• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.167-167
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• 2022

최근 빅데이터에 관련된 연구에 있어 데이터의 품질관리에 대한 논의가 꾸준히 이뤄져 오고 있다. 특히 이미지 처리 및 분석에 활용되어온 딥러닝 기술의 경우, 분류 작업 및 패턴인식 등으로부터 데이터의 특징을 추출함으로써 비지도학습(Unsupervised Learning)을 가능하게 한다는 장점이 있음에도 불구하고 빅데이터를 다루는 과정에 있어 용량, 다양성, 속도 및 신뢰성 측면에서의 한계가 있었다. 본 연구에서는 CCTV 영상을 활용한 강수량 산정 모델 개발에 있어 예측 정확도 향상 및 성능 개선을 도모할 수 있는 데이터 전처리 방법을 제안하였다. 서울 근린 AWS 4개소 지역(김포장기, 하남덕풍, 강동, 성남) 및 중앙대학교 지점 내 CCTV를 설치한 후, 최대 9개월의 영상을 확보하여 강수량 산정을 위한 딥러닝 모델을 개발하였다. 배경분리, 조도조정, 영역설정, 데이터증진, 이상데이터 분류 등이 가능한 알고리즘을 개발함으로써 데이터셋 자체에 대한 전처리 작업을 수행한 후, 이에 대한 결과를 기존 관측자료와 비교·분석하였다. 본 연구에서 제안한 전처리 방법들을 적용한 결과, 강수량 산정 모델의 예측 정확도를 평가하는 지표로 선정한 평균 제곱근 편차(Root Mean Square Error; RMSE)가 약 30% 감소함을 확인하였다. 본 연구의 결과로부터 CCTV 영상 데이터를 활용한 강수량 산정의 가능성을 확인할 수 있었으며 특히, 딥러닝 모델 개발시 필요한 적정 전처리 방법들에 대한 기준을 제시할 수 있을 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.659-671
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• 2009

Fuzzy c-means clustering technique is applied to improve the accuracy of G/R ratio used for rainfall estimation by radar reflectivity. G/R ratio is computed by the ground rainfall records at AWS(Automatic Weather System) sites to the radar estimated rainfall from the reflectivity of Kwangduck Mt. radar station with 100km effective range. G/R ratio is calculated by two methods: the first one uses a single G/R ratio for the entire effective range and the other two different G/R ratio for two regions that is formed by clustering analysis, and absolute relative error and root mean squared error are employed for evaluating the accuracy of radar rainfall estimation from two G/R ratios. As a result, the radar rainfall estimated by two different G/R ratio from clustering analysis is more accurate than that by a single G/R ratio for the entire range.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.33-41
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• 2004

Accuracy of registration between images acquired from various medical image modalities is one of the critical issues in radiation treatment planing. In this study, a method of accuracy evaluation of image registration using a homemade brain phantom was investigated. Chamfer matching of CT-MR and CT-SPECT imaging was applied for the multimodal image registration. The accuracy of image correlation was evaluated by comparing the center points of the inserted targets of the phantom. The three dimensional root-mean-square translation deviations of the CT-MR and CT-SPECT registration were 2.1${\pm}$0.8 mm and 2.8${\pm}$1.4 mm, respectively. The rotational errors were ＜ 2$^{\circ}$ for the three orthogonal axes. These errors were within a reasonable margin compared with the previous phantom studies. A visual inspection of the superimposed CT-MR and CT- SPECT images also showed good matching results.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.135-147
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• 2023

Satellite-observed significant wave heights (SWHs), which are widely used to understand the response of the ocean to climate change, require long-term and continuous validation. This study examines the accuracy and error characteristics of SWH observed by nine satellite altimeters in the North Pacific and North Atlantic Ocean for 25 years (1992-2016). A total of 137,929 matchups were generated to compare altimeter-observed SWH and in-situ measurements. The altimeter SWH showed a bias of 0.03 m and a root mean square error (RMSE) of 0.27 m, indicating relatively high accuracy in the North Pacific and North Atlantic Ocean. However, the spatial distribution of altimeter SWH errors showed notable differences. To better understand the error characteristics of altimeter-observed SWH, errors were analyzed with respect to in-situ SWH, time, latitude, and distance from the coast. Overestimation of SWH was observed in most satellite altimeters when in-situ SWH was low, while underestimation was observed when in-situ SWH was high. The errors of altimeter-observed SWH varied seasonally, with an increase during winter and a decrease during summer, and the variability of errors increased at higher latitudes. The RMSEs showed high accuracy of less than 0.3 m in the open ocean more than 100 km from the coast, while errors significantly increased to more than 0.5 m in coastal regions less than 15 km. These findings underscore the need for caution when analyzing the spatio-temporal variability of SWH in the global and regional oceans using satellite altimeter data.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.1
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• pp.1-19
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• 2018

