• Steel and Composite Structures
• /
• v.3 no.4
• /
• pp.277-287
• /
• 2003

The low boundary of load carrying capacity of the elastic-plastic spatial grid structures depend on numerous values and their variability assumed in designing process. Analysed influence all this values in searching for optimal variant of the structure lead to too great problem even taking into consideration actual computational power we have in disposal. Therefore one can take only a few values which have greatest influence on the optimal choice. In optimal analysis of the elastic-plastic spatial grid structures the previously proposed method with subsequent modification (Karczewski 1980), (Karczewski, Barszcz and Donten 1996), (Karczewski and Donten 2001) as well as computer program which was worked out by Donten K. to make possible practical utilisation this method was employed. The paper deal with evaluation of influence dimensions of particular values for choice of optimal variant of the structure. One among this values is distribution of the struts in the structure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.3
• /
• pp.225-231
• /
• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.8
• /
• pp.147-159
• /
• 1994

The performance of the improvement method of spatial resolution for satellite images based on the generalized inverse matrix is superior to the conventional methods. But, this method calculates the coefficient values for extracting the spatial information from the relation between a small pixel and large pixels. Accordingly it has the problem of remaining the blocky patterns at the result image. In this paper, a new generalized inverse matrix method is proposed which is different in the calculation method of coefficient values for extracting the spatial information. In this proposed metod, it calculates the coefficient values for extracting the spatial information from the relation between a small pixel and small pixels. Consequently it can improve the spatial resolution more efficiently without remaining the blocky patterns at the result image. The effectiveness of the proposed method is varified by simulation experiments with real TM image data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.921-926
• /
• 2003

This paper describes a new type of the spatial filter detector (SFD) with variable and multi-valued weighting function. This SFD called variable spatial filter detector with multi-valued weighting function (VSFDwMWF) uses current-mode circuits for noise resistance and high-resolution weighting values. Total weighting values consist of 7bit, 6-signal bit and 1-sign bit. We fabricate VSFDwMWF chip using Rohm 0.35${\mu}$m CMOS process. VSFDwMWF chip includes two-dimensional 10${\times}$13 photodiode array and current-mode weighting control circuit. Simulation shows the weighting values are varied and multi-valued by external switching operation. The layout of VSFDwMWF chip is shown.

• KIEAE Journal
• /
• v.8 no.5
• /
• pp.17-22
• /
• 2008

Dosan Seowon, one of the earliest and most prestigious private Confucian academies in Korea, is an example of a heritage property citizens wish to sustain for the benefit of future generations. It is recognized of its contribution to the Korean society and as an architectural and historical interest. This study conducts architectural and cultural research about Dosan Seowon by scrutinizing its evidential, historical, aesthetic and spatial values. By doing so, it is possible to recognize how appreciated qualities are vulnerable to harm through only understanding their heritage values without practical management solutions. That understanding should then provide the basis for developing and implementing management strategies (including maintenance, cyclical renewal and repair) that will best sustain the heritage values in a physical aspect. The conclusion suggests that communication about Dosan Seowon among those who are concerened is significant.

• Journal of the Korean Geographical Society
• /
• v.48 no.6
• /
• pp.879-896
• /
• 2013

The assessed land values and housing prices have been widely utilized as a basic information for the land and house trades and for evaluating governmental and local taxes. However, there exists a price difference in actual markets between the assessment level and assessed land values or housing prices. This paper emphasizes the spatial mismatch between the assessed land values and housing market prices and particularly addresses the following two aspects by focusing on spatial effects of the modifiable areal units, which would substantially affect the estimation of the assessed land values and housing prices. First, we examine the spatial distributions of the assessed land values and housing market prices, and the gap between those prices, on the basis of the aggregated spatial units(i.e., aggregation districts). Second, we explore the scale effect of the MAUP(modifiable areal unit problem) generally embedded in estimating the prices of the sampled standard lands and houses, and calibrating the correction index for the land values and housing prices for the individuals. For the application, we analysed the land values and housing prices in Seoul utilizing GIS and statistical software. As a result, some spatial clusters that the housing market prices are significantly higher than the assessed land values were identified at a finer geographic level. Also, it was empirically revealed that the statistical results from the regression of regional variables on the assessed land values for the individuals are significantly affected by the aggregation levels of the spatial units.

• Spatial Information Research
• /
• v.10 no.2
• /
• pp.233-246
• /
• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.1
• /
• pp.51-60
• /
• 2020

The purpose of this study is to determine the spatial and temporal resolutions that can represent land surface characteristics comprised of various land use using Landsat/AMSR2-based soil moisture data. We estimated the Landsat (30 m×30 m)-based soil moisture values using the soil moisture regression model. Then, the Landsat (30 m×30 m)-based soil moisture (reference values) were resampled to the relatively coarse resolutions from 1 km to 4 km, respectively. Comparing the reference values to the resampled soil moisture values, we confirmed that uncertainties were increased with the spatial resolutions of 2 km~4 km indicating that the spatial resolution of 1 km×1 km is required to represent the complicated land surface. Also, the AMSR2 soil moisture values have less uncertainties compared to SMAP data with the temporal resolution of 1~2 days. Thus, our findings can be useful for various areas such as agriculture, hydrology, forest, etc.

• Journal of Environmental Science International
• /
• v.27 no.7
• /
• pp.499-507
• /
• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Spatial Information Research
• /
• v.19 no.4
• /
• pp.81-89
• /
• 2011

This paper presents a multivariate kriging algorithm that integrates categorical data as secondary data for spatial interpolation of sparsely sampled ground survey data. Instead of using constant mean values in each attribute of categorical data, disaggregated local mean values at target grid points are first estimated by area-to-point kriging and then are used as local mean values in simple kriging with local means. This algorithm is illustrated through a case study of spatial interpolation of a geochemical copper element with geological map data. Cross validation results indicates that the presented algorithm leads to significant respective improvement of 15% and 25% in prediction capability, compared with univariate ordinary kriging and conventional simple kriging with constant mean values. It is expected that the multivariate kriging algorithm applied in this study would be effectively applied for spatial interpolation with categorical data.