• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.6 no.3
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• pp.152-163
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• 2001

Based upon the sedimentological, geochemical and micropaleontological analyses of two sediment cores from the Antarctic Peninsula (AP), three distinct lithological units can be recognized: (1) ice-proximal an/or ice-distal diamictons in the lower part of the cores, accumulated just seaward of the grounding line of the ice shlef until 11,000 yrs BP; (2) diatomaceous mud between 6,000 and 2,500 yrs BP in the middle part, resulted from a large influx of organic materials by enhanced production of open marine condition; (3) diatomaceous sandy mud since 2,500 yrs BP, characterized by an increase in sand content and decrease in TOC and diatom abundance in the lower layers, which reflects the formation of more extensive and seasonally persistent sea ice. Based on the C-14 radiocarbon dating, the sub-ice shlef deposition of the diamicton on the AP western shelf completed around 11,000 yrs BP. Colder condition was reinstated between 12,800 and 11,600 BP with a dropin TOC content and diatom abundance, which is coincident with the Younger Dryas event in the North Atlanticregion. At this time, the ice shelf, that is now absent in the study area, appears to advance as evidenced by an abrupt increase in sea-ice taxa. A climatic optimum is recognized between 9,000 and 2,500 BP, coincide witha mid-Holocene climatic optimum 'Hypsithermal Warm Period' from the other Antarctic sites. During this time, diatomaceous mud accumulated by a large influx of organic materials by enhanced production occurred in openmarine condition. Around 2,500 BP, diatomaceous sandy mud reflects the formation of more extensive and seasonally persistent sea ice, coincident with the onset of the Neoglacial in the Antarctic. Our results provide evidence of climatic change from the Antarctic Peninsula`s western shelf that helps in determining the existence and timing of Holocene milennial-scale climatic events in the Southern Hemisphere.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.314-317
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• 2009

고해상도 위성자료를 이용하여 갯벌의 다양한 퇴적환경을 공간자료화하거나 표층 생물상 분포를 파악하기 위한 연구는 여러 연구자들에 의해 수행되어졌다. 그러나 갯벌 속에 존재하는 저서생물에 대한 양을 추정하는 연구는 미미한 실정이다. 따라서 본 연구에서는 태안 근소만에 대하여 위성자료를 이용하여 갯벌의 DEM과 퇴적상 등의 분포를 공간자료화하고 이들로부터 생물상과 관련이 있는 노출시간 등의 2차 주제도를 제작하였다. 또한, 현장조사에 의해 얻어진 갯벌 생물 종/개체수와 갯벌의 퇴적환경 공간자료를 비교분석하여 갯벌 저서생물의 공간적 연관성을 파악하였다. 노출시간은 4개의 등급으로 구분하였으며 퇴적상 현장조사와 연계하여 우점종을 분석한 결과, 노출시간이 가장 긴 노출시간 I 등급 지역은 두토막눈썹참갯지렁이와, 칠게와 가재붙이가 대표종으로 분석되었다. II 지역의 모래 우세지역에는 쏙이 그리고 펄 우세지역에는 세가시육질꼬리옆새우가 우점종으로 나타났다. III과 IV 지역은 조하대쪽으로 모래 성분이 전체적으로 우세하게 나타났는데 펄털콩게와 바지락이 우정하고 있는 것으로 분석되었다.

• Economic and Environmental Geology
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• v.38 no.6 s.175
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• pp.707-716
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• 2005

Organic geochemical analyses including Rock-Eval pyrolysis, elemental analysis and stable carbon isotope analysis were performed to evaluate the characteristics of organic matter in the ODP Leg 127 Site 794A sediments and to understand paleoceanographic changes. Based on the TOC contents, C/N ratio, HI vs. OI, $\delta^{13}C_{org}$ and C/S ratio, results imply that dark layers containing a large amount of terrigenous organic matter were deposited under the suboxic/anoxic conditions, whereas the light layers containing largely marine organic matter were deposited under the oxic conditions. These results indicate that increasing surface-productivity by the input of a large amount of terrigenous organic matter from adjacent continent led to the deposition of dark layers during the interglacial highstands, whereas marine primary production and dilution caused by Kosa from the China desert area led to the deposition of light layers with the decreased to terrigenous organic matter during the glacial lowstands.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Ocean and Polar Research
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• v.21 no.2
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• pp.127-136
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• 1999

Radiolarian assemblages from KODOS area were analyzed in order to understand the biostratigraphy and paleoceanography of deep-sea sediment from the Northeast Equatorial Pacific. The sediment core was divided into two or three units on the basis of the chemical and physical properties. In the upper sediment, mixtures of Quaternary and Tertiary radiolarians are found indicating active reworking processes. Dissolution of radiolarians seem to increase with depth. Radiolarians are seldom in Unit III presumably due to dissolution and corrosion. The middle part of unit I appears to correspond to Collosphaera invaginata Zone (0.21 Ma). Unit II belongs to Collosphaera tuberosa Zone. Based on the absence of Stylatractus universus, we estimate its age to be younger than 0.42 Ma. Based on our analyses of radiolarians in Unit I and II, we estimated the age of unit III as Tertiary, particularly from Oligocene to Miocene. There may to be hiatuses of more than 3 My from late Miocene to Pliocene, which probably resulted from erosion and dissolution by the Antarctic Bottom Water Sedimentation rates during Quaternary range from 0.15 to 0.50 mm/ky with significant variabilities among stations. Radiolarians in the study area were mostly warm-water species.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.85-87
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• 2022

