• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• The Journal of the Acoustical Society of Korea
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• v.32 no.3
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• pp.227-236
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• 2013

In this paper, we present a vocal suppression algorithm that can enhance the quality of music signal coded using Spatial Audio Object Coding (SAOC) in Karaoke mode. The residual vocal component in the coded music signal is estimated by using a cross prediction method in which the music signal coded in Karaoke mode is used as the primary input and the vocal signal coded in Solo mode is used as a reference. However, the signals are extracted from the same downmix signal and highly correlated, so that the music signal can be severely damaged by the cross prediction. To prevent this, a psycho-acoustic disturbance rule is proposed, in which the level of disturbance to the reference input of the cross prediction filter is adapted according to the auditory masking property. Objective and subjective test were performed and the results confirm that the proposed algorithm offers improved quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5C
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• pp.503-511
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• 2006

H.264 adopts new coding tools such as intra-prediction, loop filter, etc. The adoption of these tools enables an H.264-coded bitstream to have more information compared with previous standards. In this paper we proposed an effective spatial error concealment method for H.264. Among the information included in an H.264-coded bitstream, we use intra-mode for recovering a damaged block. This is because prediction direction in intra-mode is highly correlated to the edge direction of a lost macroblock. We first estimate the edge direction using intra-modes of blocks adjacent to a lost macroblock, and classify the area in a damaged macroblock into the edge and the flat area. And then our method recovers pixel values in the edge area using edge-directed interpolation, and recovers pixel values in the flat area using weighted interpolation. Simulation results show the proposed method yields better video quality than conventional approaches by 0.35 to 5.48 dB.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.734-741
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• 2012

A new lossless intra coding method based on residual transform is applied to the next generation video coding standard HEVC (High Efficiency Video Coding). HEVC includes a multi-directional spatial prediction method to reduce spatial redundancy by using neighboring samples as a prediction for the samples in a block of data to be encoded. In the new lossless intra coding method, the spatial prediction is performed as samplewise DPCM (Difference Pulse Code Modulation) but is implemented as block-based manner by using residual transform and secondary residual transform on the HEVC standard. Experimental results show that the new lossless intra coding method reduces the bit rate by approximately 6.45% in comparison with the lossless intra coding method previously included in the HEVC standard.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.86-99
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• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.851-858
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• 2009

In the last decades, there has been much interest in climate variability because its change has dramatic effects on humanity. Especially, the precipitation data are measured over space and their spatial association is so complicated. So we should take into account such a spatial dependency structure while analyzing the data. However, in linear models for analyzing the data, data sets show severely skewed distribution. In the paper, we consider the Box-Cox transformation to satisfy the normal distribution prior to the analysis, and employ a Bayesian hierarchical framework to investigate the spatial patterns. The data set we considered is monthly average precipitation of the third quarter of 2007 obtained from 347 automated monitoring stations in Contiguous South Korea.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2442-2451
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• 2020

The safety level of Sodium Fast Reactors is directly related with the sodium void reactivity. A low-void effect design has been proposed within the Horizon2020 ESFR-SMART project thanks to the introduction of a sodium plenum above the active core. In order to assess the impact of this core conception on transient analysis, a map with the spatial distribution of sodium void worth can be computed and fed into a point-kinetics-based transient code. Due to the spatial correlations between neighboring zones, the global effect of voiding two different axial or radial regions is not necessarily the sum of both individual contributions. Neglecting those correlations in the void worth map and consequently in the transient analysis may lead to an unrealistic prediction of the transient sequences. In this work, a method based on sensitivity analysis and similarity assessment is proposed for predicting those correlations. The method proved to be able to establish correlations between axial slices of a sub-assembly and was checked against realistic sodium void propagation patterns.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.12
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• pp.1522-1531
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• 1988

The purpose of this paper is to explain eigenstructure based spatial spectrum estimation algorithms computing better estimates than the other approaches. Also, as an approach to overcome performance degradations in a multipath environments, the notion of forward and backwark spatial smoothing is discussed. Intensive simulation results,which include the comparisons of the eigenbased spatial spectral estimation algorithms in the situations of faulty estimation of the number of signals, are presented. The simulation results have shown that overestimation of the number of signals is more desirable than underestimation in using EV (Eigen Vector) and MUSIC (Multiple Signal Classification) algorithms and that underestimation of the number of signals is better strategy than overestimation in using eigenstructure based LP(Linear Prediction) algorithms.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.