• Spatial Information Research
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• v.3 no.1
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• pp.47-53
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• 1995

The weed survey in lowland rice fields through Korea was conducted in 1992 to determine a change of the weed communities based on different regions, soil types, planting methods, and cultural practices. GIS was applied to identify a spatial analysis of predominant weed species in specific region. On behalf of vegetatine analysis such as absolute and relative density, absolute and relative frequency, importance value, and summed dominance ratio(SDR), there was highly dominant with a perennial weed species, Eleocharis kuroguwai Ohwi over whole country. However, in particular it was most predominant at southem area of Gyunggi province in Korea. Thus, rice farmers of this area have to introduce a specific comperhensive control strategy against this predominant weed species.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• Korean Journal of Microbiology
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• v.25 no.1
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• pp.40-45
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• 1987

The gene for the Bdi I modification enzyme, which is one of Bdi I restriction-modification system, fromBrevibacterium divaricatum FERM 5948 was cloned and expressed in E. coli. For cloning of the Bdi I methylase gene, we have initially used three cloning site(EcoRI, BamHI and Sal I) of plasmid vector pBR 322 and adopted the retransformation method after Bdi I restriction endonuclease cleavage. Selection of transformants carrying the gene was based on the resistance of the modified plasmid encoding the enzyme to cleavage by Bdi I restriction enzyme, and the recombinant plasmid pBDIM 116 containing 5.6kb EcoRI insery was proved to carry the gene. Crude cell extracts prepared from strains carrying the plasmid pBDIM 116 contained an S-adenosylmethionine-dependent methyltransferase activity specific for the Bdi I recognition site, ATCGAT. The restriction map was constructed with 11 restriction enzyme, and the Bdi I restriction-modification system was also discussed.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.579-586
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• 2002

The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge information. In the edge descriptor, the edge information is the edge histogram derived from a gray-level value image. This paper proposes a new method which extracts color edge information from color images and a new approach for the contents-based image retrieval based on the color edge histogram. The poposed method and technique are applied to image retrieval of the e-catalogue. For the evaluation, the results of image retrieval using the proposed approach are compared with those of image retrieval using the edge descriptor by MPEG-7 and the statistics shows the efficiency of the proposed method. The proposed color edge model is made by combining the R,G,B channel components vectorially and by characterizing the vector norm of the edge map. The color edge histogram using the direction of the color edge model is subsequently used for the contents-based image retrieval.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.266-274
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• 2009

An abdominal aortic aneurysm occurs most commonly in older individuals (between 65 and 75), and more in men and smokers. The most important complication of an abdominal aortic aneurysm is rupture, which is most often a fatal event. An abdominal aortic aneurysm weakens the walls of the blood vessel, leaving it vulnerable to bursting open, or rupturing, and spilling large amounts of blood into the abdominal cavity. surface modeling is very useful to surgery for quantitative analysis of abdominal aortic aneurysm. the 3D representation and surface modeling an abdominal aortic aneurysm structure taken from Multi Detector Computed Tomography. The construction of the 3D model is generally carried out by staking the contours obtained from 2D segmentation of each CT slice, so the quality of the 3D model strongly defends on the precision of segmentation process. In this work we present deformable model algorithm. deformable model is an energy-minimizing spline guided by external constraint force. External force which we call Gradient Vector Flow, is computed as a diffusion of a gradient vectors of gray level or binary edge map derived from the image. Finally, we have used snakes successfully for abdominal aortic aneurysm segmentation the performance of snake was visually and quantitatively validated by experts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1755-1763
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• 2007

Specially, location information can say that is important information in this ubiquitous environment and solutions of new GIS system to utilize this are required. Examine method to improve web geography information service system because this paper uses XML and presented specific implementation result. Through internet. it planed various geography information that offer in form of service that do not receive restriction of heterogeneous system environment. Also, did thing that is easy when request service about geography information that user need and deliver fast by purpose. Supplement and to improve open-ended and XML of form normalized use and developed existent web geography information service. So that can use map service in web browser without setup of application program that buy expensive GIS software or offer in each company design and implement.

