• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.23 no.3
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• pp.1-1
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• 1987

The accurate prediction of the ship wave resistance is very important to design ships which operate satisfactorily in a wave environment. Thus, work should continue on development and validation of methods to compute ship wave patterns and wave resistance. Research efforts to improve the prediction of ship waves and wavemaking resistance are categorized in two major areas. First is the development of higher-order theories to take account of the nonlinear effect of the free surface condition and improved analytical treatment of the body boundary condition. Second is the development of direct numerical methods aimed at solving body and free-surface boundary conditions as accurately as possible. A new formulation of the slender body theory for a ship with constant speed is developed by Maruo. It is quite different from the existing slender ship theory by Vossers, Maruo and Tuck. It may be regarded as a substitute for the Neumann-Kelvin approximation. In present work, the method of asymptotic expansion of the Kelvin source is applied to obtain a new wave resistance formulation in fluid of finite depth. It takes a simple form than existing theory.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.65-72
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• 2018

Seismically induced settlement exceeding dam freeboard may lead to a dam failure. The prediction of settlement is important also because it is also reported to be strongly related to longitudinal crack width and depth, which are critical indices used for safe evaluation of dams. The empirical correlation derived from numerical simulations is most often used. In this study, two-dimensional dynamic nonlinear analyses are performed using representative CFRD and ECRD fill dams. A total of 20 recorded motions are used to account for the influence on ground motion intensity and magnitude. The calculated crest settlements are correlated to four ground motion parameters, which are peak ground acceleration (PGA), peak ground velocity (PGV), Aria Intensity ($I_A$), and magnitude. It is demonstrated that using ground motion parameters in addition to PGA can significantly increase the prediction accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Earthquakes and Structures
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• v.21 no.5
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• pp.551-562
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• 2021

In this study, the generalized intensity measure (IM) named I_Npg is analyzed. The recently proposed proxy of the spectral shape named N_pg is the base of this intensity measure, which is similar to the traditional N_p based on the spectral shape in terms of pseudo-acceleration; however, in this case the new generalized intensity measure can be defined through other types of spectral shapes such as those obtained with velocity, displacement, input energy, inelastic parameters and so on. It is shown that this IM is able to increase the efficiency in the prediction of nonlinear behavior of structures subjected to earthquake ground motions. For this work, the efficiency of two particular cases (based on acceleration and velocity) of the generalized I_Npg to predict the peak floor acceleration demands on steel frames under 30 earthquake ground motions with respect to the traditional spectral acceleration at first mode of vibration Sa(T₁) is compared. Additionally, a 3D reinforced concrete building and an irregular steel frame is used as a basis for comparison. It is concluded that the use of velocity and acceleration spectral shape increase the efficiency to predict peak floor accelerations in comparison with the traditional and most used around the world spectral acceleration at first mode of vibration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1826-1834
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• 2021

In this paper, we check the possibility of continuous tracking when photographing unknown space objects in a short period of time in an optical observation system on the ground. Simulated observation data were generated for target limited to low-orbit areas. The performance index of the prediction error was set in consideration of the property of targets. Kalman Filter was applied to predict the next location of the target. A constant velocity/acceleration dynamic model was applied to the two axes of the azimuth/elevation of the unknown space object respectively. As a result of performing the Monte Carlo simulation, the maximum error ratio of the maximum nonlinear section was less than 2%, which could be determined to ensure continuous tracking. The CA model had little change in the prediction error value for each case, making it more suitable for tracking unknown space objects. This analysis could provide a foundation for determining the orbit of unknown space objects using optical observation.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.

• Steel and Composite Structures
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• v.47 no.2
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• pp.167-184
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• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Journal of the Korean Geotechnical Society
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• v.40 no.2
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• pp.65-79
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• 2024

This study employs traditional statistical auto-regressive integrated moving average (ARIMA) and deep learning-based long short-term memory (LSTM) models to predict the deformation of earth retaining walls using inclinometer data from excavation sites. It compares the predictive capabilities of both models. The ARIMA model excels in analyzing linear patterns as time progresses, while the LSTM model is adept at handling complex nonlinear patterns and long-term dependencies in the data. This research includes preprocessing of inclinometer measurement data, performance evaluation across various data lengths and input conditions, and demonstrates that the LSTM model provides statistically significant improvements in prediction accuracy over the ARIMA model. The findings suggest that LSTM models can effectively assess the stability of retaining walls at excavation sites. Additionally, this study is expected to contribute to the development of safety monitoring systems at excavation sites and the advancement of time series prediction models.