• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.337-356
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• 2017

The purpose of the research is to explore the main cause and effect of successful marriage that Korean husbands, who are multicultural families had. To do the research, the experiment has been done with 7 husbands for 2 months. The 7 husbands took depths interview and analyzed the result by grounded theory approach. As a result, the husband experienced 'repentance of marriage' because of 'unready marriage', but 'Raising children', 'Helping housework' and 'recognition from friends and colleague' have affected the husbands to have a successful marriage adaptation. Based on the result, a political and practical proposal has been proposed to the Korean husbands who are multi-cultural family. Here are the examples of the proposal. As a social welfare policy, 'obligation of information offering for prospective spouse', 'following the Labor Standards and parental leave', 'expansion of visiting supervise system' and 'making guidance for husbands and distribute map of Immigration Office, Multicultural Family Support Center and Community Center' have proposed. And as an alternative plan for a practical social welfare policy and continuous social awareness improvement, 'various education program', 'a program with domestic married couple', 'a program with parents-in-law', 'a specific program to increase the housework participation for husbands' and necessity of development and practice of group program for husband have been emphasized.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.327-336
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• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.

• Journal of agriculture & life science
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• v.45 no.1
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• pp.21-32
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• 2011

This study was conducted to estimate carbon dioxide $(CO_{2})$ absorption and bioenergy supply potential of forests in Muju county based on GIS RS In results, it was estimated that 7,800,130 $tCO_{2}$ was absorbed and all bioenergy supply potential of 11,868,202,837 Mcal was available. Futhermore, bioenergy supply potential of 314,876,637 Mcal was available each year that was able to be supplied for the hitting during winter period to 11,241 households. This was more than all households of 10,902 in Muju county. This study suggested the methodology for estimating $CO_{2}$ absorption and bioenergy supply potential of forests on the national scale, and it was believed that reliability would be increased by estimation on the national scale using detailed forest information based on the latest techniques such as GIS RS techniques.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.261-274
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• 2004

The feature extraction using remotely sensed imagery has been recognized one of the important tasks in remote sensing applications. As the high-resolution imagery are widely used to the engineering purposes, need of more accurate feature information also is increasing. Especially, in case of the automatic extraction of linear feature such as road using mid or low-resolution imagery, several techniques was developed and applied in the mean time. But quantitatively comparative analysis of techniques and case studies for high-resolution imagery is rare. In this study, we implemented a computer program to perform and compare GDPA (Gradient Direction Profile Analysis) algorithm and Hough transformation. Also the results of applying two techniques to some images were compared with road centerline layers and boundary layers of digital map and presented. For quantitative comparison, the ranking method using commission error and omission error was used. As results, Hough transform had high accuracy over 20% on the average. As for execution speed, GDPA shows main advantage over Hough transform. But the accuracy was not remarkable difference between GDPA and Hough transform, when the noise removal was app]ied to the result of GDPA. In conclusion, it is expected that GDPA have more advantage than Hough transform in the application side.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.141-152
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• 2008

The 3D building modeling is one of crucial components in constructing 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes, which indeed take great amount of time and efforts. In recent years, many researches on 3D building modeling using aerial LiDAR data have been actively performed to aim at overcoming the limitations of existing 3D building modeling methods. Either techniques with interpolated grid data or data fusion with digital map and images have been investigated in most of existing researches on 3D building modeling with aerial LiDAR data. The paper proposed a method of 3D building modeling with LiDAR data only. Firstly, octree-based segmentation is applied recursively to LiDAR data classified as buildings in 3D space until there are no more LiDAR points to be segmented. Once octree-based segmentation is completed, each segmented patch is thereafter merged together based on its geometric spatial characteristics. Secondly, building model components are created with merged patches. Finally, a 3D building model is generated and composed with building model components. The experimental results with real LiDAR data showed that the proposed method was capable of modeling various types of 3D buildings.

• Journal of The Geomorphological Association of Korea
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• v.20 no.1
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• pp.85-96
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• 2013

This study collected national secondary and tertiary nationwide natural environment survey data of the National Institute of Environmental Research as well as the National Park's secondary and tertiary natural resource research data executed by the Korea National Park Service. The data collection is aimed at reclassification the geomorphological landscape resources of each park in varying types. The results generated a total of 3,169 geomorphological landscape resources within all the national parks. Among all the geomorphological landscape resources, 794 landscape resources were judged as Level I, which accounts for 36.9%. Next, 546 landscape resources were judged as Level II, or 25.3%, and 459 landscape resources judged as Level III, or 21.3%. Lastly, 191 landscape resources were judged as Level IV, having the lowest conservation level, or 8.9%. The number of Level I landscape resources for each national park includes 207 sites on Seoraksan, 92 sites on Dadohaehaesang Park, 84 sites on Jirisan, and 60 sites at the Taeanhaean, respectively. Dadohaehaesang National Park, Seoraksan National Park, Taeanhaean National Park, Jirisan National Park, Songnisan National Park, and Gyeryongsan National Park were evaluated as national parks having excellent landscape resources. To use these excellent landscape resources, there is a need to increase visitors' satisfaction and increase their interest in and understanding of landscape resources. To achieve this, a landscape viewpoint map must be composed and installed at the entrance or at certain points to provide visitors with useful information regarding the geomorphological landscape resources.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.404-412
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• 2019

The purpose of this study is to develop the usability evaluation contents based on the needs of different stakeholder's related to the usability of the product, and to derive the design direction and apply it as the evaluation standard by applying the product design based on the results. I created a stakeholder map for a fire detector product and identified stakeholders related to usability. Based on 3 factors(Physical, cognitive, emotional) of the usability evaluation, I conducted survey on the building users and the building managers who have different requirements. There are 12 directions (ease of installation, durability, maintainability, additional functionality, effectiveness, attractiveness, visibility, consistency of information, environmental harmony, consistency, Image suitability, reliability). Through weighted analysis of three usability evaluation factors, I found factors were ranked in the same order of importance, but they were different in importance figure. Based on the results of the survey, overall product usability aspects were improved but effectiveness and environmental coordination aspects needed to be improved.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.391-403
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• 2018

Genome resequencing by next-generation sequencing technology can reveal numerous single nucleotide polymorphisms (SNPs) within a closely-related cultivar group, which would enable the development of sufficient SNP markers for mapping and the identification of useful genes present in the cultivar group. We analyzed genome sequence data from 13 Korean japonica rice varieties and discovered 740,566 SNPs. The SNPs were distributed at 100-kbp intervals throughout the rice genome, although the SNP density was uneven among the chromosomes. Of the 740,566 SNPs, 1,014 SNP sites were selected on the basis of polymorphism information content (PIC) value higher than 0.4 per 200-kbp interval, and 506 of these SNPs were converted to Kompetitive Allele-Specific PCR (KASP) markers. The 506 KASP markers were tested for genotyping with the 13 sequenced Korean japonica rice varieties, and polymorphisms were detected in 400 KASP markers (79.1%) which would be suitable for genetic analysis and molecular breeding. Additionally, a genetic map comprising 205 KASP markers was successfully constructed with 188 $F_2$ progenies derived from a cross between the varieties, Junam and Nampyeong. In a phylogenetic analysis with 81 KASP markers, 13 Korean japonica varieties showed close genetic relationships and were divided into three groups. More KASP markers are being developed and these markers will be utilized in gene mapping, quantitative trait locus (QTL) analysis, marker-assisted selection and other strategies relevant to crop improvement.