• Journal of Ocean Engineering and Technology
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• 제34권3호
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• pp.167-179
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• 2020

Recently, the increasing importance of artificial intelligence (AI) technology has led to its increased use in various fields in the shipbuilding and marine industries. For example, typical scenarios for AI include production management, analyses of ships on a voyage, and motion prediction. Therefore, this study was conducted to predict a response amplitude operator (RAO) through AI technology. It used a neural network based on one of the types of AI methods. The data used in the neural network consisted of the properties of the vessel and RAO values, based on simulating the in-house code. The learning model consisted of an input layer, hidden layer, and output layer. The input layer comprised eight neurons, the hidden layer comprised the variables, and the output layer comprised 20 neurons. The RAO predicted with the neural network and an RAO created with the in-house code were compared. The accuracy was assessed and reviewed based on the root mean square error (RMSE), standard deviation (SD), random number change, correlation coefficient, and scatter plot. Finally, the optimal model was selected, and the conclusion was drawn. The ultimate goals of this study were to reduce the difficulty in the modeling work required to obtain the RAO, to reduce the difficulty in using commercial tools, and to enable an assessment of the stability of medium/small vessels in waves.

• Proceedings of the KSME Conference
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• 대한기계학회 2004년도 춘계학술대회
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• pp.365-370
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• 2004

Generally, component and FR-4 board are connected by solder joint. Because material properties of components and FR-4 board are different, component and FR-4 board show different coefficients of thermal expansion (CTE) and thus strains in component and board are different when they are heated. That is, the differences in CTE of component and FR-4 board cause the dissimilarity in shear strain and solder joint' failure. The first order Taylor series expansion of the limit state function incorporating with Tresca failure criterion is used in order to estimate the failure probability of solder joints under heated condition. Using shear stresses and shear strains appeared on the solder joint, we estimate the failure probability of solder joints with the Tresca failure criterion. The effects of random variables such as CTE, distance of the solder joint from the neutral point(DNP), temperature variation and height of solder on the failure probability of the solder joint are systematically studied by using the failure probability model with first order reliability method(FORM).

• Wind and Structures
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• 제34권4호
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• pp.321-334
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• 2022

Irregularity in plan shape is very common for any type of building as it enhances better air ventilation for the inhabitants. Systematic opening at the middle of the facades makes the appearance of the building plan as a butterfly one. The primary focus of this study is to forecast the force, moment and torsional coefficient of a butterfly plan shaped tall building. Initially, Computational Fluid Dynamics (CFD) study is done on the building model based on Reynolds averaged Navier Stokes (RANS) k-epsilon turbulence model. Fifty random cases of irregularity and angle of attack (AOA) are selected, and the results from these cases are utilised for developing the surrogate models. Parametric equations are predicted for all these aerodynamic coefficients, and the training of these outcomes are also done for developing Artificial Neural Networks (ANN). After achieving the target acceptance criteria, the observed results are compared with the primary CFD data. Both parametric equations and ANN matched very well with the obtained data. The results are further utilised for discussing the effects of irregularity on the most critical wind condition.

• Journal of Radiation Industry
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• 제17권4호
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Journal of Soil and Groundwater Environment
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• 제11권5호
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• pp.1-8
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• 2006

The objective of this study is to assess impacts of sorption heterogeneity on the transport of leachate leaked from unlined landfill sites and is accomplished by examining the results from a series of Monte-Carlo simulations. For random distribution coefficient ($K_{d}$) fields with four different levels of heterogeneity ranging from homogeneous to highly heterogeneous, the transport of leachate was investigated by linking a saturated flow model with a contaminant transport model. Impacts of a chemical heterogeneity were evaluated using point statistics values such as mean, standard deviation, and coefficient of variation of the concentration obtained at monitoring wells from 100 Monte-Carlo trials. Inspection of point statistics shows that the distribution of distribution coefficient in the landfill site proves to be an important parameter in controlling leachate concentrations. In comparison to homogeneous sorption, heterogeneous $K_{d^-}$ fields produce the variability in the leachate concentration for different realizations. The variability increases significantly as the variance in the $K_{d^-}$ field and the travel time between source and monitoring well increase. These outcomes indicate that use of a constant homogeneous $K_{d}$ value for predicting the transport of leachate can result in significant error, especially when variability in $K_{d}$ is high.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제18권10호
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• pp.136-152
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• 2017

