• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.667-669
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• 2003

A bio-optical model was developed specific for turbid and shallow waters. Special studies were carried out to estimate absorption and scattering properties as well as backscattering probability of suspended matter. The inversion of bio-optical model allows for direct retrieval of turbidity and chlorophyll- a from the visible-near infrared (VNIR) range sensor. Time-series satellite imagery from ASTER AM-1 sensor, were used to monitor the Laguna de Bay water quality condition. Spatial distribution of temperature for the lake was extracted from the thermal infrared (TIR) sensor. Corresponding field surveys were conducted to parameterize the bio -optical model. In-situ measurements include suspended particle and chlorophyll-a concentrations profiles from nephelometric devices and processing of water samples. Hyperspectral measurements were used to validate results of the bio -optical model and satellite- based estimation. This study provides a theoretical basis and a practical illustration of applying space- based measurements on an operational basis.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.338-338
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• 2010

공정 중 발생하는 입자는 반도체 생산 수율에 가장 큰 영향을 끼치는 원인으로 파악되며, 생산 수율을 저하시키는 원인 중 70% 가량이 이와 관련된 것으로 알려져 있다. 현재 반도체 공정에서 입자를 계측하는데 사용하는 PWP (Particle per Wafer Pass) 방법은 표준 측정방법으로 널리 쓰이고 있으나, 실시간으로 입자의 양을 측정할 수 없고, Test wafer 사용에 따른 비용증가의 단점이 있어 공정 중에 입자를 실시간으로 측정할 수 있는 대안기술이 필요한 실정이다. ISPM (In-Situ Particle Monitoring)은 레이저 산란방식을 이용한 실시간 입자측정 장비로서 오염원 발생에 대한 즉각적인 대처와 조치가 가능하고 부가적인 추가 비용이 발생하지 않기 때문에 실시간 모니터링 장비가 없는 현재의 반도체 공정에 충분히 적용될 가능성이 있다. 특히 CVD 공정은 반도체 공정의 약 30%를 차지할 만큼 중요한 단계로 생성되는 오염입자 모니터링을 통해 공정 불량 유무를 판단할 수 있을 것으로 기대된다. 본 연구에서는 Silane 가스를 이용한 PECVD (Plasma Enhanced Chemical Vapor Deposition) 공정 중 발생되는 오염입자를 ISPM을 이용하여 실시간으로 측정하였다. 챔버 배기구에 두 가지 타입의 ISPM을 설치하고 공정압력, 유량, 플라즈마 파워를 공정변수로 하여 각각의 조건에서 발생되는 오염입자의 분포 변화를 실시간으로 측정하였으며 결과를 비교 분석하였다.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Composites Research
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• v.18 no.4
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• pp.1-7
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• 2005

In-situ structural health monitoring of filament wound pressure tanks were conducted during water-pressurizing test using embedded fiber Bragg grating (FBG) sensors. We need to monitor inner strains during working in order to verify the health condition of pressure tanks more accurately because finite element analyses on filament wound pressure tanks usually show large differences between inner and outer strains. Fiber optic sensors, especially FBG sensors can be easily embedded into the composite structures contrary to conventional electric strain gages (ESGs). In addition, many FBG sensors can be multiplexed in single optical fiber using wavelength division multiplexing (WDM) techniques. We fabricated a standard testing and evaluation bottle (STEB) with embedded FBG sensors and performed a water-pressurizing test. In order to increase the survivability of embedded FBG sensors, we suggested a revised fabrication process for embedding FBG sensors into a filament wound pressure tank, which includes a new protecting technique of sensor heads, the grating parts. From the experimental results, it was demonstrated that FBG sensors can be successfully adapted to filament wound pressure tanks for their structural health monitoring by embedding.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.51-60
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• 2019

