• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.376-381
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• 1998

In this study, we was measured the radiance reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the multi-purpose satellite(KOMPSAT) to use the data in analyzing water pollution. Also we investigated the possibility of extraction of water quality factors in rivers and water body by using high resolution remote sensing data such as Airborne MSS. Especially, we tried to extract the environmental factors related with eutrophication, and also tried to develop the process technique and the radiance feature of reflectance related with eutrophication. The results were summarized as follows: First, the spectrum of sun's rays which reaches the surface of the earth was consistent with visible rays bands of 0.4${\mu}{\textrm}{m}$~0.7${\mu}{\textrm}{m}$ and about 50% of total quantity of radiation were there. And at around 0.5${\mu}{\textrm}{m}$ of green spectral band in visible rays bands, the spectrum was highest. Second, as a result of the radiance reflectance Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and suspended sediments and turbidity represented high spectral reflectance at 0.8${\mu}{\textrm}{m}$ and at 0.57${\mu}{\textrm}{m}$ each. Third, as a result of the water quality analysis by using Airborne MSS, Chlorophyll-a could have a distribution chart when carried out ratio of B3 and BS to B7. And Band 7 was useful for making the distribution chart of suspended sediments. And when we carried out PCA, suspended sediments and turbidity had distributions at PC 1 , PC 4 each similarly to ground truth data. Above results can be changed according to the change of season and time. Therefore, in order to analyze more exactly the environmental factors of water quality by using LRC data, we need to investigate constantly the ground truth data and the radiance feature of reflectance of water body. Afterward in this study, we will constantly analyze the radiance feature of the surface of water in water body by measuring the on-the-spot radiance reflectance and using low resolution satellite image(SeaWiFs). Besides, we will gather the data of water quality analysis in water body and analyze the pattern of water pollution.

• Korean Journal of Ecology and Environment
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• v.40 no.3
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• pp.419-430
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• 2007

Physiochemical factors, phytoplankton biomass (Chl ${\alpha}$) and primary productivity were investigated seasonally in the three lakes of Dongbok, Juam, and Yeongsan during April 2004${\sim}$March 2006. Microphytoplankton dominated (>60%) in Dongbok lake, and phytoplankton biomass was high in the upper area, especially during April 2004, whereas they were high in the lower area during June 2004. In Juam lake, the high phytoplankton biomass in April 2004 was contributed by nanophytoplankton. In Yeongsan lake, chlorophyll a was high in August with high contribution of nanophytoplankton. Primary production was highest in Dongbok lake, and then followed by Yeongsan and Juam lakes. Regression analysis in Dongbok take showed that Chl ${\alpha}$ and primary production had close relations with secchi depth. In Juam lake, phosphate were correlated with the Chl ${\alpha}$, while temperature and TN was correlated with primary production in the lower area. In Yeongsan lake, Chl ${\alpha}$ have positively correlation with TN/TP. Primary production in the upper have high relationship with secchi depth, however, in the lower have high relationship with turbidity. Linear regression analysis showed that nutrients of nitrogen and phosphorus should be reduced for the protections in Juam and Dongbok lakes. We suggested that suspended solids and phytoplankton growth related to turbidity are needed to manage in Yeongsan lake.

• Korean Journal of Food Science and Technology
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• v.49 no.1
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• pp.35-43
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• 2017

The sensory characteristics of 12 commercial top-fermented beers were determined by sensory descriptive analysis. Beer samples were also analyzed for soluble solids, titratable acidity, pH, reducing sugar content, bitterness unit (BU), turbidity, hunter color values, amino acid content, total phenolic content, and DPPH radical scavenging activity. Five appearance, nine aroma, six flavor/taste, and four mouth-feel related sensory attributes were evaluated by a panel of nine judges. As the result of three way analysis of variance of descriptive data, all sensory attributes except "cereal" aroma and "salty" taste showed significant differences among the beers (p<0.05). Based on the principal component analysis of the descriptive data, samples were primarily separated by first and second principal components, which accounted for 78% of the total variance between the beers with high intensities of "yellow color", "caramel aroma", and "barley taste" versus "hop aroma", "sour", and "citrus aroma". In the correlation analysis between the sensory terms and physicochemical parameters, BU, total phenolic content, titratable acidity, soluble solids, and yellowness ($b^*$) showed significant positive correlations with citrus aroma, pineapple aroma, and fresh aroma characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.482-482
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• 2022