The thermodynamic models, PC-SAFT (Perturbed-Chain Statistical Associated Fluid Theory) state equation and the Two-model approach liquid activity coefficient model NRTL (Non Random Two Liquid) + Henry + Peng-Robinson, for modeling the Rectisol process using methanol aqueous solution as the $CO_2$ removal solvent were compared. In addition, to determine the new binary interaction parameters of the PC-SAFT state equations and the Henry's constant of the two-model approach, absorption equilibrium experiments between carbon dioxide and methanol at 273.25K and 262.35K were carried out and regression analysis was performed. The accuracy of the newly determined parameters was verified through the regression results of the experimental data. These model equations and validated parameters were used to model the carbon dioxide removal process. In the case of using the two-model approach, the methanol solvent flow rate required to remove 99.00% of $CO_2$ was estimated to be approximately 43.72% higher, the cooling water consumption in the distillation tower was 39.22% higher, and the steam consumption was 43.09% higher than that using PC-SAFT EOS. In conclusion, the Rectisol process operating under high pressure was designed to be larger than that using the PC-SAFT state equation when modeled using the liquid activity coefficient model equation with Henry's relation. For this reason, if the quantity of low-solubility gas components dissolved in a liquid at a constant temperature is proportional to the partial pressure of the gas phase, the carbon dioxide with high solubility in methanol does not predict the absorption characteristics between methanol and carbon dioxide.

• Korean Journal of Remote Sensing
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• 제37권3호
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제17권4호
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• pp.236-244
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• 2024

A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.

• Journal of the Computational Structural Engineering Institute of Korea
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• 제26권3호
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• pp.191-198
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• 2013

In this paper, the effects of uncertain material constant on the nonlinear behavior of steel frames with semi-rigid joints are examined. As to the probabilistic model, a normal distribution is assumed to simulate the uncertain elastic modulus of steel material. A nonlinear structural analysis program, which can consider both semi-rigidity in joints of the steel frames and uncertainty in the material constant, is developed. Including the geometric, material and connection nonlinearites which are the parameters of nonlinear behavior of steel frames, probabilistic analysis is conducted based on the Monte-Carlo simulation. In the probabilistic analyses, we consider the three different cases for random variables. The deterministic analysis results are shown to be in good agreement with those of the previous research results in the literature. As to the probabilistic analyses, it is observed that the coefficient of variation(COV) of displacements increases as the loading increases, and that the values of COV are dependent on the structural features of the frames.

• Bulletin of the Korean Chemical Society
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• 제23권3호
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• pp.447-453
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• 2002

In this paper we have presented the results of thermodynamic, structural, and dynamic properties of model systems for liquid benzene, toluene and p-xylene in an isobaric-isothermal (NpT) ensemble at 283.15, 303.15, 323.15, and 343.15 K using molecular dynamics (MD) simulation. This work is initiated to compensate for our previous canonical (NVT) ensemble MD simulations [Bull. Kor. Chem. Soc. 2001, 23, 441] for the same systems in which the calculated pressures were too low. The calculated pressures in the NpT ensemble MD simulations are close to 1 atm and the volume of each system increases with increasing temperature. The first and second peaks in the center of mass g(r) diminish gradually and the minima increase as usual for the three liquids as the temperature increases. The three peaks of the site-site gC-C(r) at 283.15 K support the perpendicular structure of nearest neighbors in liquid benzene. Two self-diffusion coefficients of liquid benzene via the Einstein equation and via the Green-Kubo relation are in excellent agreement with the experimental measures. The self-diffusion coefficients of liquid toluene and p-xylene are in accord with the trend that the self-diffusion coefficient decreases with increasing number of methyl group. The friction constants calculated from the force auto-correlation (FAC) function with the assumption that the fast random force correlation ends at time which the FAC has the first negative value give a correct qualitative trends: decrease with increase of temperature and increase with the number of methyl group. The friction constants calculated from the FAC's are always less than those obtained from the friction-diffusion relation which reflects that the random FAC decays slower than the total FAC as described by Kubo [Rep. Prog. Phys. 1966, 29, 255].