• Journal of the Korean Geotechnical Society
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• v.18 no.2
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• pp.161-170
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• 2002

This paper describes a systematic way of simultaneously identifying the ambient pore pressure and the rigidity index $(=G/s_u)$ of soil by applying an optimization technique to the early part of piezocone dissipation test result. An analytical solution developed by Randolph & Wroth(1979) was implemented in normalized from to express the build-up and dissipation of excess pore pressures around a piezocone as a function of the rigidity index. An ambient pore pressure and optimal rigidity index were determined by minimizing the differences between theoretical and measured excess pore pressure curves using optimization technique. The effectiveness of the proposed back-analysis method was examined against the well-documented performance of piezocone dissipation tests(Tanaka & Sakagami, 1989), from the viewpoints of proper determination of selected target parameters and saving of test duration. It is shown that the proposed back-analysis method can evaluate properly the ambient pore pressure and the rigidity index by using only the early phase of the dissipation test data. Also, it is shown that the proposed back-analysis method permits the horizontal coefficient of consolidation to be identified rationally. Consideration for strain level of back-analyzed rigidity index shows that it corresponds to at least intermediate to large strain level.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.87-99
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• 2006

The Median Resolution Sensors (MRSs) for land observation such as Landsat-ETM+ and SPOT-HRV are more effective than Ocean Color Sensors (OCSs) for studying of detailed ecological and biogeochemical components of the coastal waters. In this study, we developed suspended sediment algorithm for Landsat TM/ETM+ by considering the spectral response curve of each band. To estimate suspended sediment concentration (SS) from satellite image data, there are two difference types of algorithms, that are derived for enhancing the accuracy of SS from Landsat imagery. Both empirical and remote sensing reflectance model (hereafter referred to as $R_{rs}$ model) are used here. This study tried to compare two algorithm, and verified using in situ SS data. It was found that the empirical SS algorithm using band 2 produced the best result. $R_{rs}$ model-based SS algorithm estimated higher values than empirical SS algorithm. In this study we used $R_{rs}$ model developed by Ahn (2000) focused on the Mediterranean coastal area. That's owing to the difference of oceanic characteristics between Mediterranean and Korean coastal area. In the future we will improve that $R_{rs}$ model for the Korean coastal area, then the result will be advanced.

• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.17-27
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• 1995

One of the world widely used methods in determining the intensity of a typhoon is Dvorak's technique. By applying the Dvorak's method to the typhoons which affected our country in various degress and extents without regard to their individual severity, we estimated their intensity for six different cases of typhoons. We have derived a regression equation of estimating the central pressures and maximum wind speeds for the six selected typhoons. Their intensity was estimated from the Dvork's method using GMS satellite image data. The derived equation has tested to typhoon ORCHID and the computed values have been compared with the direct observations in its central pressure and maximum wind speed. The computed values in the Dvork's method are smaller in their magnitudes than the observed corresponding values. But their relative magnitudes do not change so much at each different time step. But our results are significantly different from those of NOAA and JMA. The cause of differences are not investigated in depth in this analysis.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1555-1565
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• 2018

Detection of subtle ground deformation of volcanoes plays an important role in evaluating the risk and possibility of volcanic eruptions. Ground-fixed observation equipment is difficult to maintain and cost-inefficient. In contrast, satellite remote sensing can regularly monitor at low cost. In this paper, following the study of Chadwick et al. (2006), which applied the interferometric SAR (InSAR) technique to the Sierra Negra volcano, Galapagos. In order to investigate the deformation of the volcano before 2005 eruption, the recent activities of this volcano were analyzed using Sentinel-1, the latest SAR satellite. We obtained the descending mode Sentinel-1A SAR data from January 2017 to January 2018, applied the Persistent Scatter InSAR, and estimated the depth and expansion quantity of magma in recent years through the Mogi model. As a result, it was confirmed that the activity pattern of volcano prior to the eruption in June 2018 was similar to the pattern before the eruption in 2005 and was successful in estimating the depth and expansion amount. The results of this study suggest that satellite SAR can characterize the activity patterns of volcano and can be possibly used for early monitoring of volcanic eruption.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• 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.

• Journal of Astronomy and Space Sciences
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• v.17 no.1
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• pp.53-66
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• 2000

We developed low light CCD imaging system using thermoelectric cooling method collaboration with a company to design a commercial model. It consists of Kodak KAF-0401E(768$\times$512 pixels) CCD chip, thermoelectric module manufactured by Thermotek. This TEC system can reach an operative temperature of $-25^{\circ}C$. We employed an Uniblitz VS25s shutter and it has capability a minimum exposure time 80ms. The system components are an interface card using a Korea Astronomy Observatory (hereafter KAO) ISA bus controller, image acquisition with AD9816 chip, that is 12bit video processor. The performance test with this imaging system showed good operation within the initial specification of our design. It shows a dark current less than 0.4e-/pixel/sec at a temperature of $-10^{\circ}C$, a linearity 99.9$\pm$0.1%, gain 4.24e-/adu, and system noise is 25.3e-(rms). For low temperature CCD operation, we designed a TEC, which uses a one-stage peltier module and forced air heat exchanger. This TEC imaging system enables accurate photometry($\pm$0.01mag) even though the CCD is not at 'conventional' cryogenic temperatures(140k). The system can be a useful instrument for any other imaging applications. Finally, with this system, we obtained several images of astronomical objects for system performance tests.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.305-314
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• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Korean Journal of Optics and Photonics
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• v.23 no.4
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• pp.167-171
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• 2012

This paper reports on the stray light analysis results of a compact imaging spectrometer (COMIS) for a microsatellite STSAT-3. COMIS images Earth's surface and atmosphere with ground sampling distances of 27 m at the 18~62 spectral bands (0.4 ~ 1.05 ${\mu}m$) for the nadir looking at an altitude of 700 km. COMIS has an imaging telescope and an imaging spectrometer box into which three electronics PCBs are embedded. The telescope images a $27m{\times}28km$ area of Earth surface onto a slit of dimensions $11.8{\mu}m{\times}12.1mm$. This corresponds to a ground sampling distance of 27 m and a swath width of 28 km for nadir looking posture at an altitude of 700 km. Then the optics relays and disperses the slit image onto the detector thereby producing a monochrome image of the entrance slit formed on each row of detector elements. The spectrum of each point in the row is imaged along a detector column. The optical mounts and housing structures are designed in order to prevent stray light from arriving onto the image and so deteriorating the signal to noise ratio (SNR). The stray light analysis, performed by a non-sequential ray tracing software (LightTools) with three dimensional housing and lens modeling, confirms that the ghost and stray light arriving at the detector plane has the relative intensity of ${\sim}10^{-5}$ and furthermore it locates outside the concerned image size i.e. the field of view of the optics.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.