• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.423-434
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• 2009

The Arctic environment is sensitive to change of sea-ice distribution. The increase and decrease of sea ice work to an index of globe warming progress. In order to predict the progress of hereafter earth global warming, continuous monitoring regarding a change of the sea ice area in the Arctic should be performed. The remote sensing based on an artificial satellite is most effective on the North Pole. The sea ice observation using a passive microwave sensor has been continued from 1970's. The determination of sea ice extent and ice type is one of the great successes of the passive microwave imagers. In this paper, to investigate the seasonal and inter-annual variation of sea-ice distribution we used here the sea ice data from July 2002 to May 2009 around the Arctic within $60^{\circ}N$ for the AMSR-E 12.5km sea-ice concentration, a passive microwave sensor. From an early analysis of these data, the arctic sea-ice extent has been steadily decreasing at a rate of about 3.1%, accounting for about $2{\times}10^5\;km^2$, which was calculated for the sea-ice cover reaching its minimum extent at the end of each summer. It is also revealed that this trend corresponds to a decline in the multi-year ice that is affected mainly by summer sea surface and air temperature increases. The extent of younger and thinner (first-year) ice decreased to the 2007 minimum, but rapidly recovered in 2008 and 2009 due to the dramatic loss in 2007. Seasonal variations of the sea-ice extent show significant year-to-year variation in the seasons of January-March in the Barents and Labrador seas and August-October in the region from the East Siberian and Chukchi seas to the North Pole. The spatial distribution of multi-year ice (7-year old) indicates that the perennial ice fraction has rapidly shrunk recently out of the East Siberian, Laptev, and Kara seas to the high region of the Arctic within the last seven years and the Northeast Passage could become open year-round in near future.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.144-152
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• 1997

본 논문에서는 1년생 빙맥에 의하여 해양구조물에 작용하는 하중을 추정할 수 있는 모델을 제시하였다. 1년생 빙맥을 수면하부(keel), 수면상부(sail), 경화층(consolidated layer)의 3부분으로 나누어 각 부분에 의한 하중을 추정할 수 있는 방법을 논의하였다. 수면하부는 얼음조각(ice rubble)이 층으로 쌓여져 형성된 것이므로 수면하부에 의한 하중추정을 할 때 얼음조각을 선형 Mohr-Coulomb재료로 생각하여 토질역학(soil mechanics)의 이론을 사용하였다. 수면상부에 의한 하중도 토질역학 이론을 이용하여 추정하였으며 경화층에 의한 하중은 Korzhavin식을 이용하여 추정하였다. 제시한 모델을 이용하여 빙맥하중 추정에 미치는 인자들의 영향을 검토하였다.

• Korean Journal of Remote Sensing
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• v.3 no.2
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• pp.89-102
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• 1987

Microwave remote sensing plays a major role in areas where cloud cover and darkness prevail. In this and the next paper, models are described for the radar backscatter from two major types of sea ice in an attempt to specify optimum sensor parameters and to allow the most reliable image interpretation possible. Here, the physical-optics model using an exponential correlation function is shown to be able to presict the signatures of first-year ice under cold conditions. The effect of volume scattering by small inclusions in the first-year ics is shown to be negligible using a semi-empirical volume scattering model.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Proceedings of the KSRS Conference
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• v.2
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• pp.892-895
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• 2006

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors are examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water and water vapor and surface wind on surface emissivity on the microwave signatures. A plane parallel radiative transfer model is used to compute brightness temperatures at SSM/I frequencies over surfaces that contain open water, first-year (FY) ice and multi-year (MY) ice and their combinations. Synthetic retrievals in this study use the NASA Team (NT) algorithm for the estimation of sea ice concentrations. This study shows that if the satellite sensor’s field of view is filled with only FY ice the retrieval is not much affected by the atmospheric conditions due to the high contrast between emission signals from FY ice surface and the signals from the atmosphere. Pure MY ice concentration is generally underestimated due to the low MY ice surface emissivity that results in the enhancement of emission signals from the atmospheric parameters. Simulation results in marginal ice areas also show that the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentration. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. In particular, our results suggest that strong surface wind is more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.1-9
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• 2013

To classify the landfast ice in the north of the Greenland, observation data, multifrequency Synthetic Aperture Radar (SAR) images and texture images were used. The total four types of sea ice are first year ice, highly deformed ice, ridge and moderately deformed ice. The texture images that were processed by K-means algorithm showed higher accuracy than the ones that were processed by SAR images; however, overall accuracy of maximum likelihood algorithm using texture images did not show the highest accuracy all the time. It turned out that when using K-means algorithm, the accuracy of the multi SAR images were higher than the single SAR image. When using the maximum likelihood algorithm, the results of single and multi SAR images are differ from each other, therefore, maximum likelihood algorithm method should be used properly.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.153-156
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• 2006

