• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.35-40
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• 2011

While high-definition precipitation maps with a 270 m spatial resolution are available for South Korea, there is little information on geospatial availability of precipitation water for the famine - plagued North Korea. The restricted data access and sparse observations prohibit application of the widely used PRISM (Parameter-elevation Regressions on Independent Slopes Model) to North Korea for fine-resolution mapping of precipitation. A hybrid method which complements the PRISM grid with a sub-grid scale elevation function is suggested to estimate precipitation for remote areas with little data such as North Korea. The fine scale elevation - precipitation regressions for four sloping aspects were derived from 546 observation points in South Korea. A 'virtual' elevation surface at a 270 m grid spacing was generated by inverse distance weighed averaging of the station elevations of 78 KMA (Korea Meteorological Administration) synoptic stations. A 'real' elevation surface made up from both 78 synoptic and 468 automated weather stations (AWS) was also generated and subtracted from the virtual surface to get elevation difference at each point. The same procedure was done for monthly precipitation to get the precipitation difference at each point. A regression analysis was applied to derive the aspect - specific coefficient of precipitation change with a unit increase in elevation. The elevation difference between 'virtual' and 'real' surface was calculated for each 270m grid points across North Korea and the regression coefficients were applied to obtain the precipitation corrections for the PRISM grid. The correction terms are now added to the PRISM generated low resolution (~2.4 km) precipitation map to produce the 270 m high resolution map compatible with those available for South Korea. According to the final product, the spatial average precipitation for entire territory of North Korea is 1,196 mm for a climatological normal year (1971-2000) with standard deviation of 298 mm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.105-112
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• 2018

To determine the position of receiver, the GPS (Global Positioning System) uses position information of satellites and pseudo ranges based on signals. These are reflected by surrounding structures and multipath errors occur. This paper proposes a method for multipath error reduction using digital images to enhance the accuracy. The goal of the study is to calculate the shielding environment of receiver using image processing and apply it to GPS positioning. The proposed method, firstly, performs a preprocessing to reduce the effect of noise on images. Next, it uses hough transform to detect the outline of building roofs and determines mask angles and permissible azimuth range. Then, it classifies the satellites according to the condition using the image processing results. Finally, base on point positioning, it computes the receiver position by applying a weight model that assigns different weights to the classified satellites. We confirmed that the RMSE (Root Mean Square Error) was reduced by 2.29m in the horizontal direction and by 15.62m in the vertical direction. This paper showed the potential for the hybrid of GPS positioning and image processing technology.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.06a
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• pp.476-484
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• 2005

This paper is to evaluate rather correctly $C^{14}$ ingestion dose that inhabitants around PWR plants can receive, and draw how to apply TF(Transfer Factor) to evaluate dose by the ingestion of animal products. For this, in this paper, dose assessment and analysis about existing materials related to TF were carried out, and the methodology to present TF was based on dose assessment and analysis result. The ingestion dose calculated using TFs presented by CSA and KEPRI was high or equal compared with SAM(Specific Activity Model) which is the most conservative, on the other hand, TFs given by NEC did not consider the effect according to volume change of animal at all, Therefore, it is judged that models used in the existing codes to asses the $C^{14}$ concentration into animal products must be improved to apply fundamentally hybrid model using transfer factors, that transfer factor on each animal products have to be developed through experiment for applying to our county.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.117-128
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• 2011

In this paper, we present a novel approach for contents-based medication image retrieval from a medication image database using the shape classification and color information of the medication. One major problem in developing a contents-based drug image retrieval system is there are too many similar images in shape and color and it makes difficult to identify any specific medication by a single feature of the drug image. To resolve such difficulty in identifying images, we propose a hybrid approach to retrieve a medication image based on shape and color features of the medication. In the first phase of the proposed method we classify the medications by shape of the images. In the second phase, we identify them by color matching between a query image and preclassified images in the first phase. For the shape classification, the shape signature, which is unique shape descriptor of the medication, is extracted from the boundary of the medication. Once images are classified by the shape signature, Hue and Saturation(HS) color model is used to retrieve a most similarly matched medication image from the classified database images with the query image. The proposed system is designed and developed especially for specific population- seniors to browse medication images by using visual information of the medication in a feasible fashion. The experiment shows the proposed automatic image retrieval system is reliable and convenient to identify the medication images.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.2
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• pp.79-93
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• 2015