The major purpose of this study is to construct an in-situ soil moisture verification network employing Frequency Domain Reflectometry (FDR) sensors for Cosmic-ray soil moisture observation system operation as well as long-term field-scale soil moisture monitoring. The test bed of Cosmic-ray and FDR verification network system was established at the Sulma Catchment, in connection with the existing instrumentations for integrated data provision of various hydrologic variables. This test bed includes one Cosmic-ray Neutron Probe (CRNP) and ten FDR stations with four different measurement depths (10 cm, 20 cm, 30 cm, and 40 cm) at each station, and has been operating since July 2018. Furthermore, to assess the reliability of the in-situ verification network, the volumetric water content data measured by FDR sensors were compared to those calculated through the core sampling method. The evaluation results of FDR sensors- measured soil moisture against sampling method during the study period indicated a reasonable agreement, with average values of $bias=-0.03m^3/m^3$ and RMSE $0.03m^3/m^3$, revealing that this FDR network is adequate to provide long-term reliable field-scale soil moisture monitoring at Sulmacheon basin. In addition, soil moisture time series observed at all FDR stations during the study period generally respond well to the rainfall events; and at some locations, the characteristics of rainfall water intercepted by canopy were also identified. The Temporal Stability Analysis (TSA) was performed for all FDR stations located within the CRNP footprint at each measurement depth to determine the representative locations for field-average soil moisture at different soil profiles of the verification network. The TSA results showed that superior performances were obtained at FDR 5 for 10 cm depth, FDR 8 for 20 cm depth, FDR2 for 30 cm depth, and FDR1 for 40 cm depth, respectively; demonstrating that those aforementioned stations can be regarded as temporal stable locations to represent field mean soil moisture measurements at their corresponding measurement depths. Although the limit on study duration has been presented, the analysis results of this study can provide useful knowledge on soil moisture variability and stability at the test bed, as well as supporting the utilization of the Cosmic-ray observation system for long-term field-scale soil moisture monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.42-42
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• 2022

지금까지도 유역에서의 녹조 모니터링은 현장채수를 통한 점 단위 모니터링에 크게 의존하고 있어 기후, 유속, 수온조건 등에 따라 수체에 광범위하게 발생하는 녹조를 효율적으로 모니터링하고 대응하기에는 어려운 점들이 있어왔다. 또한, 그동안 제한된 관측 데이터로 인해 현장 측정된 실측 데이터 보다는 녹조와 관련이 높은 NDVI, FGAI, SEI 등의 파생적인 지수를 산정하여 원격탐사자료와 매핑하는 방식의 분석연구 등이 선행되었다. 본 연구는 녹조의 모니터링시 정확도와 효율성을 향상을 목표로 하여, 우선은 녹조 측정장비를 활용, 7000개 이상의 녹조 관측 데이터를 확보하였으며, 이를 바탕으로 동기간의 고해상도 위성 자료와 실측자료를 매핑하기 위해 다양한Machine Learning기법을 적용함으로써 그 효과성을 검토하고자 하였다. 연구대상지는 낙동강 내성천 상류에 위치한 영주댐 유역으로서 데이터 수집단계에서는 면단위 현장(in-situ) 관측을 위해 2020년 2~9월까지 4회에 걸쳐 7291개의 녹조를 측정하고, 동일 시간 및 공간의 Sentinel-2자료 중 Band 1~12까지 총 13개(Band 8은 8과 8A로 2개)의 분광특성자료를 추출하였다. 다음으로 Machine Learning 분석기법의 적용을 위해 algae_monitoring Python library를 구축하였다. 개발된 library는 1) Training Set과 Test Set의 구분을 위한 Data 준비단계, 2) Random Forest, Gradient Boosting Regression, XGBoosting 알고리즘 중 선택하여 적용할 수 있는 모델적용단계, 3) 모델적용결과를 확인하는 Performance test단계(R², MSE, MAE, RMSE, NSE, KGE 등), 4) 모델결과의 Visualization단계, 5) 선정된 모델을 활용 위성자료를 녹조값으로 변환하는 적용단계로 구분하여 영주댐뿐만 아니라 다양한 유역에 범용적으로 적용할 수 있도록 구성하였다. 본 연구의 사례에서는 Sentinel-2위성의 12개 밴드, 기상자료(대기온도, 구름비율) 총 14개자료를 활용하여 Machine Learning기법 중 Random Forest를 적용하였을 경우에, 전반적으로 가장 높은 적합도를 나타내었으며, 적용결과 Test Set을 기준으로 NSE(Nash Sutcliffe Efficiency)가 0.96(Training Set의 경우에는 0.99) 수준의 성능을 나타내어, 광역적인 위성자료와 충분히 확보된 현장실측 자료간의 데이터 학습을 통해서 조류 모니터링 분석의 효율성이 획기적으로 증대될 수 있음을 확인하였다.

