• Journal of the military operations research society of Korea
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• v.34 no.1
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• pp.1-11
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• 2008

A methodology for the improvement of simulation based training system for the artillery is proposed in this paper. The complex nonlinear relationship inherent among parameters in artillery firing is difficult to model and analyze. By introducing neural network based simulation, accurate representation of artillery firing is made possible. The artillery training system can greatly benefit from the improved prediction. Neural networks learning is conducted using the conjugate gradient algorithm. The evaluation of the proposed methodology is performed through simulation. Prediction errors of both regression analysis model and neural networks model are analyzed. Implementation of neural networks to training system enables more realistic training, improved combat power and reduced budget.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.10-12
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• 2003

This study was to monitor changes of leaf area index (LAI) and normalized difference vegetation index (NDVI), calculated from ground-based remotely sensed high resolution reflectance spectra, during rice (Oryza sativa L. cv. TNG 67) growth so as to determine their relationships and the optimum time period to use these parameters for yield prediction. Field experiments were conducted at the experimental farm of TARI to obtain various scales of grain yield and values of LAI and NDVI in the first and the second cropping seasons of 2001-2002. It was found that LAI and NDVI can be mutually estimated through an exponential relationship, and hence plant growth information and spectral remote sensing data become complementary counterparts through this linkage. Correlation between yield and LAI was best fitted to a nonlinear function since about 7 weeks after transplanting (WAT). The accumulated and the mean values of LAI from 15 days before heading (DBH) to 15 days after heading (DAH) were the optimum time period to predict rice yield for First Crops, while values calculated from 15 DBH to 10 DAH were the optimal timing for Second Crops.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.91-97
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• 2016

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.23 no.3
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• pp.111-116
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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.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.62-68
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• 2021

Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R²=0.998) and a considerable prediction performance (R²=0.812) for the conditions that were not included in the training data.

• International journal of steel structures
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• v.18 no.4
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• pp.1107-1124
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• 2018

The structural behavior of reinforced concrete coupled shear wall structures is greatly influenced by the behavior of their coupling beams. This paper presents a process of the seismic analysis of reinforced concrete coupled shear wall-frame system linked by hysteretic dampers at each floor. The hysteretic dampers are located at the middle portion of the linked beams which most of the inelastic damage would be concentrated. This study concerned particularly with wall-frame structures that do not twist. The proposed method, which is based on the energy equilibrium method, offers an important design method by the result of increasing energy dissipation capacity and reducing damage to the wall's base. The optimum distribution of yield shear force coefficients is to evenly distribute the damage at dampers over the structural height based on the cumulative plastic deformation ratio of the dissipation device. Nonlinear dynamic analysis indicates that, with a proper set of damping parameters, the wall's dynamic responses can be well controlled. Finally, based on the total plastic strain energy and its trend through the height of the buildings, a prediction equation is suggested.

• Computers and Concrete
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• v.24 no.4
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• pp.329-345
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• 2019

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

• Journal of Ocean Engineering and Technology
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• v.36 no.4
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• pp.243-250
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• 2022

Subsea power cables are subjected to various external loads induced by environmental and mechanical factors during manufacturing, shipping, and installation. Therefore, the prediction of the structural strength is essential. In this study, experimental and theoretical analyses were performed to investigate the axial stiffness of subsea power cables. A uniaxial tensile test of a 6.5 m three-core AC inter-array subsea power cable was carried out using a 10 MN hydraulic actuator. In addition, the resultant force was measured as a function of displacement. The theoretical model proposed by Witz and Tan (1992) was used to numerically predict the axial stiffness of the specimen. The Newton-Raphson method was employed to solve the governing equation in the theoretical analysis. A comparison of the experimental and theoretical results for axial stiffness revealed satisfactory agreement. In addition, the predicted axial stiffness was linear notwithstanding the nonlinear geometry of the subsea power cable or the nonlinearity of the governing equation. The feasibility of both experimental and theoretical framework for predicting the axial stiffness of subsea power cables was validated. Nevertheless, the need for further numerical study using the finite element method to validate the framework is acknowledged.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.