Three-dimensional structures of large ocean rings in the Gulf Stream region are investigated using the HYbrid Coordinate Ocean Model (HYCOM). Numerically simulated flow structures around four selected cyclonic and anticyclonic rings are compared with two different horizontal resolutions: $1/12^{\circ}$ and $1/48^{\circ}$. The vertical distributions of Lagrangian Coherent Structures (LCSs) are analyzed using Finite Size Lyapunov Exponent (FSLE) and Okubo-Weiss parameters (OW). Curtain-shaped FSLE ridges are found in all four rings with extensions of surface ridges throughout the water columns, indicating that horizontal stirring is dominant over vertical motions. Near the high-resolution rings, many small-scale flow structures with size O(1~10) km are observed while these features are rarely found near the low-resolution rings. These small-scale structures affect the flow pattern around the rings as flow particles move more randomly in the high-resolution models. The dispersion rates are also affected by these small-scale structures as the relative horizontal dispersion coefficients are larger for the high-resolution models. The absolute vertical dispersion rates are, however, lower for the high-resolution models, because the particles tend to move along inclined eddy orbits when the resolution is low and this increases the magnitude of absolute vertical dispersion. Since relative vertical dispersion can reduce this effect from the orbital trajectories of particles, it gives a more reasonable magnitude range than absolute dispersion, and so is recommended in estimating vertical dispersion rates.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.199-208
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• 2020

Purpose: This study was performed to introduce an in vivo hybrid multimodality technique involving the coregistration of micro-computed tomography (micro-CT) and high-resolution magnetic resonance imaging (HR-MRI) to concomitantly visualize and quantify mineralization and vascularization at follow-up in a rat model. Materials and Methods: Three adult female rats were randomly assigned as test subjects, with 1 rat serving as a control subject. For 20 weeks, the test rats received a weekly intravenous injection of 30 ㎍/kg zoledronic acid, and the control rat was administered a similar dose of normal saline. Bilateral extraction of the lower first and second molars was performed after 10 weeks. All rats were scanned once every 4 weeks with both micro-CT and HR-MRI. Micro-CT and HR-MRI images were registered and fused in the same 3-dimensional region to quantify blood flow velocity and trabecular bone thickness at T0 (baseline), T4 (4 weeks), T8 (8 weeks), T12 (12 weeks), T16 (16 weeks), and T20 (20 weeks). Histological assessment was the gold standard with which the findings were compared. Results: The histomorphometric images at T20 aligned with the HR-MRI findings, with both test and control rats demonstrating reduced trabecular bone vasculature and blood vessel density. The micro-CT findings were also consistent with the histomorphometric changes, which revealed that the test rats had thicker trabecular bone and smaller marrow spaces than the control rat. Conclusion: The combination of micro-CT and HR-MRI may be considered a powerful non-invasive novel technique for the longitudinal quantification of localized mineralization and vascularization.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.264-286
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• 2008

In 2006, the share of energy in Korea amounted to 28% from the total import, 97% from overseas dependency, and 83% for the national Greenhouse Gas (GHG) emission in 2004. Thus, from the aspects of economical and environmental policies, an energy analysis is very important, for the industry to cope with the imminent pressure for climate change. However, the estimation of GHG gas emissions due to an energy use is still done in a primitive way, whereby each industry's usage is multiplied by coefficients recommended from international organizations in Korea. At this level, it is impossible to formulate the prevailing logic and policies in face of a new paradigm that seeks to force participation of developing countries through so called post-Kyoto Protocol. In this study, a hybrid energy input-output (E-IO) analysis is conducted on the basis of the input-output(IO) table of 2000 issued by the Bank of Korea in 2003. Furthermore, according to economic sectors, emission of the GHG relative to an energy use is characterized. The analysis is accomplished from four points of view as follows: 1) estimating the GHG emission intensity by 96 sectors, 2) measuring the contribution ratio to GHG emissions by 14 energy sources, 3) calculating the emission factor of 3 GHG compounds, and 4) estimating the total amount of national GHG emission. The total amount estimated in this study is compared with a national official statistical number. The approach could be an appropriate model for the recently spreading concept of a Life Cycle Analysis as it analyzes not only a direct GHG emission from a direct energy use but also an associated emission from an indirect use. We expect this model can provide a form for the basis of a future GHG reduction policy making.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.