• Clinical and Experimental Pediatrics
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• v.56 no.6
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• pp.247-253
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• 2013

Purpose: In children with acute leukemia, bone marrow genetic abnormalities (GA) have prognostic significance, and may be the basis for minimal residual disease monitoring. Since April 2007, we have used a multiplex reverse transcriptase-polymerase chain reaction tool (HemaVision) to detect of GA. Methods: In this study, we reviewed the results of HemaVision screening in 270 children with acute leukemia, newly diagnosed at The Catholic University of Korea from April 2007 to December 2011, and compared the results with those of fluorescence in situ hybridization (FISH), and G-band karyotyping. Results: Among the 270 children (153 males, 117 females), 187 acute lymphoblastic leukemia and 74 acute myeloid leukemia patients were identified. Overall, GA was detected in 230 patients (85.2%). HemaVision, FISH, and G-band karyotyping identified GA in 125 (46.3%), 126 (46.7%), and 215 patients (79.6%), respectively. TEL-AML1 (20.9%, 39/187) and AML1-ETO (27%, 20/74) were the most common GA in ALL and AML, respectively. Overall sensitivity of HemaVision was 98.4%, with false-negative results in 2 instances: 1 each for TEL-AML1 and MLL-AF4. An aggregate of diseases-specific FISH showed 100% sensitivity in detection of GA covered by HemaVision for actual probes utilized. G-band karyotype revealed GA other than those covered by HemaVison screening in 133 patients (49.3%). Except for hyperdiplody and hypodiploidy, recurrent GA as defined by the World Health Organizationthat were not screened by HemaVision, were absent in the karyotype. Conclusion: HemaVision, supported by an aggregate of FISH tests for important translocations, may allow for accurate diagnosis of GA in Korean children with acute leukemia.

• The Journal of the Petrological Society of Korea
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• v.18 no.4
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• pp.279-291
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• 2009

Radon is a natural radionuclide originated from radioactive decay of radium in rocks and soil. It is colorless, odorless and tasteless elements that mainly distributed as gaseous phase in soil pore space. The present study analyzed the characteristics of long-term radon variation in granitic residual soil at Mt. Guemjeong in Guemjeong-gu, Busan and determined the effects of atmospheric temperature, rainfall and soil temperature and moisture. Periodic measurements of radon concentrations in soil gas were conducted by applying two types of in-situ monitoring methods (chamber system and tubing system). Radon concentration in soil gas was highest in summer and lowest in winter. The variations in soil temperature and atmospheric temperature were most effective factors in the long-term radon variations and showed positive co-relations. The air circulation between soil air and atmosphere by the temperature difference between soil and atmosphere was analyzed a major cause of the variation. However, other factors such as atmospheric pressure, rainfall and soil moisture were analyzed relatively less effective.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.495-506
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• 2018

Global interest in climate change and sea level rise has led to active research on the velocities of glaciers. In studies about the velocity of glaciers, in-situ measurements can obtain the most accurate data but have limitations to acquire periodical or long-term data. Offset tracking using SAR is actively being used as an alternative of in-situ measurements. Offset tracking has a limitation in that the accuracy of observation is lower than that of other observational techniques, but it has been improved by recent studies. Recent studies in the $Uv{\hat{e}}rsbreen$ glacier area have shown that glacier altitudes decrease at a rate of 1.5 m/year. The glacier displacement velocities in this region are heavily influenced by climate change and can be important in monitoring and forecasting long-term climate change. However, there are few concrete examples of research in this area. In this study, we applied the improved offset tracking method to observe the two-dimensional velocity in the $Uv{\hat{e}}rsbreen$ glacier. As a result, it was confirmed that the glacier moved at a maximum rate of 133.7 m/year. The measruement precisions for azimuth and line-of-sight directions were 5.4 and 3.3 m/year respectively. These results will be utilized to study long-term changes in elevation of glaciers and to study environmental impacts due to climate change.