• Computers and Concrete
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• v.25 no.6
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• pp.551-563
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• 2020

This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.3
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• pp.239-245
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• 2018

International Maritime Organization (IMO) has established standards for ship manoeuvrability and applied these standards for vessels over 100 meters in length that built since 2004 (IMO, resolution MSC.137[76]). These standards are no exception to fishing vessels. In this study we carried out a manoeuvring simulation of the new model ship (Stern trawler) of fisheries training ship of Pukyong National University based on Kijima's empirical formula. The formula takes into account of the effect of stern shape or does not take into account of the effect of stern shape. Also we checked whether the simulation results of turning motion of model ship meet IMO manoeuvrability criteria and then compared trajectories between the simulation results of model ship and the results of real sea trial test of the existing ship. In conclusion, Kijima's empirical formulas can estimate the manoeuvrability of fishing vessels at design stage approximately, it needs more parameters of fishing vessel own in case of expressing the manoeuvrability of fishing vessel accurately.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.64-70
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• 2003

This paper suggests a systematic methodology to predict chip forms using the experimental design technique and the neural network. Significant factors determined with ANOVA analysis are used as input variables of the neural network back-propagation algorithm. It has been shown that cutting conditions and cutting tool shapes have distinct effects on the chip forms, so chip breaking. Cutting tools are represented using the Z-map method, which differs from existing methods using some chip breaker parameters. After training the neural network with selected input variables, chip forms are predicted and compared with original chip forms obtained from experiments under same input conditions, showing that chip forms are same at all conditions. To verify the suggested model, one tool not used in training the model is chosen and input to the model. Under various cutting conditions, predicted chip forms agree well with those obtained from cutting experiments. The suggested method could reduce the cost and time significantly in designing cutting tools as well as replacing the“trial-and-error”design method.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.3
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• pp.23-31
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• 2018

This paper describes the effect of advice information for arriving aircraft landing order on the air traffic controller's work efficiency. The air traffic control simulator used in the experiment was modeled on the basis of the aircraft parameters from BADA, gamma-command model and the 4-dimensional trajectory using the Bezier curve. The simulation results show that advice information was helpful for the performance of the work for users who did not have the air traffic control training. On the other hand, in case of users who have experience in air traffic control training, the work efficiency was lowered when the advisory information that does not reflect the user's intention is provided. Therefore, it can be seen that the effect of improving the work efficiency through advice information can be limited depending on the skill level of the air traffic controllers and the complexity of the air traffic situation.

• Analytical Science and Technology
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• v.7 no.3
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• pp.349-359
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• 1994

A gas chromatographic method for analyzing the gas odorants concentration in natural gas was studied. Eight odorants involving TBM and THT were completely separated by using OV-10 column. The optimization of several interrelated key parameters affecting the response of FPD such as hydrogen flow rate, air flow rate and detector temperature were accomplished. A permeation device was used to obtain calibration curves of TBM and THT. This analytical method has applied to measure TBM and THT used as a natural gas odorant blend in natural gas pipeline. In order to elucidate the relationship between odor level and odorant level feasibility test of fragrance meter was demonstrated.

• Computers and Concrete
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• v.18 no.1
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• pp.1-16
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• 2016

In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of $R^2$, RMS and MAPE. For the testing of dataset, the $R^2$, RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the $R^2$, RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.

• Smart Structures and Systems
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• v.21 no.1
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• pp.1-14
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• 2018

A method is proposed to detect structural damages in the presence of damping using noisy data. This method uses Frequency Response Function (FRF) and Mode-Shapes as the input parameters for a system of Couple Sparse Coding (CSC) to study the healthy state of the structure. To obtain appropriate patterns of FRF for CSC training, Principal Component Analysis (PCA) technique is adopted to reduce the full-size FRF to overcome over-fitting and convergence problems in machine-learning training. To verify the proposed method, a numerical two-story frame structure is employed. A system of individual CSCs is trained with FRFs and mode-shapes, and then termed ensemble to detect the health condition of the structure. The results demonstrate that the proposed method is accurate in damage identification even in presence of up to 20% noisy data and 5% unconsidered damping ratio. Furthermore, it can be concluded that CSC ensemble is highly efficient to detect the location and the severity of damages in comparison to the individual CSC trained only with FRF data.