In order to compare significant wave height (SWH) data from multi-satellites (GFO, Jason-1, Envisat, Jason-2, Cryosat-2, SARAL) and SWH measurements from Ieodo Ocean Research Station (IORS), we constructed a 12 year matchup database between satellite and IORS measurements from December 2004 to May 2016. The satellite SWH showed a root mean square error (RMSE) of about 0.34 m and a positive bias of 0.17 m with respect to the IORS wave height. The satellite data and IORS wave height data did not show any specific seasonal variations or interannual variability, which confirmed the consistency of satellite data. The effect of the wind field on the difference of the SWH data between satellite and IORS was investigated. As a result, a similar result was observed in which a positive biases of about 0.17 m occurred on all satellites. In order to understand the effects of topography and the influence of the construction structures of IORS on the SWH differences, we investigated the directional dependency of differences of wave height, however, no statistically significant characteristics of the differences were revealed. As a result of analyzing the characteristics of the error as a function of the distance between the satellite and the IORS, the biases are almost constant about 0.14 m regardless of the distance. By contrast, the amplitude of the SWH differences, the maximum value minus the minimum value at a given distance range, was found to increase linearly as the distance was increased. On the other hand, as a result of the accuracy evaluation of the satellite SWH from the Donghae marine meteorological buoy of Korea Meteorological Administration, the satellite SWH presented a relatively small RMSE of about 0.27 m and no specific characteristics of bias such as the validation results at IORS. In this paper, we propose a conversion formula to correct the significant wave data of IORS with the satellite SWH data. In addition, this study emphasizes that the reliability of data should be prioritized to be extensively utilized and presents specific methods and strategies in order to upgrade the IORS as an international world-wide marine observation site.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.14 no.4
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• pp.195-204
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• 2009

In order to estimate sea surface current fields in the East Sea, we examined characteristics of mean dynamic topography (MDT) fields (or mean surface current field, MSC) generated from three different methods. This preliminary investigation evaluates the accuracy of surface currents estimated from satellite-derived sea level anomaly (SLA) data and three MDT fields in the East Sea. AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) provides a MDT field derived from satellite observation and numerical models with $0.25^{\circ}$ horizontal resolution. Steric height field relative to 500 dbar from temperature and salinity profiles in the East Sea supplies another MDT field. Trajectory data of surface drifters (ARGOS) in the East Sea for 14 years provide another MSC field. Absolute dynamic topography (ADT) field is calculated by adding SLA to each MDT. Application of geostrophic equation to three different ADT fields yields three surface geostrophic current fields. Comparisons were made between the estimated surface currents from the three different methods and in-situ current measurements from a ship-mounted ADCP (Acoustic Doppler Current Profiler) in the southwestern East Sea in 2005. For offshore areas more than 50 km away from the land, the correlation coefficients (R) between the estimated versus the measured currents range from 0.58 to 0.73, with 17.1 to $21.7\;cm\;s^{-1}$ root mean square deviation (RMSD). For coastal ocean within 50 km from the land, however, R ranges from 0.06 to 0.46 and RMSD ranges from 15.5 to $28.0\;cm\;s^{-1}$. Results from this study reveal that a new approach in producing MDT and SLA is required to improve the accuracy of surface current estimations for the shallow costal zones of the East Sea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.63-71
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• 2019

The two-fluid model proposed by Herman and Prigogine is useful for analyzing macroscopic traffic flow in a network. The two-fluid model is used for analyzing a network through the relationship between the ratio of stopped vehicles and the average moving speed of the network, and the two-fluid model has also been applied in the urban transportation network where many signalized or unsignalized intersections existed. In general, the average travel speed and moving speed of a network decrease, and the ratio of stopped vehicles and low speed vehicles in network increase as the traffic demand increases. This study proposed the two-fluid model considering congested and uncongested traffic situations. The critical velocity and the weight factor for congested situation are calibrated by minimizing the root mean square error (RMSE). The critical speed of the Seoul network was about 34 kph, and the weight factor of the congestion on the network was about 0.61. In the proposed model, $R^2$ increased from 0.78 to 0.99 when compared to the existing model, suggesting that the proposed model can be applied in evaluating network performances or traffic signal operations.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.47-56
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• 1999

In this study, tie point observation in aerial triangulation was automated by the image processing methods. The technique includes boundary extraction and We matching processes. The procedures were applied to extract points of Interest and to find their conjugate points in the other images. The image coordinates of the identified points were then used to compute their absolute coordinates. An algorithm was developed in this study for the automation of observation in aerial triangulation, which is a manual process of selecting a tie point and recording the image coordinate of the selected point. The developed algorithm automates this process through the application of a mathematical operator to extract points of interest from an arbitrary image. The root m square error of image coordinates of the developed algorithm is $6.8{\mu}m$, which is close to that of the present analytical method. In a manual environment, the accuracy of the result of a photogrammetric process is heavily dependant on the level of skill and experience of the human operator. No such problem exists in an automated system. Also, as a result of the automated system, the time spent in the observation process could be reduced by a factor of 61.2%, thereby reducing the overall cost.

• Journal of Korean Society of Steel Construction
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• v.23 no.5
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• pp.629-636
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• 2011

This paper describes the smart structural system that uses smart materials for real-time monitoring and active control of bolted joints in steel structures. The impedance-based structural health monitoring (SHM) techniques, which utilize the electro-mechanical coupling property of piezoelectric materials, was used to detect loose bolts in bolted joints. By monitoring the measured electrical impedance and comparing it with the measured baseline, a bolt loosening damage was detected. The damage was evaluated quantitatively using the damage metrics in conductance signature with respect to the healthy states. When loosening damage was detected in the bolted joint, the external heater actuated the shape memory alloy (SMA) washer. Then the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. An experiment was conducted by integrating the piezoelectric-material-based SHM function and the SMA-based active control function on a bolted joint, after which the performance of thesmart self-healing joint system was investigated.