• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.267-274
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• 2004

Using geo-statistical method, yield data of different fields were analyzed to examine their field variability according to examining year, analysis method. Semivariogram and Kriged maps of geo-statistical analysis were used to examine their spatial dependence within a filed. The results obtained were as follows. 1) Descriptive statistical results of the yield showed that the yield and the difference of yield ranged from 100 to 946kg/10a and from 272 to 653kg/10a, respectively within a field. The coefficient of variation also ranged from 5.9 to 22.4 %. 2) More than 90% of yield data were placed between 350 to 850kg/10a. e results indicated that the gram mass flow sensor should have the measuring range from 0.34 to 0.82kg/s considering the yields when 4 rows head-feeding combine with 0.8 m/s of working speed was utilized. 3) A high spatial dependence was found within paddy field. The Q values ranged from 0.20 to 0.97, and the range of spatial dependence was from 6.9 to 53.3m. From this result, the rational sampling interval for yield investigation was estimated 6.9m. 4) Yields within a field between observation years showed considerable variability even if the field was evenly cultivated and managed. To apply precision agriculture in a paddy field, the field test should be continued to build a solid data-base including meteorological data, blight damage and insect damage.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.4
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• pp.202-210
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• 2005

Since understanding on spatial variability of a field is essential to pursue precision agricultural technology, a field study for field variability and variable rate fertilization of nitrogen in a direct seeding paddy was attempted. Variable rate application of nitrogen was designed with soil test, and field application was tested in a direct seeding paddy in the Kimje, Jeonbuk, Korea. The grid samples of soil was collected from the field of which unit size was 35 m by 112 m on February before irrigating of the field. Soil organic matter, available phosphate and silicate, and extractable potassium were analyzed. Variable rate fertilizer recommendation maps of nitrogen for high yielding, HY, and low input sustainable agriculture, LISA, were derived based on the soil analysis. Direct seeding of rice was performed for variable rate treatment, VRT, for the experimental plot in 2001 and 2002, and so did for three volunteer farmers' field in 2003. Yield mapping was performed by harvesting. Economic feasibility of direct seeding of rice by variable rate fertilization was evaluated. Though increased yield of variable rate application and benefit of reducing fertilizer use and environmental impact, the cost for soil test exceeded the total reduced fertilizer cost.

• Atmosphere
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• v.20 no.3
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• pp.343-353
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• 2010

A regional wind network with complex surface conditions must be designed with sufficient space and time resolution to resolve the local circulations. In this study, the spatial variations of the wind field observed in the Seoul and Jeju regional networks were evaluated in terms of annual, seasons, and months to assess the spatial homogeneity of wind fields within the regional networks. The coherency of the wind field as a function of separation distance between stations indicated that significant coherency was sometimes not captured by the network, as inferred by low correlations between adjacent stations. A meso-velocity scale was defined in terms of the spatial variability of the wind within the network. This problem is predictably most significant with weak winds, dull prevailing wind, clear skies and significant topography. The relatively small correlations between stations imply that the wind at a given point cannot be estimated by interpolating winds from the nearest stations. For the Seoul and Jeju regional network, the meso-velocity scale has typically a same order of magnitude as the speed of the network averaged wind, revealing the large spatial variability of the Jeju network station imply topography and weather. Significant scatter in the relationship between spatial variability of the wind field and the wind speed is thought to be related to thermally-generated flows. The magnitude of the mesovelocity scale was significantly different along separation distance between stations, wind speed, intensity of prevailing wind, clear and cloudy conditions, topography. Resultant wind vectors indicate much different flow patterns along condition of contributing factors. As a result, the careful considerations on contributing factors such as prevailing wind in season, weather, and complex surface conditions with topography and land/sea contrast are required to assess the spatial variations of wind field on a regional network. The results in the spatial variation from the mesovelocity scale are useful to represent the characteristics of regional wind speed including lower surface conditions over the grid scale of large scale atmospheric model.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.55-63
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• 2012

