• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1223-1232
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• 2017

This paper presents an approach to detect changes caused by urban development over a large area using KOMPSAT optical images. In order to minimize the radiometric dissimilarities between the images acquired at different times, we apply the grid-based rough radiometric correction as a preprocessing to detect changes in a large area. To improve the accuracy of the change detection results for urban development, we mask-out non-interest areas such as water and forest regions by the use of land-cover map provided by the Ministry of Environment. The Change Vector Analysis(CVA) technique is applied to detect changes caused by urban development. To confirm the effectiveness of the proposed approach, a total of three study sites from Sejong City is constructed by combining KOMPSAT-2 images acquired on May 2007 and May 2016 and a KOMPSAT-3 image acquired on March 2014. As a result of the change detection accuracy evaluation for the study site generated from the KOMPSAT-2 image acquired on May 2007 and the KOMPSAT-3 image acquired on March 2014, the overall accuracy of change detection was about 91.00%. It is demonstrated that the proposed method is able to effectively detect urban development changes in a large area.

• Journal of Korean Society of Forest Science
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• v.80 no.3
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• pp.265-278
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• 1991

The purpose of this study is to suggest objective basic data for the design and management of the national park roadside slopes through the quantitative analysis of the visual quality included in the physical environment of the Chi-ri national park, for this, visual volumes of physical elements have been evaluated by using the mesh analysis, spatial images structure of physical elements have been analyzed by factor analysis algorithm, and degree of visual quality have been measured mainly by questionnaires. Result of this thesis can be summarized as fallows. Visual volumes of the naked, rock, ground cover of seed spray, and artificial planting are found to be the main factor determining the visual quality. Factors covering the spatial image of the national park roadside slopes landscape have been found to be the overall synthetic evaluation, spatial, appeal, physical, openness and dignity factors such as the overall the spatial, physical and openness yield high factor scores. As for the factors determining the degree of visual quality of the roadside slopes, variables such as the summit, the constructions management, harmony of landscape, visual stability of roadside slopes, suitable artificial planting and suitable constructions.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.123-130
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• 2009

Motion estimation (ME) plays an important role in digital video compression. But it limits the performance of image quality and encoding speed and is computational demanding part of the encoder. To reduce computational time and maintain the image quality, integer pel and fractional pel ME methods are proposed in this paper. The proposed method for integer pel ME uses a hierarchical search strategy. This strategy method consists of symmetrical cross-X pattern, multi square grid pattern, diamond patterns. These search patterns places search points symmetrically and evenly that can cover the overall search area not to fall into the local minimum and to reduce the computational time. The proposed method for fractional pel uses full search pattern, center biased fractional pel search pattern and the proposed search pattern. According to block sizes, the proposed method for fractional pel decides the search pattern adaptively. Experiment results show that the speedup improvement of the proposed method over Unsymmetrical cross Multi Hexagon grid Search (UMHexagonS) and Full Search (FS) can be up to around $1.2{\sim}5.2$ times faster. Compared to image quality of FS, the proposed method shows an average PSNR drop of 0.01 dB while showing an average PSNR gain of 0.02 dB in comparison to that of UMHexagonS.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.899-909
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• 2021

In Musculoskeletal ultrasound examination, a linear transducer (12-5 MHz) with a contact surface length of 6 cm, which is commonly distributed in medical institutions, does not contact with bent fingers and wrist depressions only by using a conventional liquid ultrasound gel, which causes image loss. In order to solve this image loss, the use of commercially available Water gel pad is increasing. However, it is a disposable medical product. Repeated use is prohibited and expensive. In this study, in order to reduce the economic burden caused by the use of the commercialized Water gel Pad, we searched for water-soluble food thickener powder that can be easily purchased around our lives and manufactured Gel pads. Among them, kappa-Carrageenan Gel Pad, iota-Carrageenan Gel Pad, and Agar-Agar Gel Pad satisfied the evaluation criteria without artifacts in image evaluation using the N-365 Multipurpose Phantom. Also, the price to purchase the food thickener powder was about 38 times cheaper than the existing commercialized Water gel pad. As a result, if you use the food thickener powder such as kappa-Carrageenan, iota-Carrageenan and Agar-Agar to make solidified elastic Gel pad without a cover instead of the commercially available expensive disposable Water gel pad, the convenience and usefulness of use are improved. It is judged that the manufactured Gel pads can contribute well to the role of efficient ultrasound propagation medium.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.419-431
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• 2009

