• Computers and Concrete
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• v.16 no.5
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• pp.669-682
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• 2015

Degradation of reinforced concrete (RC) structures due to chloride penetration followed by reinforcement corrosion has been a serious problem in civil engineering for many years. The numerical simulation methods at present are mainly finite element method (FEM) and finite difference method (FDM), which are based on mesh. Mesh generation in engineering takes a long time. In the present article, the numerical solution of chloride transport in concrete is analyzed using radial point interpolation method (RPIM) and element-free Galerkin (EFG). They are all meshless methods. RPIM utilizes radial polynomial basis, whereas EFG uses the moving least-square approximation. A Galerkin weak form on global is used to attain the discrete equation, and four different numerical examples are presented. MQ function and appropriate parameters have been proposed in RPIM. Numerical simulation results are compared with those obtained from the finite element method (FEM) and analytical solutions. Two case of chloride transport in full saturated and unsaturated concrete are analyzed to test the practical applicability and performance of the RPIM and EFG. A good agreement is obtained among RPIM, EFG, and the experimental data. It indicates that RPIM and EFG are reliable meshless methods for prediction of chloride concentration in concrete structures.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.1
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• pp.1-8
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• 2005

An axisymmetric compressible flow solver accounting for chemical equilibrium has been developed as an analysis tool exclusively suitable for performance prediction and optimum contour design of LRE thrust chamber. By virtue of several features focusing on user-friendliness and effectiveness including automatical grid generation and iterative calculations with changes in design parameters prescribed through only one keyword-type input file, a design engineer can evaluate very fast and easily the influences of various design inputs such as geometrical parameters and operating conditions on propulsive performance. Validations have been carried out for various aspects by detailed comparisons with the result of CEA code, experimental data of JPL nozzle, actual data for two historical engines, and ReTF data for KSR-III.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.119-127
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• 2009

Even though there are next generation Global Navigation Systems in development, only GPS and GLONASS are currently available for satellite positioning. In this study, GLONASS orbits were predicted using Keplerian elements in almanac and the orbit equation. For accuracy validation, predicted orbits were compared with precise ephemeris. As a result, the 3-D maximum and RMS (Root Mean Square) errors were 155.4 km and 56.3 km for 7-day predictions. Also, the GLONASS satellite visibility predictions were compared with real observations, and they agree perfectly except for several epochs when the satellite signal was blocked nearby buildings.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2779-2789
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• 1999

In this paper, we propose a new neural Buffered Leaky Bucket algorithm for preventing the degradation of network performance caused by congestion and dealing with the traffic congestion in ATM networks. We networks. We justify the validity of the suggested method through performance comparison in aspects of cell loss rate and mean transfer delay under a variety of traffic conditions requiring the different QoS(Quality of Service). also, the cell scheduling algorithms such as DWRR and DWEDF used for multiplexing the incoming traffics are induced to get the delay time of the traffics fairly. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate is changed by the predicted values. The prediction of traffic loss rate by neural networks can effectively reduce the cell loss rate and the cell transfer delay of next incoming cells and be applied to other traffic control systems. Computer simulation results performed for traffic prediction show that QoSs of the various kinds of traffics are increased.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.173-175
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• 2004

The degree of contamination on outdoor insulators is one of the most importance factor to determine the pollution level of outdoor insulation. Outdoor insulators in coastal are affected due to salty wind blowing from the seaside. The sea salt is known as the most dangerous pollutant. As known through the preceding study, the generation of salt pollutant and the pollution degree of outdoor insulators have a close relation in accordance with meteorological conditions, such as temperature, humidity, dewpoint, wind velocity and wind direction. Therefore, at first, we have analyzed relation between meteorological conditions and contaminants for development of prediction method. In this paper, we have investigated a statistical estimation technique based on actual data for equivalent salt deposit density(ESDD) of outdoor insulators which were installed in Kochang field test substation with multiple linear regression analysis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.60-67
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• 2014

Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method.

• Steel and Composite Structures
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• v.44 no.1
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.