• Asia-Pacific Journal of Business
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• v.12 no.1
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• pp.209-223
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• 2021

Purpose - The purpose of this study was to investigate the relationship between Korea agricultural productions and Greenhouse Gas (GHG) emissions based on Environmental Kuznets Curve (EKC) hypothesis. Design/methodology/approach - This study utilized time series data of economic growth, greenhouse gas, agricultural productions, trade dependency, and energy usages. In order to econometric procedure of EKC hypothesis, this study utilized unit root test and cointegration test to check staionarity of each variable and also adopted Vector Error Correction Model (VECM) and Ordinary Least Square (OLS) to analyze the short and long run relationships. Findings - In the short run, greenhouse gas emissions resulting from economic growth show an inverse U-shape relationship, and an increase in agricultural production and energy consumption led to increase in greenhouse gas emission. In the long run, total GHG emissions and CO2 emissions show an N-shaped relationship with economic growth, and an increase in agricultural production has resulted in a decrease in total GHG and CO2 emissions. However, methane (CH4) and nitrous oxide (N2O) emissions showed an inverse U-shape relationship with economic growth, which indicated the environment and production process of agricultural production. Research implications or Originality - Korea agricultural production has different effects on the GHG emission sources, and in particular, methane (CH4) and nitrous oxide (N2O) emissions show to increase as the agricultural production expansions, so policy or technological development in related sector is required. Especially, in the context of the 2030 GHG reduction road-map, if GHG-related reduction technologies or policies are spread, national GHG emission reduction targets can be achieved and this is possible to predict the decline in production in the sector and damage to the related industries.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.623-629
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• 2018

In this paper, we propose a real-time obstacle detection and avoidance path generation algorithm for UAV. 2-D Lidar is used to detect obstacles, and the detected obstacle data is used to generate real-time histogram for local avoidance path and a 2-D SLAM map used for global avoidance path generation to the target point. The VFH algorithm for local avoidance path generation generates a real-time histogram of how much the obstacles are distributed in the vector direction and distance, and this histogram is used to generate the local avoidance path when detecting near fixed or dynamic obstacles. We propose an algorithm, called modified $RRT^*-Smart$, to overcome existing limitations. That generates global avoidance path to the target point by creating lower costs because nodes are checked whether or not straight path to a target point, and given arbitrary lengths and directionality to the target points when nodes are created. In this paper, we prove the efficient avoidance maneuvering through various simulation experiment environment by creating efficient avoidance paths.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Asian Journal of Business Environment
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• v.13 no.2
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• pp.9-20
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• 2023

Purpose: This study aims to map the spatial distribution of poverty using nighttime light satellite images as a proxy indicator of economic activities and infrastructure distribution in D.I Yogyakarta, Indonesia. Research design, data, and methodology: This study uses official poverty statistics (National Socio-economic Survey (SUSENAS) and Poverty Database 2015) to compare satellite imagery's ability to identify poor urban areas in D.I Yogyakarta. National Socioeconomic Survey (SUSENAS), as poverty statistics at the macro level, uses expenditure to determine the poor in a region. Poverty Database 2015 (BDT 2015), as poverty statistics at the micro-level, uses asset ownership to determine the poor population in an area. Pearson correlation is used to identify the correlation among variables and construct a Support Vector Regression (SVR) model to estimate the poverty level at a granular level of 1 km x 1 km. Results: It is found that macro poverty level and moderate annual nighttime light intensity have a Pearson correlation of 74 percent. It is more significant than micro poverty, with the Pearson correlation being 49 percent in 2015. The SVR prediction model can achieve the root mean squared error (RMSE) of up to 8.48 percent on SUSENAS 2020 poverty data.Conclusion: Nighttime light satellite imagery data has potential benefits as alternative data to support regional poverty mapping, especially in urban areas. Using satellite imagery data is better at predicting regional poverty based on expenditure than asset ownership at the micro-level. Light intensity at night can better describe the use of electricity consumption for economic activities at night, which is captured in spending on electricity financing compared to asset ownership.