• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1282-1291
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• 1993

The color image of the rose was segmented by the cluster analysis on the color space into the characteristic sub-regions, the degree of bloom of the flower was represented numerically base on the segmented image and judged by the artificial neural network system whose input variable were the characteristic regions. Judgement by neural system were compared with that of the farmers and it was found that degree of agreement were fairly good.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.482-487
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• 1993

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO $_{2}$ Arc Welding using the finite element method. The validity of the above results is demonstrated by experimental elastic stress relief method which is called Holl Drilling Method. The first part of numarical analysis performs a three-dimensional transient heat transfer anslysis, and the second part then uses results of the first part and performs a three-dimensional transient thermo-clasto-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method were used to train a backpropagation neural network to predict residual stress. Architecturally, the finite element method were used to train a backpropagation voltage and the current, a hidden layer to accommodate failure mechanism mapping, and an output layer for residual stress. The trained network was then applied to the prediction of residual stress in the four specimens. The results of predicted residual stress have been very encouraging.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.1
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• pp.122-129
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• 1997

Thermo-elastic-plastic analyses used in solving plate forming process are often computationally expensive. To obtain an optimal process of line heating typically requires numerous iterations between the simulation and a finite element analysis. This process often becomes prohibitive due to the amount of computer time required for numerical simulation of line heating process. Therefore, a new techniques that could significantly reduce the computer time required to solve a complex analysis problem would be beneficial. In this paper, we considered factors that influence the bending effect by line heating and developed inference engine by using the concept of artificial neural network. To verify the validity of the neural network, we used results obtained from numerical analysis. We trained the neural network with the data made from numerical analysis and experiments varying the structure of neural network, in other words varying the number of hidden layers and the number of neurons in each hidden layers. From that we concluded that if the number of neurons in each hidden layers is large enough neural network having two hidden layers can be trained easily and errors between exact value and results obtained from trained network are not so large. Consequently, if there are enough number of training pairs, artificial neural network can infer similar results. Based on the numerical results, we applied the artificial neural network technique to deal with mechanical behavior of line heating at simulation stage effectively.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.145-158
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• 2013

I estimate stock prices of listed companies using financial information and Ohlson model, which is used for the evaluation of company value. Furthermore, I use the artificial neural network, one of artificial intelligence systems, which are not based on linear relationship between variables, to estimate stock prices of listed companies. By reapplying this in estimating stock prices of newly listed companies, I evaluate the appropriateness in stock valuation with such methods. The result of practical analysis of this study is as follows. On the top of that, the multiplier for the actual stock price is accounted by generating the estimated stock prices based on the artificial neural network model. As a result of the comparison of two multipliers, the estimated stock prices by the artificial neural network model does not show statistically difference with the actual stock prices. Given that, the estimated stock price with artificial neural network is close to the actual stock prices rather than the estimated stock prices with Ohlson model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.552-560
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• 2010

This paper presents an application of layered Artificial Neural Network controller to study load frequency control problem in power system. The objective of control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed controller has been designed for a two-area interconnected power system. Only one artificial neural network controller (ANN), which controls the inputs of each area in the power system together, is considered. In this study, back propagation-through time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and ANN controller, separately. For the first time comparative study has been carried out between SMES and CES unit, all of the areas are included with SMES and CES unit separately. By comparing the results for both cases, the performance of ANN controller with CES unit is found to be better than conventional controllers with SMES, CES and ANN with SMES.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.333-340
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• 2000

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and apply the newly developed techniques for assessment of landslide susceptibility to study areas, Yongin. Landslide locations detected from interpretation of aerial photo and field survey, and topographic, soil and geological maps of the Yongin area were collected. The data of the locations of land-slide, slope, soil texture, topography and lithology were constructed into spatial database using GIS. Using the factors, landslide susceptibility was analyzed by artificial neural network methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and landslide location data.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.529-543
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• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.133-138
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• 2024

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.269-278
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• 2022

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.