• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.749-755
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• 2023

A new approximation method was proposed for treating the non-resonant spatial self-shielding effects of double heterogeneous region such as the double heterogeneous effect of VHTR fuel compact in the thermal energy range and that of BP compact with BISO. The method was developed based on the effective homogenization method and a spherical unit cell model with explicit coated layers and a matrix layer. The self-shielding factor was derived from the relation between the collision probabilities for a double heterogeneous compact and the effective cross section for the homogenized compact. First, the collision probabilities and transmission probabilities for all layers of the spherical model were calculated using conventional collision probability solver. Then, the effective cross section for the homogenized sphere cell representing the homogenized compact was obtained from the transmission probability calculated using the probability density function of a chord length. The verification calculations revealed that the proposed method can predict the self-shielding factor with a maximum error of 2.3% and the double heterogeneous effect with a maximum error of 200 pcm in the typical VHTR problems with various packing fractions and BP compact sizes.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.91-98
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• 2016

In order to apply Kriging methods for geostatistics of spatial data, an estimation of spatial coherency functions is required priorly based on the spatial distance between measurement points. In the study, the typical coherency functions, such as semi-variogram, homeogram, and covariance function, were estimated using the national geoid model. The test area consisting of 2°×2° and the Unified Control Points (UCPs) within the area were chosen as sampling measurements of the geoid. Based on the distance between the control points, a total of 100 sampling points were grouped into distinct pairs and assigned into a bin. Empirical values, which were calculated with each of the spatial coherency functions, resulted out as a wave model of a semi-variogram for the best quality of fit. Both of homeogram and covariance functions were better fitted into the exponential model. In the future, the methods of various Kriging and the functions of estimated spatial coherency need to be studied to verify the prediction accuracy and to calculate the Mean Squared Prediction Error (MSPE).

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.614-630
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• 2006

Improved estimates of populations at risk for quick and effective response to natural and man-made disasters require spatial disaggregation of zonal population data because of the spatial mismatch problem in areal units between census and impact zones. This paper implements a dasymetric surface model to facilitate spatial disaggregation of the population of a census block group into populations associated with each constituent pixel and evaluates the performance of the surface-based spatial disaggregation model visually and statistically. The surface-based spatial disaggregation model employed geographic information systems (GIS) to enable dasymetric interpolation to be guided by satellite-derived land use and land cover data as additional information about the geographic distributor of population. In the spatial disaggregation, percent cover based empirical sampling and areal weighting techniques were used to objectively determine dasymetric weights for each grid cell. The dasymetric population surface for the Atlanta metropolitan area was generated by the surface-based spatial disaggregation model. The accuracy of the dasymetric population surface was tested on census counts using the root mean square error (RMSE) and an adjusted RMSE. The errors related to each census track and block group were also visualized by percent error maps. Results indicate that the dasymetric population surface provides high-precision estimates of populations as well as the detailed spatial distribution of population within census block groups. The results also demonstrate that the population surface largely tends to overestimate or underestimate population for both the rural and forested and the urban core areas.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.1
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• pp.1-9
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• 2016

Range-based wireless localization system must measure accurate range between a mobile node (MN) and reference nodes. However, non-line-of-sight (NLOS) error caused by the spatial structures disturbs the localization system obtaining the accurate range measurements. Localization methods using the range measurements including NLOS error yield large localization error. But filter-based localization methods can provide comparatively accurate location solution. Motivated by the accuracy of the filter-based localization method, a filter residual-based NLOS error estimation method is presented in this paper. Range measurement-based residual contains NLOS error. By considering this factor with NLOS error properties, NLOS error is mitigated. Also a process noise covariance matrix tuning method is presented to reduce the time-delay estimation error caused by the single dynamic model-based filter when the speed or moving direction of a MN changes, that is the used dynamic model is not fit the current dynamic of a MN. The presented methods are evaluated by simulation allowing direct comparison between different localization methods. The simulation results show that the presented filter is more accurate than the iterative least squares- and extended Kalman filter-based localization methods.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.680-686
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• 2003

Digital waveguide model is a general method that is used in physical modeling of musical instruments. Wave motion is analyzed by time or by space in digital waveguide model. Because sampling is made via time, it is general that musical instrument model is described by wave motion of time. In this paper, we synthesized the musical instrument sound by adding instrument body model to the spatial based string model. In this way, we could improve sound quality and process musical instrument model's tone control variables effectively. We explained about delay error that happens in string and body in space based sampling and showed method to process fractional delay using FD (Fractional Delay)filter. Finally, we explained the relation between tone quality and number of delays. And we also compared the result with time base digital waveguide model.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.