• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.335-342
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• 2015

In this study, data linking module called GeoSWMM was developed using a typical secondary flooding model SWMM in order to improve the accuracy of the input data of SWMM and to map hourly inundation estimation areas that were not represented in the conventional inundation map. GeoSWMM is a data linking module of GIS and SWMM, which can generate a SWMM project file directly from sewer network GIS data. Utilizing the GeoSWMM the project file of SWMM model was constructed in the study area, Seocho 2-dong, Seoul. The actual flooding has occurred September 21, 2010 and the actual rainfall data were used for flood simulation. As a result, the outflow started from 2 PM due to the lack of water flow capacity of the sewage system. Based on the results, hourly inundation estimation maps were produced and compared with flood train map in 2010. The comparison showed about 66% matching in the overlap of inundation areas. By utilizing GeoSWMM that was developed in this study, it is easy to build the sewer network data for SWMM. In addition, the creation of hourly inundation estimation map using SWMM will be much help to flood disaster prevention plan.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.592-597
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• 2020

Currently, river survey data is mainly performed by acquiring longitudinal and cross-sectional data of rivers using total stations or the GNSS(Global Navigation Satellite System). There is not much research that addresses the use of LiDAR(Light Detection and Ranging)systems for surveying rivers. This study evaluates the applicability of using LiDAR data for surveying rivers The Ministry of Land, Infrastructure and Transport recently launched a drone-based river fluctuation survey. Pilot survey projects were conducted in major rivers nationwide. Studies related to river surveying were performed using the ground LiDAR(Light Detection And Ranging)system.Accuracy was ensured by extracting the linearity of the object and comparing it with the total station survey performance. Data on trees and other features were extracted to generate three-dimensional geospatial information for the point-cloud data on the ground.Deviations were 0.008~0.048m. and compared with the results of surveying GNSS and the use of drone LiDAR data. Drone LiDAR provided accurate three-dimensional spatial information on the entire target area. It was able to reduce the shaded area caused by the lack of surveying results of the target area. Analyses such as those of area and slope of the target sites are possible. Uses of drones may therefore be anticipated for terrain analyses in the future.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.144-159
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• 2006

Hydrological models are tools intended to realistically represent the basin's complex system in which hydrological characteristics result from a number of physical, vegetative, climatic, and anthropomorphic factors. Spatially distributed hydrological models were first developed in the 1960s, Remote sensing(RS) data and Geographical Information System(GIS) play a rapidly increasing role in the field of hydrology and water resources development. Although very few remotely sensed data can applied in hydrology, such information is of great. One of the greatest advantage of using RS data for hydrological modeling and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful model analysis, prediction and validation. In this paper, SLURP model is selected as semi-distributed hydrological model and MODIS Leaf Area Index(LAI), Normalized Difference Vegetation Index(NDVI) as Remote sensing input data to hydrological modeling of Kyung An-chen basin. The outlet of the Kyung An stage site was simulated, We evaluated two RS data, based on ability of SLURP model to simulate daily streamflows, and How the two RS data influence the sensitivity of simulated Evapotranspiration.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.113-122
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• 2007

The rapid growth of aerial survey and remote sensing technology has enabled the rapid acquisition of very large amounts of geographic data, which should be analyzed using real-time visualization technology. The level of detail(LOD) algorithm is one of the most important elements for realizing real-time visualization. We chose the triangulated irregular network (TIN) method to generate normalized digital elevation model(DEM) data. First, we generated TIN data using contour lines obtained from a two-dimensional(2D) digital map and created a 2D grid array fitting the size of the area. Then, we generated normalized DEM data by calculating the intersection points between the TIN data and the points on the 2D grid array. We used constrained Delaunay triangulation(CDT) and ray-triangle intersection algorithms to calculate the intersection points between the TIN data and the points on the 2D grid array in each step. In addition, we simulated a three-dimensional(3D) terrain model based on normalized DEM data with real-time visualization using a Microsoft Visual C++ 6.0 program in the DirectX API library and a quad-tree LOD algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8A
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• pp.820-829
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• 2006

IP-based design methodology has been popularly employed for SoC design to reduce design complexity and to cope with time-to-market pressure. Due to the request for high performance of current mobile systems, embedded SoC design needs a multi-processor to manage problems of high complexity and the data processing such as multimedia, DMB and image processing in real time. Interface module for communication between system buses and processors are required, since many IPs employ different protocols. High performance processors require interface module to minimize the latency of data transmission during read-write operation and to enhance the performance of a top level system. This paper proposes an automatic interface generation system based on FSM generated from the common protocol description sequence of a bus and an IP. The proposed interface does not use a buffer which stores data temporally causing the data transmission latency. Experimental results show that the area of the interface circuits generated by the proposed system is reduced by 48.5% on the average, when comparing to buffer-based interface circuits. Data transmission latency is reduced by 59.1% for single data transfer and by 13.3% for burst mode data transfer. By using the proposed system, it becomes possible to generate a high performance interface circuit automatically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.

• Journal of KIISE
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• v.42 no.1
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• pp.97-106
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• 2015

Recently, research on database encryption for data protection and query result authentication methods has been performed more actively in the database outsourcing environment. Existing database encryption schemes are vulnerable to order matching and counting attack of intruders who have background knowledge of the original database domain. Existing query result integrity auditing methods suffer from the transmission overhead of verification object. To resolve these problems, we propose a group-order preserving encryption index and a query result authentication method based on the encryption index. Our group-order preserving encryption index groups the original data for data encryption and support query processing without data decryption. We generate group ids by using the Hilbert-curve so that we can protect the group information while processing a query. Finally, our periodic function based data grouping and query result authentication scheme can reduce the data size of the query result verification. Through performance evaluation, we show that our method achieves better performance than an existing bucket-based verification scheme, it is 1.6 times faster in terms of query processing time and produces verification data that is 20 times smaller.

• Journal of The Geomorphological Association of Korea
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• v.19 no.3
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• pp.37-49
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• 2012

The objective of this paper is to propose a methodology for surface sediments classification in tidal flats that can combine ground survey data with high-resolution remote sensing data by multivariate kriging. Unlike conventional methodologies that have classified remote sensing data by using pre-classified sediment components, a new classification methodology presented in this paper first generates sediment component fraction maps and then classifies the sediments on a final stage. For generating sediment component fractions, regression kriging, as one of multivariate kriging algorithms, is applied to integrate ground survey data and remote sensing data. First, trend components of sand, silt, and clay are derived through regression analysis of ground survey data and spectral information from remote sensing data. Then, residuals at sample locations are computed and interpolated to generate residual components in the study area. Finally, the sediment component fractions are computed by adding the residuals to the trend components and are classified on a final stage. A case study at the Baramarae tidal flats with KOMPSAT-2 imagery is carried out to evaluate the classification capability of the proposed classification methodology. Through the case study, the proposed methodology showed the best classification accuracy, compared with the conventional classification methodologies. Especially, much improvement of classification accuracy for fine-grained sediments were also obtained. Therefore, it is expected that the presented classification methodology would be an effective one for surface sediments classification in tidal flats.