북극과 남극의 해빙을 촬영한 Kompsat-1 EOC 영상을 SSM/I Sea Ice Concentration(SIC)과 비교하였다. EOC 영상은 2005년 $7{\sim}8$월 북극 해빙지역의 가장자리를 지나는 10개 궤도(624 영상)와 $9{\sim}11$월 남극대륙의 가장자리를 지나는 11개 궤도(676 영상)에서 얻어졌다. 그 중 구름의 영향이 없는 약 12%의 영상으로부터 감독분류와 육안분류를 통해 Multi-year ice와 First-year ice(M+F), Young ice(Y), New ice(N)로 해빙의 유형을 구분하여 SIC를 계산하였으며, 이를 NASA Team Algorithm(NTA)으로 계산된 SSM/I SIC와 비교하였다. 북극의 여름철에는 해빙의 시공간적 변화가 매우 크기 때문에 EOC SIC(M+F+Y+N)와 SSM/I SIC의 상관계수는 0.671로 잘 일치하지 않았다. 남극의 봄철에 N을 제외한 EOC SIC(M+F+Y)의 경우 SSM/I SIC와 0.873의 높은 상관계수를 가졌다. 이로부터 NTA로 계산된 남극의 SSM/I SIC가 M과 F를 비롯하여 Y도 포함하는 것을 알 수 있었다.

• Korean Journal of Childcare and Education
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• v.17 no.1
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• pp.41-63
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• 2021

Objective: The purpose of this research was to examine the effects of an 'Ice' project, a topic chosen based on children's interests to discover the improvement of scientific and mathematical abilities, and creativity of four-year-old children. Methods: For this research, 34 four-year-old children from M childcare center were selected. Seventeen children were placed in the experimental group and the remaining 17 children were placed in the comparison group. After the project was completed, to observe the differences between the two groups, the Mann-Whitney U test was conducted. Results: First, the 'Ice' project had an effect on improving children's scientific abilities and its subfactors. Second, the 'Ice' project hadsignificant effects on improving children's algebraic and geometric mathematical skills. Third, excluding the resistance to premature closure among the subfactors of creativity, the 'Ice' project contributed to improve children's creativity and all sub-factors. Conclusion/Implications: The 'Ice' project activities, a subject chosen from the interests of children, led active play participation from children and brought positive effects in immersion of play and activity. Such effects proved to affect children's scientific abilities, mathematical abilities, and creativity, and suggest this research can be used as base line data in follow-up research on various project activities.

• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.55-61
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• 2011

A field trial in an ice-covered sea is one of the most important tasks in the design of icebreaking ships and offshore structures. To correctly estimate the ice load and ice resistance of a ship's hull, it is essential to understand the material properties of sea ice during ice field trials and to use the proper experimental procedure for gathering effective ice data. The first Korean-made icebreaking research vessel, "ARAON," had her second sea ice trial in the Arctic Ocean during the summer season of 2010. This paper describes the test procedures used to obtain proper sea ice data, which provides the basic information for the ship's performance in an ice-covered sea and is used to estimate the correct ice load and ice resistance of the IBRV ARAON. The data gathered from the sea ice in the Chukchi Sea and Beaufort Sea during the Arctic voyage of the ARAON includes the temperature, density, and salinity of the sea ice, which was believed to be from two-year old ice floes. This paper analyses the gathered sea ice data in comparison with data from the first voyage of the ARAON during her Antarctic Sea ice trial.

• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1263-1268
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• 2018

Three different ice core samples from Antarctica were analyzed to identify activity concentrations of radioactive isotopes. Tracking migration of radioactive isotopes to Antarctica can provide a key clue to understand global environmental changes caused by radiation exposures because the Antarctic ice cores can preserve unique characteristics of various environmental conditions. We are particularly interested in the $^{137}Cs$ nucleus, because it is closely related to radiation exposure from nuclear power plant accidents and nuclear bomb tests. With its half life of $30.17{\pm}0.03$ years, $^{137}Cs$ can also be used to assess the age of sedimentation occurring after around the year 1945. We selected three ice core samples, called Tarn8, Styx27, and H25, from different time periods; the Tarn8 sample is known to be from earlier than ~ 1000 AD, the Styx27 sample is approximately from the year 1945, and the H25 sample is from the year 2012. Radioactive isotope measurements of the ice core samples were performed using a 100% HPGe detector at Cheongpyeong Underground Radiation Laboratory (CURL). We measured the activity of $^{137}Cs$ in the H25 sample to be $0.98{\pm}0.82mBq/kg$. Although the activity has a large uncertainty mainly due to the limited sample quantity, the $^{137}Cs$ isotopes in the Antarctic ice core were measured for the first time in Korea.