최근 CCTV 출입 기록, 휴대폰 보안, 스마트 매장 등에서 얼굴 인식을 통해 개인을 식별하는 기술이 널리 사용되고 있다. 카메라의 각도, 조명, 사람의 움직임 등 얼굴 인식에 많은 외부 환경이 영향을 미치고 있지만 그중에서도 실제 영상에서 얼굴이 차지하는 영역이 작아 저해상도 얼굴 인식에 어려움을 겪고 있다. 이러한 문제점을 해결하고자 본 논문에서는 이미지 해상도가 얼굴 인식에 끼치는 영향을 알아보고 이미지 초해상화를 통해 얼굴 인식 성능을 개선하고자 한다. 쌍선형, 양3차 회선 보간법과 딥러닝 기반의 이미지 초해상화 모델인 RCAN을 이용하여 업스케일링한 데이터셋에 대해 학습한 ArcFace를 통해 얼굴 검증 평가를 진행하였다. 고해상도 이미지는 얼굴 인식 성능을 향상시키며, RCAN을 사용한 이미지 초해상화가 보간법을 사용한 방법보다 더 좋은 성능을 보였다.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.677-687
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• 2021

In this study, we analyzed diatoms from core RS15-GC41 collected in Iselin Bank, Ross Sea. A total of 24 genera and 35 species of diatoms are identified, and the having valve abundance of diatoms varies from 0.2 to 28.6×10⁶/g. Four diatom assemblage zones are established by the vertical distribution of diatoms, and changed with a cycle of 100 kyrs. RS15-GC41 were deposited over the last 400 kyrs (corresponding to Marine Isotope Stages 1-11). The open-water species Fragilariopsis kerguelensis, Rhizosolenia styliformis, and Thalassionema nitzschioides abundantly occurred in interglacial periods. Whereas, Actinocyclus actinochilus abundantly dominant during the glacial periods. The distribution of these diatoms indicated, it can be seen that the sea-ice extent was larger and lasted longer during MIS 7, 9, and 11 than that of MIS 1, 3, and 5. Moreover, Paralia sulcata was abundantly predominant in MIS 7, 9, and 11; this finding suggests likely indicating that P. sulcata was transported from the coastal/inner shelf area to the study site, during accumulated in the sediments, reworked with the influx of ice-rafted debris by the currents

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.137-147
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• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.27-27
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• 2018

최근 국지적인 집중 호우에 따른 홍수 피해와 도로에 홍수가 발생하고 있다. 또한, 기존의 도로위 강우관측 방식은 인근 강우관측소에서 관측된 강우량을 활용하며 지상 관측소 또는 AWS기상관측소의 관측 네트워크와 근접한 거리에서 강우량 편차가 크고 원하는 위치에서의 강우량과 다르며 인근관측소와의 거리가 멀어질수록 강우량의 정확도는 감소하게 된다. 국지적인 집중호우로 인한 도로위의 홍수에 따른 피해를 줄이기 위해서는 현재 운영 중인 관측망 외에 보다 상세화된 위치에서 강우량을 관측하고 이에 따라 실시간으로 강우정보를 수집하는 것이 필요하다. 따라서, 원하는 위치에서의 보다 정확한 강우량 값을 관측하기 위해서는 고해상도의 강우 관측망을 형성할 필요가 있다. 차량용 강우센서는 관측시 차량을 사용하기 때문에 고밀도 강우 관측 망을 형성하기 용이하다. 하지만 기존 강우량계와 달리 차량용 강우센서는 빛의 양을 이용하여 강우량을 변환시켜 측정되기 때문에 정확한 강우보정기술의 개발하는 것이 필수적이다. 본 연구에서는 차량용 강우센서를 활용하여 정확도 높은 강우량 관측을 위한 관계식을 개발했습니다. 관계식은 실내실제 관측되는 차량용 강우센서 정보 값에 적용하여 강우량을 생산하고 실제 강우관측 값과 비교 검실험을 통해 도출한 후 강우 관측장비 인근에서 실제 주행실험을 통해 강우관측소에서 관측된 강우량 값과 비교 및 검증을 수행하였습니다. 차량용 강우센서 정보 수집을 위해 데이터 스키마를 표준화하여 실시간으로 수집체계를 구축하였고 이는 보다 여러 위치에 있는 많은 차량에서 재해 관리를 위해 도로기상정보를 수집하고 활용할 수 있을 것입니다.