하천의 합류부에서는 수질이 다른 유체가 혼합하여 합류 전과 다른 특성을 보인다. 하천의 합류부에서 수질을 효율적으로 관리하기 위해서는 수질의 공간적인 혼합 특성을 규명하는 것이 중요하다. 합류부에서 수질의 공간적인 혼합 특성을 분석하기 위해 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기 조직화 지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하였다. 세 가지 기법을 비교하여 어떤 알고리즘이 합류부의 수질 변화 특성을 더 뚜렷하게 나타내는지 분석하였다. 수질 변화 비교 인자들은 pH, chlorophyll, DO, Turbidity 등이 있고, 수질 인자들은 YSI를 활용해 측정하였다. 자료의 측정 지역은 낙동강과 황강이 합류하는 지역이며, 보트에 YSI 장비를 부착하고 횡단하여 측정하였다. 측정한 데이터를 R 프로그램을 통해 세 가지 기법을 적용시켜 수질 변화 비교를 분석한다. 토폴로지 데이터 분석(topological data analysis, TDA)은 거대하고 복잡한 데이터로부터 유의미한 정보를 추출하는 데 사용하고, 자기조직화지도(Self-Organizing Map, SOM) 기법은 차원 축소와 군집화를 동시에 수행한다. k-평균 알고리즘(K-means clustering algorithm) 기법은 주어진 데이터를 k개의 클러스터로 묶는 머신러닝 비지도학습에 속하는 알고리즘이다. 세 가지 방법들의 주목적은 클러스터링이다. 클러스터 분석(Cluster analysis)이란 주어진 데이터들의 특성을 고려해 동일한 성격을 가진 여러 개의 그룹으로 대상을 분류하는 데이터 마이닝의 한 방법이다. 군집화 방법들인 TDA, SOM, K-means를 이용해 합류 지역의 수질 특성들을 클러스터링하여 수질 패턴들을 분석해 하천 수질 오염을 방지할 수 있을 것이다. 본 연구에서는 토폴로지 데이터 분석(topological data analysis, TDA), 자기조직화지도(Self-Organizing Map, SOM), k-평균 알고리즘(K-means clustering algorithm) 세 가지 기법을 이용하여 합류부에서의 수질 특성을 비교하며 어떤 기법이 합류의 특성을 더욱 뚜렷하게 나타내는지 규명했다. 합류의 특성을 군집화 방법을 이용해 알게 된다면, 합류부의 수질 변화 패턴을 다른 합류 지역에서도 적용할 수 있을 것으로 기대된다.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.5
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• pp.919-926
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• 2000

The experiment in this study was conducted as a part of an effort to evaluate filter performance with pilot-filters consisting of one mono-media and two dual-media columns. Particle distribution analysis using a particle counter is more sensitive and better than turbidity analysis in observing particle detachments and a breakthrough. In sand media filters having 1.5 m of available head, caution is needed in the head loss of the late stages of filtration, and for dual-media filters, appropriate media configuration and effluent Quality monitoring should be used for preventing the final breakthrough. Also the time of particle breakthrough in the dual media filter can be deferred by increasing bed depth, and it is necessary to use a filtration aid prior to filtration to prevent breakthrough of these intermediate sized particles in high filtration rate.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.741-749
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• 2008

Many small-scale ponds that serve as ecological habitat, recreation and irrigation are faced to eutrophication problem, which causes aesthetic nuisance and ultimately loss of their functions. Thus accurate evaluation of the trophic state of these ponds is essential to provide rational information to the stakeholders so that they can develop effective management actions. In this study, the trophic state of a small pond (Wonheung) that experiencing water quality degradation due to vicinity land development was assessed using various Trophic State Indexes (TSIs) and statistical analysis including Principal Components Analysis (PCA) based on the field monitoring data obtained from May to December, 2007. The results showed that the pond is under eutrophic state with average total nitrogen (T-N) and total phosphorus (T-P) concentrations of $708.1{\mu}g/L$ and $59.3{\mu}g/L$, respectively. The factor loading plot obtained from PCA showed distinct two influencing factors, PC 1 and PC 2. PC 1 was grouped by T-P, Chlorophyll a (Chl-a), suspended solids (SS), TN/TP ratio, and transparency that all strongly related to the eutrophication state, while PC 2 by temperature, conductivity, dissolved oxygen (DO) and turbidity that explains the seasonal water quality variations. The limiting factor was identified as light rather than phosphorus by both T-N/T-P ratio and TSI indexes analysis. The results and methodology adopted in this study can be used for water quality assessment for other small ponds and lakes.

