• Journal of Environmental Science International
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• v.6 no.5
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• pp.541-550
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• 1997

A least-square regression analysis is applied for the estimation of velocity streamfunction field based on discretely sampled current meter data. The coefficients of a streamfuunction that is expanded in terms of trigonometric basis function are obtained by enforcing the horizontal non-divergence of two-dimensional flow field. This method avoids Interpolation and gives a root-mean-square (rms) residual of fit which Includes the divergent part and noisiness of oceanic data. The Implementation of the method Is done by employing a boundary-fitted, curvilinear orthogonal coordinate which facilitates the specification of boundary conditions. An application is successfully made to the Texas-Louisiana shelf using the 32 months current meter data (31 moorings) observed as a part of the Texas-Louisiana Shelf and Transport Processes Study (LATEX). The rms residual of the fitting is relatively small for the shelf, which indicates the field Is Ivell represented by the streamnfunction.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.10 no.5
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• pp.345-349
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• 2000

$Al_2O_3/ZrO_2$eutectic fibers were grown by micro-pulling down technique and investigated their microstructure as a function of solidification rate. $Al_2O_3/ZrO_2$eutectic fibers 0.2~2 mm in diameter and 500 mm in length have been grown with a pulling rate of 0.1~15 mm/min. The eutectic microstructures changed as a function of fulling rate from rod-shaped to cellular shape containing some thin lamellar pattern via uniform lamellar structure. Typical lamellar thickness decreased from about 380 nm to 110 nm as the pulling rate increased from 1 mm/min to 15 mm/min. The interlamellar spacing fitted with the inverse-square-root dependence on pulling rate according to $\lambda$= $1{\times}v^{-1/2}$, where $\lambda$ has the dimension in $\mu\textrm{m}$ and v is $\mu\textrm{m}$/s. Hardness value reached 13.1 GPa at 15 mm/min of pulling rate and tensile strength 900 MPa at 10 mm/min were also increased as the interlamellar spacing decreased.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• The Journal of Korean Physical Therapy
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• v.20 no.1
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• pp.1-9
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• 2008

Purpose: This study examined the effects of pre-eccentric exercise to the quadriceps for the prevention of delayed onset muscle soreness (DOMS) and recovery of muscular function, depending on the training intensities. Methods: Subjects were divided into one of three groups that control group, a low intensity eccentric exercise group (LIEE group) and a high intensity eccentric exercise group (HIEE group). Subjects who underwent pre-eccentric exercise undertook exercise at an intensity of 25% and 75% of maximal voluntary contractions, respectively. After undertaking pre-eccentric exercise for eight weeks, eccentric exercise was applied again to induce DOMS. Measurements were conducted to examine pain and muscular function changes before, immediately after, after the induction of DOMS, and at the first, third, fifth and seventh days after the induction of DOMS. Results: Subjects who underwent pre-eccentric exercise showed a significant difference from the control group for the changes in the visual analogue scale (VAS) pain threshold, pressure pain threshold and muscle thickness by isometric contraction from measuring DOMS, and in particular, pain threshold by isometric contraction had remarkable effect in the LIEE group of subjects. For the change of the root mean square values using mechanomyography (MMG) as the measurement of muscular function recovery, subjects who had undertaken pre-eccentric exercise group showed a significant difference as compared to the control group. Conclusion: Pre-eccentric exercise was very effective in preventing and recovering delayed onset muscle soreness and was helpful to prevent and recover from decreased muscular function. The difference based on the intensity of exercise was not great, but it was more effective in the low intensity eccentric exercise group of subjects. Therefore, it can be considered that pre-eccentric exercise has a high application value as a physical therapy intervention for prevention and rehabilitation of sports injuries.

• Earthquakes and Structures
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• v.8 no.6
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• pp.1463-1480
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• 2015

This research aims to develop an empirical model for simulation of time-varying frequency in earthquake ground motion so as to be used easily in engineering applications. Briefly, 10545 recordings of the Next Generation Attenuation (NGA) global database of accelerograms from shallow crustal earthquakes are selected and binned by magnitude, distance and site condition. Then the wavelet spectrum of each acceleration record is calculated by using one-dimensional continuous wavelet transform, and the frequencies corresponding to the maximum values of the wavelet spectrum at a series of sampling time, named predominant frequencies, are extracted to analyze the variation of frequency content of seismic ground motions in time. And the time-variation of the predominant frequencies of 178 magnitude-distance-site bins for different directions are obtained by calculating the mean square root of predominant frequencies within a bin. The exponential trigonometric function is then use to fit the data, which describes the predominant frequency of ground-motion as a function of time with model parameters given in tables for different magnitude, distance, site conditions and direction. Finally, a practical frequency-dependent amplitude envelope function is developed based on the time-varying frequency derived in this paper, which has clear statistical parameters and can emphasize the effect of low-frequency components on later seismic action. The results illustrate that the time-varying predominant frequency can preferably reflect the non-stationarity of the frequency content in earthquake ground motions and that empirical models given in this paper facilitates the simulation of ground motions.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1121-1137
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• 2015

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.95-110
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• 2017

The network RTK surveying has been widely used in the field of cadastral surveys in recent years, and its use is gradually expanding. As a result of the implementation of GPS static surveys by civilian companies in accordance with the progress of the cadastral surveys and gradual civilian transfer plans and cadastral surveys, there has been an increase in the number of civilian companies performing surveys. In this paper, we describe the process of applying the results of analysis of conformity using the network RTK site calibration function on the local coordinate system to the GPS static surveying of the cadastral reference points in Anyang city. In addition, the measurement results of the network RTK site calibration function and the results of the GPS static surveying network reconciliation in the local coordinate system were compared, and the performance was determined within 0.04m maximum of RMSE(Root Mean Square Error), and further study on the application method is needed.