• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Parasites, Hosts and Diseases
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• 제48권3호
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• pp.219-224
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• 2010

This study has been conducted to estimate the occurrence of Cryptosporidium oocysts in water supplies in the Metropolitan area of Seoul, South Korea, for 10 years from 2000 to 2009. Water samples were collected quarterly at 6 intakes in the Han River and its largest stream and 6 conventional Water Treatment Plants (WTPs) serving drinking water for 10 million people of Seoul. Cryptosporidium oocysts were found in 22.5% of intake water samples and arithmetic mean was 0.65 oocysts/10 L (range 0-22 oocysts/10 L). Although the annual mean of oocyst number was as low as 0.04-1.90 oocysts/10 L, 3 peaks in 2004 and 2007 were observed and the pollution level was a little higher in winter. The lowest density was observed at Paldang intake and the pollution level increased at Kuui and Jayang intakes. At the end of the largest stream, oocysts were found in 70% of collected samples (mean 5.71 oocysts/10 L) and it seemed that its joining the Han River resulted in the increase at Kuui intake and downstream. Oocyst removal by physical process exceeded 2.0-2.3 log and then all finished water samples collected at 6 WTPs were negative for Cryptosporidium in each 100 L sample for 10 years. These results suggested that domestic wastewater from the urban region could be a source of Cryptosporidium pollution and separating sewage systems adjacent to the intakes could be meaningful for some intakes having weakness related to parasitological water quality.

• 한국해안·해양공학회논문집
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• 제29권6호
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• pp.410-418
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• 2017

저마루구조물의 안정성 및 배후 호안의 월파량은 배후수역의 수위에 영향을 받음에도 단면 수리모형실험 수행 시 수위상승량이 알려지지 않아 계측 결과가 왜곡될 수 있다. 본 연구에서는 저마루 구조물 제체 배후면의 평균수위 상승량을 예측하기 위해 단면수리모형실험과 해수유동수치모형실험을 수위-유량 관계식으로 결합하는 결합기법을 시도하였다. 이 기법을 사용함으로써 평균수위 상승량과 유속장을 단면수리모형실험과 동시간대에 얻을 수 있어 이러한 문제점들을 상당한 수준으로 해소할 수 있게 되었다. 또한 구조물의 개구부를 통한 유출유속의 강도에 관해서도 개략적인 정보를 얻을 수 있어 저마루구조물의 적정 평면배치안 선정에도 도움이 될 수 있다.

• 한국해양공학회지
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• 제16권2호
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• pp.6-9
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• 2002

The long-term monthly variations of tide with tidal harmonic analysis in Pusan Harbour are investigated. The present spring tidal range decreased 1.4 cm and the variation of phase lag increased than 1974. The high and low water level of yearly mean sea level is show during the February to March and August to September, respectively. It is important to note that the larger lunar elliptic N2 is large in comparison with lunisolar diurnal K1 and principal lunar diurnal O1. The ratios (Correction Factors) of monthly mean sea level and the main 4 tidal constituents are evaluated to correct the shortly (monthly) observed tide for the design of harbour facilities.

• 한국해안·해양공학회논문집
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• 제29권6호
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• pp.335-349
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• 2017

수중에 설치되는 잠제는 해안침식을 방어하기 위한 대표적인 연안구조물로 지금까지 다양한 형태의 잠제가 제안 연구되어 왔다. 이와 같은 잠제는 천단에서의 급격한 수심변화에 의해 구조물 주변에서 복잡한 파동장을 형성한다. 본 연구는 3차원투과성잠제를 대상으로 잠제 주변에서 형성되는 파고분포 및 평균수위분포를 수치적으로 검토하였다. 수치해석에는 오픈소스 CFD 소스코드인 OLAFOAM을 적용하였으며, 투과성직립벽 및 불투과성 잠제에 대한 기존의 실험결과와의 비교를 통해 수치해석모델의 적용성을 검증하였다. 이를 바탕으로 설상사주의 형성조건에 있는 투과성잠제 주변에서 형성되는 파고분포 및 평균수위분포를 검토하였다. 수치해석결과, 잠제 사이의 개구부 폭이 감소할수록 개구부 중앙에서는 파고가 증가하지만 개구부 배후에서는 개구폭이 증가할수록 파고가 증가하며, 연안으로부터의 잠제 설치위치는 파고의 변화에 크게 영향을 미치지 않음을 확인하였다. 또한, 잠제의 개구부 폭이 감소함에 따라 잠제 개구부의 제두부 근방에서 평균수위 하강이 증가함을 확인하였다.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• 한국안전학회지
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• 제35권2호
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• pp.28-33
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• 2020

Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.

• 한국수자원학회논문집
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• 제55권11호
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• pp.903-911
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• 2022

물을 공급하기 위한 자원 중 하나인 지하수는 다양한 자연적 요인에 의해 수위의 변동이 발생한다. 최근, 인공신경망을 이용하여 지하수위의 변동을 예측하는 연구가 진행되었다. 기존에는 인공신경망 연산자 중 학습에 영향을 미치는 Optimizer로 경사하강법(Gradient Descent, GD) 기반 Optimizer를 사용하였다. GD 기반 Optimizer는 초기 상관관계 의존성과 해의 비교 및 저장 구조 부재의 단점이 존재한다. 본 연구는 GD 기반 Optimizer의 단점을 개선하기 위해 GD와 화음탐색법(Harmony Search, HS)를 결합한 새로운 Optimizer인 Gradient Descent combined with Harmony Search(GDHS)를 개발하였다. GDHS의 성능을 평가하기 위해 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용하여 이천율현 관측소의 지하수위를 학습 및 예측하였다. GD 및 GDHS를 사용한 MLP의 성능을 비교하기 위해 Mean Squared Error(MSE) 및 Mean Absolute Error(MAE)를 사용하였다. 학습결과를 비교하면, GDHS는 GD보다 MSE의 최대값, 최소값, 평균값 및 표준편차가 작았다. 예측결과를 비교하면, GDHS는 GD보다 모든 평가지표에서 오차가 작은 것으로 평가되었다.

• 운동영양학회지
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• 제23권4호
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• pp.1-5
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• 2019

[Purpose] The goal of this study was to review data on physical activity level (PAL), a crucial index for determining estimated energy requirement (EER), calculated as total energy expenditure (TEE, assessed with doubly labeled water [DLW]) divided by resting metabolic rate (RMR, PAL = TEE/RMR) in female athletes and to understand the methods of assessing athletes' EERs in the field. [Methods] For the PAL data review among female athletes, we conducted a PubMed search of the available literature related to the DLW method. DLW studies measuring TEE and RMR were included for the present review. [Results] Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes' EERs that can be used in the field. [Conclusion] Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes' EERs that can be used in the field.