The core of this study is finding out the efficient band selection or extraction method discovering the optimal spectral bands when applying canonical correlation classifier (CCC) to hyperspectral data. The optimal efficient bands grounded on each separability decision technique are selected using Multispec$^{(C)}$ software developed by Purdue university of USA. Total 6 separability decision techniques are used, which are Divergence, Transformed Divergence, Bhattacharyya, Mean Bhattacharyya, Covariance Bhattacharyya, Noncovariance Bhattacharyya. For feature extraction, PCA transformation and MNF transformation are accomplished by ERDAS Imagine and ENVI software. For the comparison and assessment on the effect of feature selection and feature extraction, land cover classification is performed by CCC. The overall accuracy of CCC using the firstly selected 60 bands is 71.8%, the highest classification accuracy acquired by CCC is 79.0% as the case that executes CCC after appling Noncovariance Bhattacharyya. In conclusion, as a matter of fact, only Noncovariance Bhattacharyya separability decision method was valuable as feature selection algorithm for hyperspectral image classification depended on CCC. The lassification accuracy using other feature selection and extraction algorithms except Divergence rather declined in CCC.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1334-1344
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• 2020

Objective: Compressed sensing (CS) has gained wide interest since it accelerates MRI acquisition. We aimed to compare the 3D post-contrast T1-weighted volumetric isotropic turbo spin echo acquisition (VISTA) with CS (VISTA-CS) and without CS (VISTA-nonCS) in intracranial vessel wall MRIs (VW-MRI). Materials and Methods: From April 2017 to July 2018, 72 patients who underwent VW-MRI, including both VISTA-CS and VISTA-nonCS, were retrospectively enrolled. Wall and lumen volumes, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured from normal and lesion sites. Two neuroradiologists independently evaluated overall image quality and degree of normal and lesion wall delineation with a four-point scale (scores ≥ 3 defined as acceptable). Results: Scan coverage was increased in VISTA-CS to cover both anterior and posterior circulations with a slightly shorter scan time compared to VISTA-nonCS (approximately 7 minutes vs. 8 minutes). Wall and lumen volumes were not significantly different with VISTA-CS or VISTA-nonCS (interclass correlation coefficient = 0.964-0.997). SNR was or trended towards significantly higher values in VISTA-CS than in VISTA-nonCS. At normal sites, CNR was not significantly different between two sequences (p = 0.907), whereas VISTA-CS provided lower CNR in lesion sites compared with VISTA-nonCS (p = 0.003). Subjective wall delineation was superior with VISTA-nonCS than with VISTA-CS (p = 0.019), although overall image quality did not differ (p = 0.297). The proportions of images with acceptable quality were not significantly different between VISTA-CS (83.3-97.8%) and VISTA-nonCS (75-100%). Conclusion: CS may be useful for intracranial VW-MRI as it allows for larger scan coverage with slightly shorter scan time without compromising image quality.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.6
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• pp.439-446
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• 2011

In this research, enhanced land-cover classification methods using high-resolution satellite image (HRSI) and GIS in terms of practicality and accuracy was proposed. It aims for understanding non-point pollutant origin/loading, assessment the efficiency of rainfall storage/infiltration facilities and sounds water-environment management. The result of applying enhanced land-cover classification methods to the urban region verifies that roof and road area are including various vegetations such as roof garden, flower bed in the median strip and street tree. This accounts for 3% of total study area, and more importantly it was counted as impervious area by GIS alone or conventional indoor work. The feasibility of the method was assessed by applying to rainfall-runoff analysis for three weak rainfall in the range of 7.1-10.5 mm events in 2000, Chiba, Japan. A good agreement between simulated and observed runoff hydrograph was obtained. In comparison, the hydrograph simulated with land-use parameters by the detailed land-use information of 10m grid had an error between 31%~71%, while enhanced method showed 4% to 29%, and showed the improvement particularly for reproducing observed peak and recession flow rate of hydrograph in weak rainfall condition.

• Corrosion Science and Technology
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• v.12 no.1
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• pp.1-6
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• 2013

Recently Digital TV industry has drastically been moving the illuminating system, which causes an obvious product change from PDP and LCD to LED model to provide high-definition image. Due to strong competition in the digital industry, TV manufacturers make a great efforts to reduce production cost by using low-priced materials such as steels instead of aluminum and plastic etc. In this paper we have developed a new low-priced electrogalvanized steel sheet, which has a black resin composite layer, to substitute conventional high-priced PCM steel and plastic mold for rear cover panel in the digital TV. The black resin composite was prepared by mechanical dispersion of the mixture solution that consists of high solid polyester resin, melamine hardener, black pigment, micronized silica paste, polyacrylate texturing particle and miscellaneous additives. The composite solution was coated on the steel sheet using roll coater followed by induction furnace curing and cooling. Although the coated layer has a half thickness compared to the conventional PCM steels having $23{\mu}m$ thickness, it exhibits excellent quality for the usage of rear cover panel. The new steel sheet was applied to test products to get quality certification from worldwide electronic appliance customers. Detailed discussion provides in this paper including preparation of composite solution, roll coating technology, induction curing technology and quality evaluation from customers.