• Korean Journal of Food Science and Technology
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• v.32 no.5
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• pp.1073-1078
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• 2000

Wet noodles were prepared with all purposed flour and whey powder, and effects of added whey powder on dough rheology and noodle quality were examined using texture profile analysis, sensory evaluation and colorimeter. The initial pasting temperature in amylograph and the maximum resistance in extensograph increased with the addition of whey powder, while the water absorption and the dough development time in farinograph and the extensibility in extensograph decreased. The weight and volume of cooked noodles decreased and turbidity of soup increased with the addition of whey powder. Sensory evaluation revealed that the texture, taste and overall acceptabillity of cooked noodle from 5% whey powder were significantly different from the others. Texture profile analysis of cooked noodles showed decrease of hardness, cohesiveness, chewiness and springiness with the increase of whey powder. L and a values of wet noodles decreased with the addition of whey powder and b value increased.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Journal of Environmental Health Sciences
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• v.7 no.1
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• pp.21-31
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• 1981

This survey was carried out to investigate the temperature, pH value, nitrogen (ammonia, nitrite, nitrate), turbidity, color, chloride ion, $KMnO_4$ consumed, and hardness as chemical analysis of the tap water in Seoul city area during the period from September to Octobor, 1979, and to observe the differences among the values by the distance from the water purification plant and by the district supplied tap water from-the each water purification plant. The results obtained were as follows: 1) An average of the water temperature was $19.8\pm 0.2\circ$C. 2) An average of pH was $7.18\pm 0.02$. The difference among each district was statistically significant (p<0.01), but it was not observed among each distance. 3) An average of turbidity was $1.25\pm 0.12$ ppm. The difference among each district was highly significant (p<0.01), respectively, but not among each distance. 4) An average of color was $1.43\pm 0.16$ ppm, and there were statistically significant differences by the distance and by the district (p<0.01). 5) An average of ammonia nitrogen was $0.022\pm 0.005$ ppm. The differences among each distance, and district were statistically significant (P<0.01). 6) An average of nitrite nitrogen was $0.0050\pm 0.0013$ ppm, and the difference among each distance was highly significant (p<0.01), respectively, and each district showed statistical significance (p<0.01). 7) An average of nitrate nitrogen was $0.82\pm 0.08$ ppm. The difference among each district was significant (p<0.05), and each distance showed high significance (p<0.01). 8) An average of $KMnO_4$ consumed was $3.73\pm 0.16$ ppm, and the difference among each district was significant (p<0.05), but it was not observed among each distance. 9) An average of chloride ion was $8.56\pm 0.28$ ppm, and the difference among each district was higly significant (p<0.01), respectively, but it was not observed among each distance. 10) An average of hardness was $40.69\pm 1.17$ ppm, and there was statistically significant difference by each district (P<0.01), but not by distance. 11) The interrelation between temperature and pH of the tap water revealed the negative correlation from the coefficient of it as showed r=-0.6073 and p<0.01. 12) Except water temperature, there were negative correlationships between pH and other water qualities. 13) Correlation coefficients of $KMnO_4$ comsumed and ammonia nitrogen, nitrite nitrogen were statistically significant but that of $KMnO_4$ consumed and nitrate nitrogen showed no statistical correlationship. 14) Ammonia nitrogen seems to have high correlationship with nitrite nitrogen(r= +0.6669), but not with nitrate nitrogen. 15) Nitrate nitrogen seems to have statistically significant correlationship with nitrite nitrogen (r=+0.4959), but not with ammonia nitrogen. 16) The interrelation between chloride ion and hardness of the tap water revealed positive correlation from the coefficient of it as showed as r=+0.4888 and p<0.01.