• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.4
• /
• pp.285-294
• /
• 2018

In this study, we implemented the data quality monitoring algorithm which is the previous step for real-time Global Navigation Satellite System (GNSS) correction generation and compared Global Positioning System (GPS) and BeiDou System (BDS). Signal Quality Monitoring (SQM), Data QM, and Measurement QM (MQM) that are well known in Ground Based Augmentation System (GBAS) were used for quality monitoring. SQM and Carrier Acceleration Ramp Step Test (CARST) of MQM result were divided by satellite elevation angle and analyzed. The data which are judged as abnormal are removed and presented as Root Mean Square (RMS), standard deviation, average, maximum, and minimum value.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.4
• /
• pp.343-348
• /
• 2023

The ionosphere acts as the largest error source in the Global Navigation Satellite System (GNSS) signal transmission. Ionospheric total electron content (TEC) is also easily affected by changes in the space environment, such as solar activity and geomagnetic storms. In this study, we analyze changes in the regional ionosphere using the Qusai-Zenith Satellite System (QZSS), a regional satellite navigation system. Observations from 9 GNSS stations in South Korea are used for estimating the QZSS TEC. In addition, the performance of QZSS TEC is analyzed with observations from day of year (DOY) 199 to 206, 2023. To verify the performance of our results, we compare the estimated QZSS TEC and CODE Global Ionosphere Map (GIM) at the same location. Our results are in good agreement with the GIM product provided by the CODE over this period, with an averaged difference of approximately 0.1 TECU and a root mean square (RMS) value of 2.89 TECU.

• Smart Media Journal
• /
• v.7 no.1
• /
• pp.16-23
• /
• 2018

In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.3
• /
• pp.632-640
• /
• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Journal of Ocean Engineering and Technology
• /
• v.24 no.6
• /
• pp.7-15
• /
• 2010

Analyses of wind wave characteristics near the Korean marginal seas were performed in 2008 and 2009 by comparisons of an operational wind wave forecast model and ocean buoy data. In order to evaluate the model performance, its results were compared with the observed data from an ocean buoy. The model used in this study was very good at predicting the characteristics of wind waves near the Korean Peninsula, with correlation coefficients between the model and observations of over 0.8. The averaged Root Mean Square Error (RMSE) for 48 hrs of forecasting between the modeled and observed waves and storm surges/tide were 0.540 m and 0.609 m in 2008 and 2009, respectively. In the spatial and seasonal analysis of wind waves, long waves were found in July and September at the southern coast of Korea in 2008, while in 2009 long waves were found in the winter season at the eastern coast of Korea. Simulated significant wave heights showed evident variations caused by Typhoons in the summer season. When Typhoons Kalmaegi and Morakot in 2008 and 2009 approached to Korean Peninsula, the accuracy of the model predictions was good compared to the annual mean value.

• Wind and Structures
• /
• v.31 no.5
• /
• pp.445-457
• /
• 2020

The buffeting response is a vital consideration for long-span bridges in typhoon-prone areas. In the conventional analysis, the turbulence and structural vibrations are assumed as stationary processes, which are, however, inconsistent with the non-stationary features observed in typhoon winds. This poses a question on how the stationary assumption would affect the evaluation of buffeting responses under non-stationary wind actions in nature. To figure out this problem, this paper presents a comparative study on buffeting responses of a long-span cable-stayed bridge based on stationary and non-stationary perspectives. The stationary and non-stationary buffeting analysis frameworks are firstly reviewed. Then, a modal analysis of the example bridge, Sutong Cable-stayed Bridge (SCB), is conducted, and stationary and non-stationary spectral models are derived based on measured typhoon winds. On this condition, the buffeting responses of SCB are finally analyzed by following stationary and non-stationary approaches. Although the stationary results are almost identical with the non-stationary results in the mean sense, the root-mean-square value of buffeting responses are underestimated by the stationary assumption as the time-varying features existing in the spectra of turbulence are neglected. The analytical results highlights a transition from stationarity to non-stationarity in the buffeting analysis of long-span bridges.

• Geomechanics and Engineering
• /
• v.33 no.2
• /
• pp.183-194
• /
• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• Journal of Forest and Environmental Science
• /
• v.39 no.4
• /
• pp.195-202
• /
• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.36 no.1
• /
• pp.29-40
• /
• 2020

Purpose: The purpose of this study was to compare the accuracy of impression taking method using the encoded healing abutment, scan body and pick-up impression coping with different implant angulations. Materials and Methods: Master model was fabricated by 3D printer and three implants were placed into the model with 0°, 10° and 20° mesial angulation. The abutments were secured to each implants and master model was scanned to make a reference model. Group P model was fabricated using pick-up impression copings and model was scanned after securing the abutments. Encoded healing abutment (Group E) and scan body (Group S) were secured on the master model and digital impression was taken using intraoral scanner 15 times each. Each STL files of test groups were superimposed with reference model using best fit alignment and root mean square (RMS) value was analyzed. Results: The RMS values were lowest in Group P, followed by Group S and Group E. Group P showed significant difference with Group S and E (P < 0.05) while there was no significant difference between Group S and E. Correlation between implant angulation and RMS value was significant in Group E (P < 0.05). Conclusion: The pick-up impression coping method showed higher accuracy and there was no significant difference in accuracy between the healing abutment and the scan body. The clinical use of the encoded healing abutment is possible, but it should be used with caution in the case of angulated implant.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.5
• /
• pp.27-39
• /
• 2004

In the first part of this study, five homogeneous regions in view of topographical and geographically homogeneous aspects except Jeju and Ulreung islands in Korea were accomplished by K-means clustering method. A total of 57 rain gauges were used for the regional frequency analysis with minimum rainfall series for the consecutive durations. Generalized Extreme Value distribution was confirmed as an optimal one among applied distributions. Drought rainfalls following the return periods were estimated by at-site and regional frequency analysis using L-moments method. It was confirmed that the design drought rainfalls estimated by the regional frequency analysis were shown to be more appropriate than those by the at-site frequency analysis. In the second part of this study, LH-moment ratio diagram and the Kolmogorov-Smirnov test on the Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions were accomplished to get optimal probability distribution. Design drought rainfalls were estimated by both at-site and regional frequency analysis using LH-moments and GEV distribution, which was confirmed as an optimal one among applied distributions. Design rainfalls were estimated by at-site and regional frequency analysis using LH-moments, the observed and simulated data resulted from Monte Carlotechniques. Design drought rainfalls derived by regional frequency analysis using L1, L2, L3 and L4-moments (LH-moments) method have shown higher reliability than those of at-site frequency analysis in view of RRMSE (Relative Root-Mean-Square Error), RBIAS (Relative Bias) and RR (Relative Reduction) for the estimated design drought rainfalls. Relative efficiency were calculated for the judgment of relative merits and demerits for the design drought rainfalls derived by regional frequency analysis using L-moments and L1, L2, L3 and L4-moments applied in the first report and second report of this study, respectively. Consequently, design drought rainfalls derived by regional frequency analysis using L-moments were shown as more reliable than those using LH-moments. Finally, design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were derived by regional frequency analysis using L-moments, which was confirmed as a more reliable method through this study. Maps for the design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were accomplished by the method of inverse distance weight and Arc-View, which is one of GIS techniques.