• Korean Journal of Remote Sensing
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• v.18 no.1
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Earthquakes and Structures
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• v.26 no.2
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• pp.97-115
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• 2024

In this paper, a database consisting of the dynamic shear modulus ratio and damping ratio test data of clay obtained from 406 groups of triaxial tests is constructed with the starting area of Xiong'an New Area as the research background. The aim is to study the nonlinear dynamic properties of clay in this area under cyclic loading. The study found that the effective confining pressure and plasticity index have certain influences on the dynamic shear modulus ratio and damping ratio of clay in this area. Through data analysis, it was found that there was a certain correlation between effective confining pressure and plasticity index and dynamic shear modulus ratio and damping ratio, with fitting degree values greater than 0.1263 for both. However, other physical indices such as the void ratio, natural density, water content and specific gravity have only a small effect on the dynamic shear modulus ratio and the damping ratio, with fitting degree values of less than 0.1 for all of them. This indicates that it is important to consider the influence of effective confining pressure and plasticity index when studying the nonlinear dynamic properties of clays in this area. Based on the above, prediction models for the dynamic shear modulus ratio and damping ratio in this area were constructed separately. The results showed that the model that considered the combined effect of effective confining pressure and plasticity index performed best. The predicted dynamic shear modulus ratio and damping ratio closely matched the actual curves, with approximately 88% of the data falling within ±1.3 times the measured dynamic shear modulus ratio and approximately 85.1% of the data falling within ±1.3 times the measured damping ratio. In contrast, the prediction models that considered only a single influence deviated from the actual values, particularly the model that considered only the plasticity index, which predicted the dynamic shear modulus ratio and the damping ratio within a small distribution range close to the average of the test values. When compared with existing prediction models, it was found that the predicted dynamic shear modulus ratio in this paper was slightly higher, which was due to the overall hardness of the clay in this area, leading to a slightly higher determination of the dynamic shear modulus ratio by the prediction model. Finally, for the dynamic shear modulus ratio and damping ratio of the engineering site in the starting area of Xiong'an New Area, we confirm that the prediction formulas established in this paper have high reliability and provide the applicable range of the prediction model.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• ETRI Journal
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• v.30 no.4
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• pp.506-515
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• 2008

The H.264/AVC standard has adopted new coding tools such as intra-prediction, variable block size, motion estimation with quarter-pixel-accuracy, loop filter, and so on. The adoption of these tools enables an H.264/AVC-coded bitstream to have more information than was possible with previous standards. In this paper, we propose an effective spatial error concealment method with low complexity in H.264/AVC intra-frame. From information included in an H.264/AVC-coded bitstream, we use prediction modes of intra-blocks to recover a damaged block. This is because the prediction direction in each prediction mode is highly correlated to the edge direction. We first estimate the edge direction of a damaged block using the prediction modes of the intra-blocks adjacent to a damaged block and classify the area inside the damaged block into edge and flat areas. Our method then 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 that the proposed method yields better video quality than conventional approaches.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.917-923
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• 2010

The local area weather information is very important element to estimate where the air-pollutant will flow. But the existing NBC hazard prediction model does not consider the local area weather information. So, in this paper, we present SN-HPM that uses the local area wether information to perform more accurate and reliable estimate, and embody it to program.

• The Journal of Engineering Geology
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• v.18 no.3
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• pp.303-309
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• 2008

A probability of slope hazards was predicted at a natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analyzing results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated. Also, the landslides prediction map was made up using the prediction model by the effect factors. The landslide susceptibility of stone relics was investigated as the grading classification of occurrence probability. In the landslides prediction map, the high probability area was $3,489m^2$ and it was 10.1% of total prediction area. The high probability area has over 70% of occurrence probability. If landslides are occurred at the predicted area, the three stories stone pagoda of Jinjeon-saji(National treasure No. 122) and the stone lantern of Jinjeon-saji(Treasure No.439) will be collapsed by debris flow.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.109-116
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• 2013

By IPCC climate change scenario, the socioeconomic actions such as the land use change are closely associated with the climate change as an up zoning action of urban development to increase green gas emission to atmosphere. Prediction of the land use change with rational quality can provide better data for understanding of the climate change in future. This study aims to predict land use change of Cheongju in future and SLEUTH model is used to anticipate with the status quo condition, in which the pattern of land use change in future follows the chronical tendency of land use change during last 25 years. From 40 years prediction since 2000 year, the area urbanized compared with 2000 year increases up to 87.8% in 2040 year. The ratios of the area urbanized from agricultural area and natural area in 2040 are decreased to 53.1% and 15.3%, respectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.635-644
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• 2005

This paper is. a study on relation between road traffic noise(RTN) and sound power level(PWL). At present, many experimental formulae and prediction formulae are used for prediction of RTN. But these formulae are difficult to appiy to the metropolitan area because these formulae are inaccurate in the different condition from reference condition. This paper calculate RTN and PWL of each prediction formula, choose the best one and make a noise map of the subject area. Procedure is as follows. First, calculate $L_{eq}$ of RTN using experimental formulae and prediction formulae. Second, calculate PWL using $L_{eq}$ of RTN and distance attenuation for point source at semi-free field. Third, choose the most accurate formula. And finally, make a noise map of the subject area at present and future. The result using noise map will be able to apply to application field. Noise mapping tool used on this paper is Raynoise program using Ray Tracing Method(RTM), Mirror Image Source Method(MISM) and Hybrid Method(HM).

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.323-330
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• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• Atmosphere
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• v.28 no.3
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• pp.247-261
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• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.