Soil properties are not random values which is represented by mean and standard deviation but show spatial correlation. Especially, soils are highly variable in their properties and rarely homogeneous. Thus, the accuracy and reliability of probabilistic analysis results is decreased when using only one random variable as design parameter. In this paper, to consider spatial variability of soil property, one-dimensional random fields of coefficient of consolidation ($C_v$) were generated based on a Karhunen-Loeve expansion. A Latin hypercube Monte Calro simulation coupled with finite difference method for Terzaghi's one dimensional consolidation theory was then used to probabilistic analysis. The results show that the failure probability is smaller when consider spatial variability of $C_v$ than not considered and the failure probability increased when the autocorrelation distance increased. Thus, the uncertainty of soil can be overestimated when spatial variability of soil property is not considered, and therefore, to perform a more accurate probabilistic analysis, spatial variability of soil property needed to be considered.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Publications of The Korean Astronomical Society
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• v.22 no.4
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• pp.141-149
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• 2007

Preliminary time-series observations for short-period variability survey (SPVS) were carried out using a 155mm refractor and a $2k{\times}3k$ CCD camera at Bohyunsan Optical Astronomy Observatory. We found 21 new variable stars in the $90'{\times}60'$ test field region : 9 eclipsing binary stars, $5{\delta}$ Scuti type stars, a ${\gamma}$ Doradus type star, and 6 long period variables. The observing field center is R.A. $05^h\;00^m\;00^s$, DEC. $50^{\circ}\;00'\;00"$ (J2000.0). The period and amplitude ranges for the short-period variables, i.e., ${\delta}$ Scuti stars, were 0.052day - 0.107day and 0.012mag - 0.064mag, respectively.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.205-217
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• 2022

The thermodynamic properties of shaft lining concrete (SLC) are important evidence for the design and construction, and the spatial variability of concrete materials can directly affect the stochastic thermal analysis of the concrete structures. In this work, an array of field experiments of the concrete materials are carried out, and the statistical characteristics of thermophysical parameters of SLC are obtained. The coefficient of variation (COV) and scale of fluctuation (SOF) of uncertain thermophysical parameters are estimated. A three-dimensional (3-D) stochastic thermal model of concrete materials with heat conduction and hydration heat is proposed, and the uncertain thermodynamic properties of SLC are computed by the self-compiled program. Model validation with the experimental and numerical temperatures is also presented. According to the relationship between autocorrelation functions distance (ACD) and SOF for the five theoretical autocorrelation functions (ACFs), the effects of the ACF, COV and ACD of concrete materials on the uncertain thermodynamic properties of SLC are analyzed. The results show that the spatial variability of concrete materials is subsistent. The average temperatures and standard deviation (SD) of inner SLC are the lowest while the outer SLC is the highest. The effects of five 3-D ACFs of concrete materials on uncertain thermodynamic properties of SLC are insignificant. The larger the COV of concrete materials is, the larger the SD of SLC will be. On the contrary, the longer the ACD of concrete materials is, the smaller the SD of SLC will be. The SD of temperature of SLC increases first and then decreases. This study can provide a reliable reference for the thermodynamic properties of SLC considering spatial variability of concrete materials.

• The Bulletin of The Korean Astronomical Society
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• v.19
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• pp.30.2-31
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• 1994

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.221-237
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• 2005

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.32.1-32.1
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• 2021

We present the magnetic field strengths of CTA 102 using multi-frequency data at 2.6-343.5 GHz in order to study the physical origins of radio flares. The observations at 22 and 43 GHz were conducted using the single-dish radio telescopes of the Korean VLBI Network (KVN) from December 2012 until May 2018 (MJD 56200-58400). We used multi-frequency data obtained from the Effelsberg 100-m, OVRO 40-m, Metsähovi 14-m, IRAM 30-m, SMA, ALMA, and VLBA telescopes. During the period of the observations, two major flares (R1 and R2) are seen clearly at 15 and 37 GHz during MJD 57500-57800 and MJD 58000-58300, respectively. The source shows typical variability with time-scales ranging from 20-161 days at 15 GHz. The variability Doppler factor is in the range of 11.51-31.23. The quasi-simultaneous radio data are used to investigate the synchrotron spectrum of the source, finding that the synchrotron radiation is self-absorbed. The turnover frequency and the peak flux density of the synchrotron self-absorption (SSA) spectra are in ranges of 38.06-167.86 GHz and 1.49-10.38 Jy, respectively. From the SSA spectra, magnetic field strengths are estimated to be < 10 mG. The equipartition magnetic field strengths are larger than the SSA magnetic field strengths by a factor of > 100. This indicates that the radio flares may be related to a particle energy-dominated emission region.