• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.245-254
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• 2018

Impact of tropospheric correction techniques on accuracy of the GPS (Global Positioning System) derived ellipsoidal heights has been experimentally assessed in this paper. To this end, 247 baselines were constructed from a total of 88 CORS (Continuously Operating Reference Stations) in Korea. The GPS measurements for seven days, acquired from the so-called integrated GNSS (Global Navigation Satellite Systems) data center via internet connection, have been processed by two baseline processing software packages with an application of the empirical models, such as Hopfield, modified Hopfield and Saastamoinen, and the estimation techniques based on the DD (Double-Differenced) measurements and the PPP (Precise Point Positioning) technique; hence a total number of the baseline processed and tested was 8,645. Accuracy and precision of the estimated heights from the various correction schemes were analyzed about baseline lengths and height differences of the testing baselines. Details of these results are summarized with a view to hopefully providing an overall guideline of a suitable selection of the modeling scheme with respect to processing conditions, such as the baseline length and the height differences.

• Journal of the Korean Geotechnical Society
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• v.39 no.6
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• pp.5-12
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• 2023

Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.

• Journal of KIISE
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• v.41 no.9
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• pp.728-743
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• 2014

Finger printing and sequence alignment are well-known approaches for document similarity comparison. A fingerprinting method is simple and fast, but it can not find particular similar regions. A string alignment method is used for identifying regions of similarity by arranging the sequences of a string. It has an advantage of finding particular similar regions, but it also has a disadvantage of taking more computing time. The Multi-Level Alignment (MLA) is a new method designed for taking the advantages of both methods. The MLA divides input documents into uniform length blocks, and then extracts fingerprints from each block and calculates similarity of block pairs by comparing the fingerprints. A similarity table is created in this process. Finally, sequence alignment is used for specifying longest similar regions in the similarity table. The MLA allows users to change block's size to control proportion of the fingerprint algorithm and the sequence alignment. As a document is divided into several blocks, similar regions are also fragmented into two or more blocks. To solve this fragmentation problem, we proposed a united block method. Experimentally, we show that computing document's similarity with the united block is more accurate than the original MLA method, with minor time loss.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.10
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• pp.788-793
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• 2017

This paper presents the verification of accuracies of theoretical models for calculating the microwave reflections from rough sea surfaces. First of all, the Pierson-Moskowitz ocean spectrum was used to generate the rough sea surfaces. Then the relationship between the significant wave heights, root-mean-square(RMS) heights and wind speed was derived by estimating the significant wave heights and RMS heights of the generated sea surfaces according to various wind speeds, and compared the derived relationship with other measurement data sets. The reflection coefficients of the sea surfaces were calculated by using a numerical method(the moment method). Then, the numerical results were compared with Ament model, PO(Physical Optics) model, GO(Geometrical Optics) model and B-M(Brown-Miller) model for various roughness conditions(wind speed) and incidence angles. It was found that the Ament model is not accurate except for a very low roughness conditions($kh_{rms}$<0.4, k is wavenumber and $h_{rms}$ is RMS height). It was also found that at incidence angles lower than $70^{\circ}$, the PO and the GO models agree well with the numerical results, while the B-M model agrees well with the numerical analysis results at incidence angles higher than $80^{\circ}$ for very rough sea surfaces with $kh_{rms}$>10.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.902-915
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• 2011

Recently in the area-based stereo matching field, Adaptive Support-Weight (ASW) method that weights matching cost adaptively according to the luminance intensity and the geometric difference shows promising matching performance. However, ASW requires more computational cost than other matching algorithms do and its real-time implementation becomes impractical. By applying Integral Histogram technique after approximating to the Bilateral filter equation, the computational time of ASW can be restricted in constant time regardless of the support window size. However, Integral Histogram technique causes loss of the matching accuracy during approximation process of the original ASW equation. In this paper, we propose a novel algorithm that maintains the ASW algorithm's matching accuracy while reducing the computational costs. In the proposed algorithm, we propose Sub-Block method that groups the pixels within the support area. We also propose the method adjusting the disparity search range depending on edge information. The proposed technique reduces the calculation time efficiently while improving the matching accuracy.

• Journal of Korea Water Resources Association
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• v.34 no.1
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• pp.57-65
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• 2001

A numerical model for analyzing transcritical flow in open channel is tested to various cases of channel shape. As the numerical models developed for transcritical flow until now mainly focused on the application to only prismatic or hypothetical channels, there are some restrictions to apply the nonprismatic channels. In this study, to verify the accuracy and stability of second-order implicit ENO scheme, the numerical model was applied to the channels which haute the varying channel bed and width. Also the numerical model was applied to unsteady flow as well as steady flow. The study shows that the numerical model provides good accuracy in the calculation of stage and velocity with no numerical oscillation, particularly in the calculation of hydraulic jump and discontinous flow Then the implicit ENO scheme demonstrated good accuracy as a high-resolution scheme and stability as an implicit scheme.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.48-56
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• 2004

This research focuses on the development of a fast datum transformation model to be used in GIS that utilizes real-time data transformation. Instance, when a GIS data constructed according to a datum is conformed to another datum, instead of transforming the axes of the original data, the data is transformed right before the results are reflected on the monitor. In this research, the prospects of calculating transformation parameters for every grid cells on the area based on two-dimensional conformal transformation model in order to decrease real-time datum transformation time while maintaining a high accuracy has been investigated. Research results showed that for a fixed area, the accuracies of the two-dimensional conformal transformation and the three-dimensional datum transformation, which requires more computing time, were almost equal and fast transformation speed, high accuracy real-time datum transformation is made feasible by implementing the grid-divided two-dimensional conformal transformation model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.76-86
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• 2013

As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

• Journal for History of Mathematics
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• v.24 no.2
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• pp.1-15
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• 2011

Approximation is a very useful approach in mathematics research. It was the same in traditional Chinese and Chosun mathematics. This study derived five characteristics from approximation approaches which were found in Chinese and Chosun mathematical books: improvement of approximate values, common and inevitable use of approximate values, recognition of approximate values and their reasons, comparison of their exactness, application of approximate principles. Through these characteristics, we can infer what Chinese and Chosun mathematicians recognized approximate values and how they manipulated them. They took approximate approaches by necessity or for the sake of convenience in mathematical study and its applications. Also, they tried to improve the degree of exactness of approximate values and use the inverse calculations to check them.