• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.382-390
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• 2021

Synthetic aperture sonars as well as side scan sonars or multibeam echo sounders have been commercialized and are widely used for seafloor imaging. In Korea related research such as the development of a towed synthetic aperture sonar system is underway. In order to obtain high-resolution synthetic aperture sonar images, it is necessary to accurately estimate the platform motion on which it is installed, and a precise underwater navigation system is required. In this paper we are going to provide reference data for determining the required navigation accuracy and precision of navigation sensors by quantitatively analyzing how much distortion of the sonar images occurs according to motion characteristics of the platform equipped with the synthetic aperture sonar. Five types of motions are considered and normalized root mean square error is defined for quantitative analysis. Simulation for error analysis with parameter variation of motion characteristics results in that yaw and sway motion causes the largest image distortion whereas the effect of pitch and heave motion is not significant.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.340-345
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• 2020

In this study, we developed the quality control and assurance method of measurement data of SPAR (Soil-Plant-Atmosphere-Research) system, a climate change research facility, for the first time. It was found that the precise processing of CO2 flux data among many observations were sig nificantly important to increase the accuracy of canopy photosynthesis measurements in the SPAR system. The collected raw CO2 flux data should first be removed error and missing data and then replaced with estimated data according to photosynthetic lig ht response curve model. Comparing the correlation between cumulative net assimilation and soybean biomass, the quality control and assurance of the raw CO2 flux data showed an improved effect on canopy photosynthesis evaluation by increasing the coefficient of determination (R2) and lowering the root mean square error (RMSE). These data processing methods are expected to be usefully applied to the development of crop growth model using SPAR system.

• Journal of Ocean Engineering and Technology
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• v.36 no.2
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• pp.101-107
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• 2022

Dean (1965) proposed the use of the root mean square error (RMSE) in the dynamic free surface boundary condition (DFSBC) and kinematic free-surface boundary condition (KFSBC) as an error evaluation criterion for wave theories. There are well known wave theories with RMSE more than 1%, such as Airy theory, Stokes theory, Dean's stream function theory, Fenton's theory, and trochodial theory for deep-water waves. However, none of them can be applied for deep-water breaking waves. The purpose of this study is to provide a closed-form solution for deep-water waves with RMSE less than 1% even for breaking waves. This study is based on a previous study (Shin, 2016), and all flow fields were simplified for deep-water waves. For a closed-form solution, all Fourier series coefficients and all related parameters are presented with Newton's polynomials, which were determined by curve fitting data (Shin, 2016). For verification, a wave in Miche's limit was calculated, and, the profiles, velocities, and the accelerations were compared with those of 5^th-order Stokes theory. The results give greater velocities and acceleration than 5^th-order Stokes theory, and the wavelength depends on the wave height. The results satisfy the Laplace equation, bottom boundary condition (BBC), and KFSBC, while Stokes theory satisfies only the Laplace equation and BBC. RMSE in DFSBC less than 7.25×10^-2% was obtained. The series order of the proposed method is three, but the series order of 5^th-order Stokes theory is five. Nevertheless, this study provides less RMSE than 5^th-order Stokes theory. As a result, the method is suitable for offshore structural design.

• Progress in Medical Physics
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• v.32 no.4
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• pp.116-121
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• 2021

Purpose: To investigate the effects of dose rate on intensity-modulated radiation therapy (IMRT) quality assurance (QA). Methods: We performed gamma tests using portal dose image prediction and log files of a multileaf collimator. Thirty treatment plans were randomly selected for the IMRT QA plan, and three verification plans for each treatment plan were generated with different dose rates (200, 400, and 600 monitor units [MU]/min). These verification plans were delivered to an electronic portal imager attached to a Varian medical linear accelerator, which recorded and compared with the planned dose. Root-mean-square (RMS) error values of the log files were also compared. Results: With an increase in dose rate, the 2%/2-mm gamma passing rate decreased from 90.9% to 85.5%, indicating that a higher dose rate was associated with lower radiation delivery accuracy. Accordingly, the average RMS error value increased from 0.0170 to 0.0381 cm as dose rate increased. In contrast, the radiation delivery time reduced from 3.83 to 1.49 minutes as the dose rate increased from 200 to 600 MU/min. Conclusions: Our results indicated that radiation delivery accuracy was lower at higher dose rates; however, the accuracy was still clinically acceptable at dose rates of up to 600 MU/min.

• Journal of Environmental Science International
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• v.32 no.8
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• pp.595-611
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• 2023

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m³ , UM: 5.76 to 7.44 ug/m³ ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.415-421
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• 2023

In this study, the piezoresistive properties of cementitious composites enhanced with carbon nanotubes for improved electrical conductivity were analyzed using a deep learning-based transformer algorithm. Experimental execution was performed in parallel for acquisition of training data. Previous studies on mixture design, specimen fabrication, chemical composition analysis, and piezoresistive performance testing are also reviewed in this paper. Notably, specimens in which fly ash substituted 50% of the binder material were fabricated and evaluated in this study, in addition to carbon nanotube-infused specimens, thereby exploring the potential enhancement of piezoresistive characteristics in conductive cementitious materials. The experimental results showed more stable piezoresistive responses in specimens with fly-ash substituted binder. The transformer model was trained using 80% of the gathered data, with the remaining 20% employed for validation. The analytical outcomes were generally consistent with empirical measurements, yielding an average absolute error and root mean square error between 0.069 to 0.074 and 0.124 to 0.132, respectively.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.139-147
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• 2012

Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.