0$ maps in the brain of the model at 10 equally spaced points in a respiration cycle and analyzed the spatial gradients of each of them. The results were compared with experimental measurements in the literature. Results: The simulation predicted a maximum temporal variation of the $B_0$ shift in the brain of about 7 Hz at 7T. The magnitudes of the respiration-induced $B_0$ gradient in the x (right/left), y (anterior/posterior), and z (head/feet) directions determined by volumetric linear fitting, were < 0.01 Hz/cm, 0.18 Hz/cm, and 0.26 Hz/cm, respectively. These compared favorably with previous reports. We found that simulation voxel sizes greater than 5 mm can produce unreliable results. Conclusion: We have presented an efficient simulation framework for respiration-induced $B_0$ variation in the head. The method can be used to predict $B_0$ shifts with high spatiotemporal resolution under different breathing conditions and aid in the design of dynamic $B_0$ compensation strategies.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.71-77
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• 2016

Solar energy is a viable source to replace fossil fuels. However, challenges associated with site selection for solar panel installation inhibit the uptake of solar energy systems. Expressway slopes offer a potentially attractive alternative for solar panel installation for the following reasons: expressway slopes are vacant public sites, they are abundant (about 4,193km in South Korea), and they are linear in nature. Traditoinally when selecting sites for solar systems conventional surveying methods are employed. Unfortunately, these methods can be dangerous, time consuming, and labor intensive. To overcome these limitations of conventional site selection methodologies, we propose an automated approach using numerical maps. First, contour and expressway polylines are extracted separately from numeric maps. The extracted contour lines are then converted into a digital terrain model; this is used to calculate aspect and slope information. Next, the extracted expressway lines are projected onto a binary image and refined to recover the disconnections, and then applied to create a buffer zone to narrow the search space. Finally, all data sets are overlaid to identify candidate sites for solar panel systems and are visually verified through comparisons with aerial photos.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2271-2283
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• 2015

In this paper, we propose a method which infers an accurate disparity map for radiometrically varying stereo images. For this end, firstly, we transform the input color images to the log-chromaticity color space from which a linear relationship can be established during constructing a joint pdf between input stereo images. Based on this linear property, we present a new stereo matching cost by combining weighted mutual information and the SIFT (Scale Invariant Feature Transform) descriptor with segment-based plane-fitting constraints to robustly find correspondences for stereo image pairs which undergo radiometric variations. Experimental results show that our method outperforms previous methods and produces accurate disparity maps even for stereo images with severe radiometric differences.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2378-2387
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• 1998

This thesis proposes a design algorithm of an efficient space response compactor for Built-In Self-Testing of VLSI circuits. The proposed design algorithm of space compactors can be applied independently from the structure of Circuit Cnder Test. There are high hardware overhead cost in conventional space response compactors and the fault coverage is reduced by aliasing which maps faulty circuit's response to fault-free one. However, the proposed method designs space response compactors with reduced hardware overheads and does not reduce the fault coverage comparing to conventional method. Also, the proposed method can be extended to general N -input logic gate and design the most efficient space response L'Ompactors according to the characteristies of output sequence from CUT. The prolxlsed design algorithm is implemented by C language on a SUN SPARC Workstation, and some experiment results of the simulation applied to ISCAS'85 benchmark circuits with pseudo random patterns generated bv LFSR( Linear Feedback Shift Register) show the efficiency and validity of the proposed design algorithm.

• Textile Coloration and Finishing
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• v.21 no.5
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• pp.41-50
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• 2009

Design of experiments (DoE) concept was successfully applied to determine the optimum processing conditions that yield maximum % exhaustion for berberine interaction with synthetic tanning agent pretreated polyamide substrates. The potential of synthetic tanning agent to provide anionic sites on the polyamide for berberine interaction which is cationic in nature was tested to increase the % exhaustion of berberine in this article. Experiments were designed according to Central Composite Rotatable Design (CCRD). The three factors for synthetic tanning agent pretreatment and two factors for berberine interaction each at five different levels, including central and axial points were considered. Experiments were conducted in a laboratory scale infra-red treatment instrument according to CCRD. For each response, second order polynomial models were developed using multiple linear regression analysis incorporating linear, interactions and squared effects of all variables and then optimized. The significance of the mathematical model developed was ascertained using Excel regression (solver) analysis module. Analysis of variance (ANOVA) was performed to check the adequacy and accuracy of the fitted models. The response surfaces and contour maps showing the interaction of process variables were constructed. Applying Monte Carlo simulation, response surface and contour plots, optimum operating conditions were found and at this optimum point, % exhaustion of 81% and 74% respectively for synthetic tanning agent pretreatment and berberine interaction were observed and subsequently the results were experimentally investigated.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.625-638
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• 2018

In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.246-254
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• 2020

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.