• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.69-76
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• 2008

Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.

• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.227-233
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• 2018

This study presents the generation and accuracy assessment of predicted orbital ephemeris based on satellite laser ranging (SLR) for geostationary Earth orbit (GEO) satellites. Two GEO satellites are considered: GEO-Korea Multi-Purpose Satellite (KOMPSAT)-2B (GK-2B) for simulational validation and Compass-G1 for real-world quality assessment. SLR-based orbit determination (OD) is proactively performed to generate orbital ephemeris. The length and the gap of the predicted orbital ephemeris were set by considering the consolidated prediction format (CPF). The resultant predicted ephemeris of GK-2B is directly compared with a pre-specified true orbit to show 17.461 m and 23.978 m, in 3D root-mean-square (RMS) position error and maximum position error for one day, respectively. The predicted ephemeris of Compass-G1 is overlapped with the Global Navigation Satellite System (GNSS) final orbit from the GeoForschungsZentrum (GFZ) analysis center (AC) to yield 36.760 m in 3D RMS position differences. It is also compared with the CPF orbit from the International Laser Ranging Service (ILRS) to present 109.888 m in 3D RMS position differences. These results imply that SLR-based orbital ephemeris can be an alternative candidate for improving the accuracy of commonly used radar-based orbital ephemeris for GEO satellites.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.2
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• pp.59-68
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• 2019

This paper proposed a method to estimate the vertical delay from the slant delay, which can improve accuracy of the ionospheric correction of SBAS. Proposed method used Chapman profile which is a model for the vertical electron density distribution of the ionosphere. In the proposed method, we assumed that parameters of Chapman profile are given and the vertical ionospheric can be modeled with linear function. We also divided ionosphere into multi-layer. For the verification, we converted slant ionospheric delays to vertical ionospheric delays by using the proposed method and generated the ionospheric correction of SBAS with vertical delays. We used International Reference Ionosphere (IRI) model for the simulation to verification. As a result, the accuracy of ionospheric correction from proposed method has been improved for 17.3% in daytime, 10.2% in evening, 2.1% in nighttime, compared with correction from thin shell model. Finally, we verified the method in the SBAS user domain, by comparing slant ionospheric delays of users. Using the proposed method, root mean square value of slant delay error decreased for 23.6% and max error value decreased for 27.2%.

• Geosystem Engineering
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• v.21 no.5
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• pp.273-281
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• 2018

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value ($R^2$) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.347-356
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• 2020

In this study, Inclined Geosynchronous Orbit (IGSO) and Geostationary Orbit (GEO) of BeiDou System (BDS) and Quasi Zenith Satellite System (QZSS) satellites positions and clock errors calculated by broadcast ephemeris and compared with Multi-GNSS Experiment (MGEX) products provided by five Analysis Centers (ACs). Root Mean Square Errors (RMSE) calculated for satellite position error. The IGSO results showed that 1.82 m, 0.91 m, 1.28 m in BDS and 1.34 m 0.36 m 0.49 m in QZSS and the GEO results showed that 2.85 m, 6.34 m, 6.42 m in BDS and 0.47 m, 4.79 m, 5.82 m in QZSS in the direction of radial, along-track and cross-track respectively. RMS calculated for satellite clock error. The IGSO result showed that 2.08 ns and 1.24 ns and the GEO result showed that 1.28 ns and 1.12 ns in BDS and QZSS respectively.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Journal of Environmental Impact Assessment
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• v.14 no.4
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Atmosphere
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• v.32 no.3
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• pp.179-189
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• 2022

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.